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RECENT DEVELOPMENTS OF ARTIFICIAL INTELLIGENCE FOR BANANA: APPLICATION AREAS, LEARNING ALGORITHMS, AND FUTURE CHALLENGES

ABSTRACT

Bananas are the world’s most traded fruits. Several analytical models using artificial intelligence (AI) have been developed to resolve challenges facing the banana supply chain. The number of publications in this field has steadily increased each year. However, a literature review regarding the trends of recent AI developments is not available. Thus, this study reviews the current scenario of scientific research involving AI in the stages of the banana supply chain (pre-harvest, harvest, post-harvest, processing and retail). This review covers literature published between 2015 and 2020 from online databases. Fifty-two relevant studies were retrieved from 23 countries. Consequently, we propose an AI-performance framework based on real applications implemented for bananas: the application domain, learning algorithms, performance metrics, and reported impacts. This paper discovers 11 AI-application areas for bananas, such as ripeness, leaf diseases, quality grading, crop type, crop yield, and soil control. Moreover, this review summarizes the main functionality of learning algorithms found in the literature (ANN, CNN, SVM, and K-NN). Finally, the future challenges are discussed. This comprehensive review will help researchers understand AI applications in the banana sector and analyze the knowledge gap for future studies.

KEYWORDS
review; banana; predictive model; machine learning; deep learning

INTRODUCTION

Technological advances in artificial intelligence (AI) in the field of agriculture have intensified in recent years (Benos et al., 2021Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: A comprehensive updated review. Sensors 21(11):1–55. DOI: https://doi.org/10.3390/s21113758
https://doi.org/10.3390/s21113758...
; Jha et al., 2019Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture 2:1–12. DOI: https://doi.org/10.1016/j.aiia.2019.05.004
https://doi.org/10.1016/j.aiia.2019.05.0...
; Kamilaris & Prenafeta-Boldú, 2018Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147:70–90. DOI: https://doi.org/10.1016/j.compag.2018.02.016
https://doi.org/10.1016/j.compag.2018.02...
; Liakos et al., 2018Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: A review. Sensors 18(8):2674. DOI: https://doi.org/10.3390/s18082674
https://doi.org/10.3390/s18082674...
; Meshram et al., 2021Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences 1. DOI: https://doi.org/10.1016/j.ailsci.2021.100010:100010
https://doi.org/10.1016/j.ailsci.2021.10...
; Sharma et al., 2020Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A (2020) A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers and Operations Research 119. DOI: https://doi.org/10.1016/j.cor.2020.104926:104926
https://doi.org/10.1016/j.cor.2020.10492...
). Predictive models and applications using AI have been developed to increase the competitiveness and efficiency of agricultural production processes (Elavarasan et al., 2018Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture 155:257–282. DOI: https://doi.org/10.1016/j.compag.2018.10.024
https://doi.org/10.1016/j.compag.2018.10...
; Pathan et al., 2020Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: A comprehensive review. Artificial Intelligence in Agriculture 4:81–95. DOI: https://doi.org/10.1016/j.aiia.2020.06.001
https://doi.org/10.1016/j.aiia.2020.06.0...
). Thus, the agricultural supply chain has been considered a subject of AI research (Meshram et al., 2021Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences 1. DOI: https://doi.org/10.1016/j.ailsci.2021.100010:100010
https://doi.org/10.1016/j.ailsci.2021.10...
; Sharma et al., 2020Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A (2020) A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers and Operations Research 119. DOI: https://doi.org/10.1016/j.cor.2020.104926:104926
https://doi.org/10.1016/j.cor.2020.10492...
), and the findings have been published to increase current understanding in agricultural management (Handayati et al., 2015Handayati Y, Simatupang TM, Perdana T (2015) Agri-food supply chain coordination: The state-of-the-art and recent developments. Logistics Research 8(1):1–15. DOI: https://doi.org/10.1007/s12159-015-0125-4
https://doi.org/10.1007/s12159-015-0125-...
; Pathan et al., 2020Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: A comprehensive review. Artificial Intelligence in Agriculture 4:81–95. DOI: https://doi.org/10.1016/j.aiia.2020.06.001
https://doi.org/10.1016/j.aiia.2020.06.0...
; Pereira et al., 2018Pereira TdS, Robaina AD, Peiter MX, Torres RR, Bruning J (2018) The use of artificial intelligence for estimating soil resistance to penetration. Engenharia Agricola 38(1):142–148. DOI: https://doi.org/10.1590/1809-4430-Eng.Agric.v38n1p142-148/2018
https://doi.org/10.1590/1809-4430-Eng.Ag...
; Zhao et al., 2019Zhao H, Chen Z, Jiang H, Jing W, Sun L, Feng M (2019) Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time - A Case Study in Zhanjiang, China. Remote Sensing 11(22):2673. DOI: https://doi.org/10.3390/rs11222673
https://doi.org/10.3390/rs11222673...
). According to Meshram et al. (2021)Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences 1. DOI: https://doi.org/10.1016/j.ailsci.2021.100010:100010
https://doi.org/10.1016/j.ailsci.2021.10...
, agricultural tasks are categorized into three major areas: pre-harvest, harvest, and post-harvest.

Siddiq et al. (2020)Siddiq M, Ahmed J, Lobo MG (2020) Handbook of banana production, postharvest science, processing technology, and nutrition. Hoboken, Wiley, 284 p. DOI: https://doi.org/10.1002/9781119528265
https://doi.org/10.1002/9781119528265...
and FAO (2020)FAO - Food and Agriculture Organization for the United Nations (2020) Preliminary assessment of the impacts of the COVID-19 pandemic on trade in bananas and tropical fruits. Rome, FAO. Available: http://www.fao.org/3/cb1993en/cb1993en_commodity_focus.pdf. In: Food Outlook – Biannual Report on Global Food Markets.
http://www.fao.org/3/cb1993en/cb1993en_c...
announce that the banana supply chain (BSC) faces several challenges and opportunities. Some studies have examined challenges, such as post-harvest losses (Priyadarshi et al., 2021Priyadarshi R, Routroy S, Garg GK (2021) Postharvest supply chain losses: A state-of-the-art literature review and bibliometric analysis. Journal of Advances in Management Research 18(3):443–467. DOI: https://doi.org/10.1108/JAMR-03-2020-0040
https://doi.org/10.1108/JAMR-03-2020-004...
), management (Hiranphaet, 2018Hiranphaet A (2018) The supply chain management of the cultivated banana in Nakhon Pathom. In: International Conference on ICT and Knowledge Engineering (ICT&KE). Proceedings… DOI: https://doi.org/10.1109/ICTKE.2018.8612314
https://doi.org/10.1109/ICTKE.2018.86123...
), and environmental impacts (Rattanapan & Ounsaneha, 2020Rattanapan C, Ounsaneha W (2020) Environmental impact assessment of Thai banana supply chain. International Journal of Environmental Science and Development 11(7):341–346. DOI: https://doi.org/10.18178/IJESD.2020.11.7.1273
https://doi.org/10.18178/IJESD.2020.11.7...
) in developing a sustainable supply chain for bananas. So, Tinzaara et al. (2018)Tinzaara W, Stoian D, Ocimati W, Kikulwe E, Otieno G, Blomme G (2018) Challenges and opportunities for smallholders in banana value chains. In: Kema G, Drenth A, Achieving sustainable cultivation of bananas. Cambridge, Burleigh Dodds Science Publishing. p.1–27. DOI: https://doi.org/10.19103/AS.2017.0020.10
https://doi.org/10.19103/AS.2017.0020.10...
proposed the principal challenges in the BSC, as shown in Figure 1. Thus, the most common challenges in the pre-harvest stage are soil-nutrient control, irrigation or water management, and pest and disease control. In terms of harvest, maturity, ripening process, and handling are significant issues. In the next stage, the key challenges are post-harvest losses, shelf life, and fruit grading. Finally, in the processing stage, the major research areas are product-quality control and the value addition of bananas (products or by-products).

FIGURE 1.
Challenges in each stage of the banana supply chain (adopted from Meshram et al., 2021Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences 1. DOI: https://doi.org/10.1016/j.ailsci.2021.100010:100010
https://doi.org/10.1016/j.ailsci.2021.10...
; Tinzaara et al., 2018Tinzaara W, Stoian D, Ocimati W, Kikulwe E, Otieno G, Blomme G (2018) Challenges and opportunities for smallholders in banana value chains. In: Kema G, Drenth A, Achieving sustainable cultivation of bananas. Cambridge, Burleigh Dodds Science Publishing. p.1–27. DOI: https://doi.org/10.19103/AS.2017.0020.10
https://doi.org/10.19103/AS.2017.0020.10...
).

The BSC has emerged as a recent research area of machine learning (ML) and deep learning (DL) (Benos et al., 2021Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: A comprehensive updated review. Sensors 21(11):1–55. DOI: https://doi.org/10.3390/s21113758
https://doi.org/10.3390/s21113758...
; Kamilaris & Prenafeta-Boldú, 2018Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147:70–90. DOI: https://doi.org/10.1016/j.compag.2018.02.016
https://doi.org/10.1016/j.compag.2018.02...
; Meshram et al., 2021Meshram V, Patil K, Meshram V, Hanchate D, Ramkteke SD (2021) Machine learning in agriculture domain: A state-of-art survey. Artificial Intelligence in the Life Sciences 1. DOI: https://doi.org/10.1016/j.ailsci.2021.100010:100010
https://doi.org/10.1016/j.ailsci.2021.10...
; Pathan et al., 2020Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: A comprehensive review. Artificial Intelligence in Agriculture 4:81–95. DOI: https://doi.org/10.1016/j.aiia.2020.06.001
https://doi.org/10.1016/j.aiia.2020.06.0...
; Rehman et al., 2019Rehman TU, Mahmud MS, Chang YK, Jin J, Shin J (2019) Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture 156:585–605. DOI: https://doi.org/10.1016/j.compag.2018.12.006
https://doi.org/10.1016/j.compag.2018.12...
). Thus, studies on solving banana issues and improving the agricultural processes of the BSC using AI technology have increased in recent years. Given this scenario, the volume of scientific articles from different countries, regarding this research area, has increased. However, no survey study has presented the main trends in predictive AI-based models that have been implemented. Consequently, a review work must be considered.

This paper presents a comprehensive review of AI applications for bananas to explain the current scientific scenario. This review was conducted to examine the findings and trends regarding AI-application areas, the implementation of machine learning (ML) and deep learning (DL) algorithms, performance metrics, and future challenges regarding AI for bananas. The systematic review consists of collecting ML and DL techniques that have been implemented on predictive AI-based models across the BSC (pre-harvest, harvest, post-harvest, processing, and retail). The main contributions of this review are as follows: (a) to provide an empirical analysis of AI approaches, to recognize the performance of ML and DL approaches in the BSC, and (b) to put forward an AI framework based on real applications implemented along the stages of the BSC.

REVIEW

The review process followed the guidelines for systematic studies on software engineering (Wohlin, 2014Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. London, ACM Digital Library, Proceedings… p1-10. DOI: https://doi.org/10.1145/2601248.2601268. Accessed Jan 08, 2020.
https://doi.org/10.1145/2601248.2601268...
). This review covers recent literature published between 2015 and 2020 from digital databases (Web of Science, Science Direct, Scopus, Springer Link, IEEE Xplore, Wiley, Taylor & Francis, and Google Scholar). This study used the search keywords such as banana, machine learning, deep learning, prediction, estimation, and forecast. Different strings were defined for each database using search terms. The search for articles began in January 2021. The final database search was conducted in February 2021. The lists of references of the manuscripts selected for full-text reading were manually examined by the authors for potentially relevant studies, which was completed in June 2021. Fifty-two relevant papers from 23 countries were retrieved from all databases. The dataset of selected publications for this study is available for download and replication at this link: https://cutt.ly/VniXv2H.

AI applications

The review findings emphasize that researchers have developed and tested AI-based predictive models to contribute to banana challenges. For clustering into application areas, challenges in the BSC (Figure 1) were considered. As a result, an overview of the AI approaches in the BSC is shown in Figure 2. Eleven application areas were discovered in the 52 reviewed publications, as shown in Table 1. The application areas according to their number of publications found are the following (in decreasing order): ripeness (13 publications), diseases (11 publications), quality (5 publications), crop-type (5 publications), crop yield (5 publications), soil (4 publications), fruit recognition (4 publications), process parameters (2 publications), production for consumers (1 publication), age bunch (1 publication), and pest (1 publication).

FIGURE 2.
Clustering of selected publication by AI-application area.
TABLE 1.
Overview of AI approaches for bananas.

Figure 3 depicts the number of publications per year for the 11 application areas. The number of these types of publications has steadily increased each year. The annual growth rate was 66.10% (from 2015 to 2020). Additionally, there was an annual increase of 50% between 2019 and 2020. Similarly, analyzing the evolution of the application areas, both ripening and diseases have been considered the most active AI-application areas in BSC in the last five years. Eight of the 11 (73%) domain areas had publications in 2020, while six of the 11 (55%) had publications in 2019. Thus, process parameters, ripening, and quality were the first research works published between 2015 and 2016. Studies on leaf diseases and fruit recognition were published in 2017. During 2018 and 2019, predictive models for crop yield, crop type, and soil were indexed. Recently, banana-age bunches and pests have been developed as application areas for bananas in the BSC.

FIGURE 3.
Yearly publication according to application area.

Figure 4 introduces the distribution of publications to identify the most popular application area in BVC. Therefore, only ripeness and disease (two of 11 application areas) accounted for 46% of the selected publications. Additionally, ripeness, disease, crop type, quality grading, crop yield, and fruit recognition (6 application areas) accounted for 83% of the total publications (Figure 4). Thus, the classification of banana ripeness levels (13 publications) and diseased banana leaves (11 publications) are considered the most popular application areas. In this manner, we can conclude that reviewed publications belong to the stages at the beginning of the BSC (pre-harvest, harvest, and post-harvest), more than the latest stages (processing and retail), as shown Figure 2 and Figure 4.

FIGURE 4.
Distribution of publications by application area.

The review findings determine that the 52 selected publications were from 23 countries worldwide, as shown in Table 2. Thus, India and China are active countries that developed AI models related to seven and six application areas, respectively. Asian countries (India, China, Philippines, Taiwan, Thailand, Malaysia, and Indonesia) tend to research ripeness, soil, fruit recognition, and crop type. Countries such as Malaysia and Indonesia only have AI applications in terms of ripeness. Conversely, Latin American countries (Brazil, Peru, Ecuador, and Colombia) have published studies regarding diseases, pests, soil, and crop yield.

TABLE 2.
AI applications developed by country.

Individual summary of selected publications by application area

This section presents a brief description of the 52 selected publications of the 11 AI-application areas implemented for bananas (Table 1).

1) Classification of crop type:

Crop management is considered an essential activity in pre-harvest practices such as food security and healthy crop assessment (Benos et al., 2021Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: A comprehensive updated review. Sensors 21(11):1–55. DOI: https://doi.org/10.3390/s21113758
https://doi.org/10.3390/s21113758...
). Image processing and DL algorithms help in crop diagnosis in earlier stages (Kamilaris & Prenafeta-Boldú, 2018Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147:70–90. DOI: https://doi.org/10.1016/j.compag.2018.02.016
https://doi.org/10.1016/j.compag.2018.02...
).

Ringland et al. (2019)Ringland J, Bohm M, Baek SR (2019) Characterization of food cultivation along roadside transects with Google Street View imagery and deep learning. Computers and Electronics in Agriculture 158:36–50. DOI: https://doi.org/10.1016/j.compag.2019.01.014
https://doi.org/10.1016/j.compag.2019.01...
developed a DL tools for crop identification and applied them to Google Street View (GSV) imagery (street level) to characterize food cultivation practices along roadside transects at very high spatial resolution. The model used a CNN architecture, called Inception V3, and transfer learning to classify six major commodity field crops: banana, cassava, maize, eucalyptus, rice, scrub, and sugarcane.

Neupane et al. (2019)Neupane B, Horanont T, Hung ND (2019) Deep learning based banana plant detection and counting using high-resolution red–green-blue (RGB) images collected from unmanned aerial vehicle (UAV). PLOS ONE 14(10):e0223906. DOI: https://doi.org/10.1371/journal.pone.0223906
https://doi.org/10.1371/journal.pone.022...
proposed a DL model for banana plant, detection and counting, using high-resolution RGB images collected from an unmanned aerial vehicle (UAV). The CNN architecture, called Faster-RCNN, detects and counts the number of banana plants on an orchard. This application detects banana plants through yellow, red, and black markers, representing correct, incorrect, and missed detections, respectively.

Zhao et al. (2019)Zhao H, Chen Z, Jiang H, Jing W, Sun L, Feng M (2019) Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time - A Case Study in Zhanjiang, China. Remote Sensing 11(22):2673. DOI: https://doi.org/10.3390/rs11222673
https://doi.org/10.3390/rs11222673...
implemented CNN and RNN models for early-crop classification using imagery time series (satellite images). The model learned the phenological information of five crops: paddy, sugarcane, banana, pineapple, and eucalyptus. Thus, this model combines spatial and temporal patterns for crop classification.

Mandal et al. (2020)Mandal D, Kumar V, Rao YS (2020) An assessment of temporal RADARSAT-2 SAR data for crop classification using KPCA based support vector machine. Geocarto International: 1-28. DOI: https://doi.org/10.1080/10106049.2020.1783577
https://doi.org/10.1080/10106049.2020.17...
implemented an SVM classifier for six labels: rice, sugarcane, cotton, bananas, bare fields, and mixed classes. The predictive model used images of satellite multitemporal crop classification. The image dataset described the geometric features of the crop from a 4-month interval covering the critical crop-growth stages.

Sinha et al. (2020)Sinha P, Robson A, Schneider D, Kilic T, Mugera HK, Ilukor J, Tindamanyire JM (2020) The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda. ISPRS Journal of Photogrammetry and Remote Sensing 167:85–103. DOI: https://doi.org/10.1016/j.isprsjprs.2020.06.023
https://doi.org/10.1016/j.isprsjprs.2020...
developed a predictive model using the hyperspectral reflectance properties of individual banana leaves to differentiate 12 banana genotypes. Moreover, the study extrapolated ground-based hyperspectral measures from high-resolution WorldView-3 (WV3) satellite imagery. The study assessed two classification models: the CPPLS classification algorithm and RF-based classification.

2) Prediction of soil features:

Monitoring of soil properties is essential for agricultural management (Benos et al., 2021Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: A comprehensive updated review. Sensors 21(11):1–55. DOI: https://doi.org/10.3390/s21113758
https://doi.org/10.3390/s21113758...
). Banana crops have essential soil-nutritional requirements such as N, P, and K (Alves et al., 2015Alves PFS, dos Santos SR, Kondo MK, Pegoraro RF, Araújo ED (2015) Soil physical attributes in chemigated banana plantation with wastewater. Engenharia Agricola 35(6):998–1008. DOI: https://doi.org/10.1590/1809-4430-Eng.Agric.v35n6p998-1008/2015
https://doi.org/10.1590/1809-4430-Eng.Ag...
; de Andrade Neto et al., 2017de Andrade Neto TMD, Coelho EF, Silva ACPD (2017) Calcium nitrate concentrations in fertigation for “terra” banana production. Engenharia Agricola 37(2):385–393. DOI: https://doi.org/10.1590/1809-4430-Eng.Agric.v37n2p385-393/2017
https://doi.org/10.1590/1809-4430-Eng.Ag...
; Rajput et al., 2017Rajput A, Memon M, Memon KS, Tunio S, Sial TA, Khan MA (2017) Nutrient composition of banana fruit as affected by farm manure, composted pressmud and mineral fertilizers. Pakistan Journal of Botany 49(1):101–108. Available: https://www.pakbs.org/pjbot/PDFs/49(1)/14.pdf
https://www.pakbs.org/pjbot/PDFs/49(1)/1...
). Furthermore, the decrease of banana crop yield can be caused by a lack of soil nutrients or weak soil management (Cevallos et al., 2020Vite Cevallos H, Carvajal Romero H, Barrezueta Unda S (2020) Aplicación de algoritmos de aprendizaje automático para clasificar la fertilidad de un suelo bananero. Revista Conrado 16(72):15–19. Available: https://conrado.ucf.edu.cu/index.php/conrado/article/view/1202
https://conrado.ucf.edu.cu/index.php/con...
). Thus, healthy soil is associated with a low incidence of plant pathogens and diseases (David and Guico, 2019David AIC, Guico MLC (2019) Presence or absence of Fusarium oxysporum f. sp. cubense tropical Race 4 (TR4) classification using machine learning methods on soil properties IEEE. Region 10 Annual International Conference. (TENCON). IEEE, Jeju, Proceedings… DOI: https://doi.org/10.1109/TENCON.2018.8650116
https://doi.org/10.1109/TENCON.2018.8650...
). ML techniques help farmers in the decision-making process to improve the quality of soil and banana fruit.

David and Guico (2019)David AIC, Guico MLC (2019) Presence or absence of Fusarium oxysporum f. sp. cubense tropical Race 4 (TR4) classification using machine learning methods on soil properties IEEE. Region 10 Annual International Conference. (TENCON). IEEE, Jeju, Proceedings… DOI: https://doi.org/10.1109/TENCON.2018.8650116
https://doi.org/10.1109/TENCON.2018.8650...
developed a predictive model to classify the type of soil properties (suppressive and conducive soil) in a banana crop that can cause diseases (Panama). The model considered the following soil features as input of the model: dielectric properties, moisture, temperature, and sunlight. The study compared four ML models (GBC, KNN, SVM, and ANN) and selected the best algorithms with the best performance. This served as an early diagnosis of soil infections in banana crops.

Cevallos et al. (2020)Vite Cevallos H, Carvajal Romero H, Barrezueta Unda S (2020) Aplicación de algoritmos de aprendizaje automático para clasificar la fertilidad de un suelo bananero. Revista Conrado 16(72):15–19. Available: https://conrado.ucf.edu.cu/index.php/conrado/article/view/1202
https://conrado.ucf.edu.cu/index.php/con...
introduced an ML model to estimate soil quality (classifying into 3 labels: optimal, acceptable, and not acceptable). The study used soil-nutritional compounds (N, P, K, Ca, Mg, Fe, Mn, Cu, and Zn) as samples that were extracted from 0 to 30 cm deep from the soil of the banana crop. The author used seven ML algorithms (MLP, BN, LGR, DT, RF, SVM, and KNN) to compare their performances and chose the best predictive model.

Yuan et al. (2020)Yuan J, Wen T, Zhang H, Zhao M, Penton CR, Thomashow LS, Shen Q (2020) Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt. ISME Journal 14(12):2936–2950. DOI: https://doi.org/10.1038/s41396-020-0720-5
https://doi.org/10.1038/s41396-020-0720-...
focused on the classification of soils. The authors developed classification models for diseased and healthy soils using features such as bacterial and fungal samples. The study employed metadata from multiple independent sources (soil samples from different countries). It analyzed the soil of four crops (banana, cucumber, watermelon, and lily). Their proposal was based on detecting models of microbial disease patterns (fusarium wilt) and identifying microbial community characteristics used to predict soil health. The authors compared the performances of three ML algorithms: RF, SVM, and LGR.

Vigneswaran & Selvaganesh (2020)Vigneswaran E, Selvaganesh M (2020) Decision support system for crop rotation using. 4th International Conference on Inventive Systems and Control (ICISC). Machine Learning, Proceedings… DOI: https://doi.org/10.1109/ICISC47916.2020.9171120
https://doi.org/10.1109/ICISC47916.2020....
proposed modeling soil nutrients for crop rotation such as banana, rice, corn, and turmeric to search for better crops yield for farmers. The authors introduced a model to predict suitable crops for cultivation and crop rotation by measuring the soil quality. Regarding the features of the model, they considered soil samples with five nutrient values: urea, K, Mg, pH, and N. The study used neuro-fuzzy logic and RBF algorithms to develop the ML model.

3) Detection and classification of leaf diseases:

Banana crops are treated for a variety of diseases (Amara et al., 2017Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. In: Proceedings of Gesellschaft für Informatik Datenbanksysteme für business, Technologie und Web (BTW Workshop). Alemania, dblp computer science bibliography. p.79-88. Proceedings… Available: https://dblp.org/rec/conf/btw/AmaraBA17.html. Accessed Jan 08, 2020.
https://dblp.org/rec/conf/btw/AmaraBA17....
; Aruraj et al., 2019Aruraj A, Alex A, Subathra MSP, Sairamya NJ, Thomas George S, Vinodh Ewards SE (2019) Detection and classification of diseases of banana plant using local binary pattern and support vector machine. In: International Conference on Signal Processing and Communication (ICSPC). Proceedings... DOI: https://doi.org/10.1109/ICSPC46172.2019.8976582
https://doi.org/10.1109/ICSPC46172.2019....
), which cause injury to leaves and impact production loss to the farmers (Siddiq et al., 2020Siddiq M, Ahmed J, Lobo MG (2020) Handbook of banana production, postharvest science, processing technology, and nutrition. Hoboken, Wiley, 284 p. DOI: https://doi.org/10.1002/9781119528265
https://doi.org/10.1002/9781119528265...
). Therefore, detecting diseases at an earlier stage and thus taking preventive action to maintain healthy banana crops is challenging for farmers (Athiraja & Vijayakumar, 2021Athiraja A, Vijayakumar P (2021) Banana disease diagnosis using computer vision and machine learning methods. Journal of Ambient Intelligence and Humanized Computing 12(6):6537–6556. DOI: https://doi.org/10.1007/s12652-020-02273-8
https://doi.org/10.1007/s12652-020-02273...
). Moreover, detection and classification of leaf disease by using AI technology allow reducing a large work of monitoring in bigger crops (Singh and Misra, 2017Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture 4(1):41–49. DOI: https://doi.org/10.1016/j.inpa.2016.10.005
https://doi.org/10.1016/j.inpa.2016.10.0...
). According to the literature, image processing and ML classifiers assist in disease diagnosis.

Singh and Misra (2017)Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture 4(1):41–49. DOI: https://doi.org/10.1016/j.inpa.2016.10.005
https://doi.org/10.1016/j.inpa.2016.10.0...
presented a model that includes an image segmentation technique and a genetic algorithm for automatic detection as well as classification of plant leaves disease. Samples of banana leaf with early scorch disease, rose with bacterial disease, lemon leaf with Sun burn disease, and fungal disease in beans leaf are considered for input data. To classify the leaf disease, the authors proposed a new algorithm, then compared the performance of different ML methods such as MDC and SVM.

Amara et al. (2017)Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. In: Proceedings of Gesellschaft für Informatik Datenbanksysteme für business, Technologie und Web (BTW Workshop). Alemania, dblp computer science bibliography. p.79-88. Proceedings… Available: https://dblp.org/rec/conf/btw/AmaraBA17.html. Accessed Jan 08, 2020.
https://dblp.org/rec/conf/btw/AmaraBA17....
introduced a deep learning-based approach that automated the classification of banana leaves: healthy leaf, diseased leaf by black sigatoka, and diseased leaf by black speckle. The model learns the color and texture features of images (RGB and grayscale) using the LeNet89 architecture as a CNN to classify banana-leaf diseases.

Ferentinos (2018)Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145:311–318. DOI: https://doi.org/10.1016/j.compag.2018.01.009
https://doi.org/10.1016/j.compag.2018.01...
proposed a CNN architecture for the identification of plant diseases using simple leaf images (healthy or diseased). The study has three classifications: healthy banana leaves, black sigatoka, and black speckle (the last ones are leaf diseases). The study used five CNN architecture to compare the results.

Aruraj et al. (2019)Aruraj A, Alex A, Subathra MSP, Sairamya NJ, Thomas George S, Vinodh Ewards SE (2019) Detection and classification of diseases of banana plant using local binary pattern and support vector machine. In: International Conference on Signal Processing and Communication (ICSPC). Proceedings... DOI: https://doi.org/10.1109/ICSPC46172.2019.8976582
https://doi.org/10.1109/ICSPC46172.2019....
proposed texture-pattern techniques for identifying and classifying diseases in banana leaves. The study implemented SVM and KNN algorithms and compared the performances of two study cases: healthy vs. black sigatoka, and healthy vs. cordana leaf spot.

Liao et al. (2019)Liao W, Ochoa D, Gao L, Zhang B, Philips W (2019) Morphological analysis for banana disease detection in close range hyperspectral remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Proceedings…p.3697-3700. DOI: https://doi.org/10.1109/IGARSS.2019.8899087
https://doi.org/10.1109/IGARSS.2019.8899...
used labeled samples of late-stage banana-leaf disease to train the predictive model. In the study, banana-leaf disease was detected using spectral-spatial information through morphological profiles. The PCA and SVM algorithms were used to classify the three stages: early, middle, and late.

Tsai et al. (2019)Tsai C-F, Chen Y-C, Tsai C-E (2019) Real life image recognition of Panama disease by an effective deep learning approach. International Conference on Machine Learning and Cybernetics. Proceedings…1–5. DOI: https://doi.org/10.1109/ICMLC48188.2019.8949269
https://doi.org/10.1109/ICMLC48188.2019....
developed a real-life image-recognition method for panama disease using a DL approach. The study compared the performance of five CNN architectures (LeNet-5, VGG16, VGG-19, ResNet-34, and ResNet-50) with different activation functions. CNN models were used to extract original color images to identify two labels of banana leaves: normal banana and leaves infected by panama disease.

Campos et al. (2020) applied ML techniques and digital image processing to monitor the severity of a yellow sigatoka attack on banana crops. In the study, RGB aerial images by UAV were used. The study developed a predictive model in two stages: (1) classification to identify abnormal leaves using SVM and ANN, and (2) classification to identify scene elements, such as a healthy leaf, central vein, abnormal leaf, and soil, using the SVM technique.

Athiraja and Vijayakumar (2021)Athiraja A, Vijayakumar P (2021) Banana disease diagnosis using computer vision and machine learning methods. Journal of Ambient Intelligence and Humanized Computing 12(6):6537–6556. DOI: https://doi.org/10.1007/s12652-020-02273-8
https://doi.org/10.1007/s12652-020-02273...
proposed a ML model to (1) classify banana plants diseases such as panama wilt, leaf spot, virus diseases, crown rot, anthracnose, tip rot, and (2) recognize the disease cells into six labels: initial, very tiny, tiny, medium, high, and very high. Moreover, for that purpose the authors trained and compared the result of ML algorithms such as MDC, KNN, SVM, CBR, and ANFIS (a mixture of the technique of ANN and fuzzy logic). The ANFIS algorithm attained excellent results for all labels.

Chaudhari and Patil (2020)Chaudhari V, Patil M (2020) Banana leaf disease detection using K-means clustering and Feature extraction techniques. In: IEEE International Conference on Advances in Computing, Communication & Materials (ICACCM). Proceedings…p126–130. DOI: https://doi.org/10.1109/icaccm50413.2020.9212816
https://doi.org/10.1109/icaccm50413.2020...
introduced an automated system to identify banana-leaf diseases by extracting color, shape, and texture features. The authors used K-means clustering to segment images in diseased leaves and the SVM algorithm to classify diseases such as sigatoka, cucumber mosaic virus, banana bacterial wilt, and panama disease.

Selvaraj et al. (2020)Gomez Selvaraj M, Vergara A, Montenegro F, Alonso Ruiz H, Safari N, Raymaekers D, Ocimati W, Ntamwira J, Tits L, Omondi AB, Blomme G (2020) Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS Journal of Photogrammetry and Remote Sensing 169:110–124. DOI: https://doi.org/10.1016/j.isprsjprs.2020.08.025
https://doi.org/10.1016/j.isprsjprs.2020...
combined high-resolution satellite imagery data with advanced ML models to detect and classify banana plants and provide information regarding their overall health status. The model used image processing, then VGG-16 and Retina-Net (CNN architecture) to classify healthy and infected plants.

Criollo et al. (2020)Criollo A, Mendoza M, Saavedra E, Vargas G (2020) Design and evaluation of a convolutional neural network for banana leaf diseases classification. In: Proceedings of IEEE Engineering International Research Conference (EIRCON). Proceedings…p1-4. DOI: https://doi.org/10.1109/eircon51178.2020.9254072
https://doi.org/10.1109/eircon51178.2020...
implemented a CNN to detect banana-leaf diseases using RGB images. The predictive model was trained to classify three labels of banana-leaf diseases: black sigatoka, bacterial wilt, and health status.

4) Prediction of pest incidence:

Pests and diseases damage banana crops; therefore, they must be controlled to minimize post-harvest losses and ensure fruit quality (Siddiq et al., 2020Siddiq M, Ahmed J, Lobo MG (2020) Handbook of banana production, postharvest science, processing technology, and nutrition. Hoboken, Wiley, 284 p. DOI: https://doi.org/10.1002/9781119528265
https://doi.org/10.1002/9781119528265...
). After the review, only one study discussed pests in banana crops. Almeyda (2020)Almeyda E, Paiva J, Ipanaque W (2020) Pest incidence prediction in organic banana crops with machine learning techniques. In: Proceedings of IEEE Engineering International Research Conference (EIRCON). Proceedings…p1-4. DOI: https://doi.org/10.1109/eircon51178.2020.9254034
https://doi.org/10.1109/eircon51178.2020...
developed an ML model to predict the level of thrips pest incidence in banana crops. The author used two supervised learning algorithms: LGR and SVM. Climatological and soil data were used as features and the level of incidence (low and medium) as labels. To obtain climatological and soil data, a weather station and network of IoT sensors were implemented.

5) Classification of ripeness stages:

Ripeness is a significant topic in banana harvest because of its impact on fruit quality and price (Mazen & Nashat, 2019Mazen FMA, Nashat AA (2019) Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering 44(8):6901–6910. DOI: https://doi.org/10.1007/s13369-018-03695-5
https://doi.org/10.1007/s13369-018-03695...
). Farmers must identify banana ripeness levels to reduce losses during the post-harvest process and extend storage life due to bananas have a high rate of deterioration (Siddiq et al., 2020Siddiq M, Ahmed J, Lobo MG (2020) Handbook of banana production, postharvest science, processing technology, and nutrition. Hoboken, Wiley, 284 p. DOI: https://doi.org/10.1002/9781119528265
https://doi.org/10.1002/9781119528265...
). According to the literature, AI and computer vision help to detect efficiently banana ripeness.

Adebayo et al. (2016)Adebayo SE, Hashim N, Abdan K, Hanafi M, Mollazade K (2016) Prediction of quality attributes and ripeness classification of bananas using optical properties. Scientia Horticulturae 212:171–182. DOI: https://doi.org/10.1016/j.scienta.2016.09.045
https://doi.org/10.1016/j.scienta.2016.0...
(2017)Adebayo SE, Hashim N, Abdan K, Hanafi M, Zude-Sasse M (2017) Prediction of banana quality attributes and ripeness classification using artificial neural network. Acta Horticulturae 1152(1152):335–344. DOI: https://doi.org/10.17660/ActaHortic.2017.1152.45
https://doi.org/10.17660/ActaHortic.2017...
presented a classification system for predicting six banana ripening stages. Thus, the quality attributes of bananas, such as chlorophyll, elasticity, and soluble solids content were assessed for input data. The authors implemented an ANN classifier (using multilayer-perceptron).

Mohopatra et al. (2017) proposed a non-destructive assessment method to measure ripening stages of red banana (7 labels). The study analyzed dielectric properties as features for the prediction of seven labels of banana ripeness. It used three combination of ML algorithms for image processing: CLBP, LBP, NRLBP and FCM. For classification at different ripening, FCM clustering method gave far better results.

Zhang et al. (2018)Zhang Y, Lian J, Fan M, Zheng Y (2018) Deep indicator for fine-grained classification of banana’s ripening stages. EURASIP Journal on Image and Video Processing 2018(1):1–10. DOI: https://doi.org/10.1186/s13640-018-0284-8
https://doi.org/10.1186/s13640-018-0284-...
proposed a novel CNN classifier for bananas at seven ripening stages. The algorithm learned a set of fine-grained image features. Additionally, the study compared the performance of the CNN model with other ML algorithms such as SVM.

Chen et al. (2018)Chen LY, Wu CC, Chou TI, Chiu SW, Tang KT (2018) Development of a dual MOS electronic nose/camera system for improving fruit ripeness classification. Sensors 18(10):3256. DOI: https://doi.org/10.3390/s18103256
https://doi.org/10.3390/s18103256...
developed a method to monitor fruit maturity: unripe, half-ripe, fully ripe, and over-ripe. Their proposal focused on monitoring variations in the volatile organic compounds produced by the fruit during the maturation process. The predictive model used the SVM and KNN algorithms combined with PCA and LDA.

Mazen and Nashat (2019)Mazen FMA, Nashat AA (2019) Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering 44(8):6901–6910. DOI: https://doi.org/10.1007/s13369-018-03695-5
https://doi.org/10.1007/s13369-018-03695...
developed an automatic computer-vision system to identify the ripening stages of bananas between four labels: green, yellowish green, mid-ripen, and overripen. The proposed system is based on extracting texture features of the banana fruit, which used HSV color space, and development of brown spots. The model implemented numerous ML techniques such as ANN, SVM, NB, KNN, DT, and DAC. Consequently, ANN-based framework performed the best results.

Pu et al. (2019)Pu YY, Sun DW, Buccheri M, Grassi M, Cattaneo TMP, Gowen A (2019) Ripeness classification of Bananito fruit (Musa acuminata, AA): A comparison study of visible spectroscopy and hyperspectral imaging. Food Analytical Methods 12(8):1693–1704. DOI: https://doi.org/10.1007/s12161-019-01506-7
https://doi.org/10.1007/s12161-019-01506...
demonstrated hyperspectral imaging for accurate and non-destructive ripeness classification of bananito fruit (tree maturiry stages). The model is based on the visible peel spectra, considering features such as fruit firmness, soluble solids content, and color parameters. The study compared the experimental results of three algorithms: SVM, SIMCA, and PLSDA.

Sabilla et al. (2019)Sabilla IA, Wahyuni CS, Fatichah C, Herumurti D (2019) Determining banana types and ripeness from image using machine learning methods. In: IEEE International Conference of Artificial Intelligence and Information Technology (ICAIIT). Proceedings… DOI: https://doi.org/10.1109/ICAIIT.2019.8834490
https://doi.org/10.1109/ICAIIT.2019.8834...
determined ripeness levels of bananas based on banana peels and RGB images. Thus, the ripeness levels were assessed: unripe, ripe, and overripe. The model was trained using separately ML techniques such as SVM, KNN, and DT.

Vetrekar et al. (2019a)Vetrekar N, Ramachandra R, Raja KB, Gad RS (2019a) Multi-spectral imaging for artificial ripened banana detection. In: European Workshop on Visual Information Processing (EUVIP). Proceedings... DOI: https://doi.org/10.1109/EUVIP47703.2019.8946158
https://doi.org/10.1109/EUVIP47703.2019....
presented a multispectral imaging approach to acquire the eight spatial and spectral narrow spectrum bands across the VIS and NIR wavelength range to detect artificially ripened bananas using SVM classifier. Subsequently, the authors (Vetrekar et al., 2019b)Vetrekar N, Ramachandra R, Raja KB, Gad RS (2019b) Multi-spectral imaging to detect artificial ripening of banana: A comprehensive empirical study. In: IEEE International Conference on Imaging Systems and Techniques (IST). Proceedings... DOI: https://doi.org/10.1109/IST48021.2019.9010525
https://doi.org/10.1109/IST48021.2019.90...
presented another study on the detection of the artificial ripening of bananas using six different feature-extraction methods independently, complemented with SVM and ProCRC algorithms.

Saad et al. (2019)Saad D, Rotzer S, Zimmermann M (2019) Set-based design in agile development: Developing a banana sorting module – A practical approach. IEEE International Conference on Industrial Engineering and Engineering Management 159–164. DOI: https://doi.org/10.1109/IEEM44572.2019.8978748
https://doi.org/10.1109/IEEM44572.2019.8...
developed a banana-peeling machine for ripeness classification to reject bananas at a supermarket. The automatic system classified tree levels of maturity: unripe, ripe and overripe bananas. To reach that purpose, the authors developed a CNN architecture and used RGB images, which were captured by the banana-peeling machine.

Zhu and Spachos (2020)Zhu L, Spachos P (2020) Food grading system using support vector machine and YOLOv3 methods. In: IEEE Symposium on Computers and Communications (ISCC). Proceedings… p1-6. DOI: https://doi.org/10.1109/ISCC50000.2020.9219589
https://doi.org/10.1109/ISCC50000.2020.9...
implemented a two-layer image-processing system based on AI for banana grading. In the first stage, the model used an SVM algorithm regarding color and texture features to classify the ripening stages of bananas (unripened, ripened, and over-ripened). In the second stage, the model uses a CNN architecture known as YOLO v3 to classify specific ripeness stages: mid-ripened and well-ripened.

Ni et al. (2020)Ni J, Gao J, Deng L, Han Z (2020) Monitoring the change process of banana freshness by GoogLeNet. IEEE Access 8:228369–228376. DOI: https://doi.org/10.1109/ACCESS.2020.3045394:228369
https://doi.org/10.1109/ACCESS.2020.3045...
analyzed the banana-freshness changing process. The samples of banana appearance were gotten by the number of days it has been stored: one, three, five, seven, nine, and eleven days (seven labels in total). The predictive model used the GoogLeNet architecture (CNN) to classify banana freshness at different time intervals. Moreover, the results improved their accuracy after applying data augmentation for the dataset and transfer learning.

6) Prediction of age bunch:

Age-bunch control is an important practice during the harvest stage (Siddiq et al., 2020Siddiq M, Ahmed J, Lobo MG (2020) Handbook of banana production, postharvest science, processing technology, and nutrition. Hoboken, Wiley, 284 p. DOI: https://doi.org/10.1002/9781119528265
https://doi.org/10.1002/9781119528265...
). The maturity indices of banana bunches (immature, mature, and over-mature) is noticed through changes in color, size, shape, length, and volume. In this review, we discovered a unique study on the detection of banana-age bunches. Fu et al. (2020)Fu L, Duan J, Zou X, Lin J, Zhao L, Li J, Yang Z (2020) Fast and accurate detection of banana fruits in complex background orchards. IEEE Access 8:196835–196846. DOI: https://doi.org/10.1109/ACCESS.2020.3029215:196835
https://doi.org/10.1109/ACCESS.2020.3029...
introduced a tele-detection method for banana detection in natural environments under different illumination and occlusion conditions. The study used a regular RGB color camera to obtain banana-age bunch images at different growing stages (immature or mature) and shapes of bunches. The YOLOv4 architecture was trained and tested in different illumination environments (sunny front-light, sunny back-light, and cloudy conditions) to detect banana age-bunch.

7) Prediction of quality grading:

Although banana crops face several challenges, the international banana market has high-quality standards, according to FAO (2019)FAO - Food and Agriculture Organization for the United Nations (2019) Banana market Review. Rome, FAO. Available: http://www.fao.org/3/cb0168en/cb0168en.pdf.
http://www.fao.org/3/cb0168en/cb0168en.p...
. Farmers need to identify the levels of banana quality in an efficient way. Moreover, quality grading is essential to place sale prices of fruit in order to maximize their economic income. However, the morphological and physiological characteristics of bananas can be influenced by several aspects such as type of cultivar (Dittakan et al., 2017Dittakan K, Theera-Ampornpunt N, Witthayarat W, Hinnoy S, Klaiwan S, Pratheep T (2017) Banana cultivar classification using scale invariant shape analysis. In: International Conference on Information Technology (INCIT). Proceedings... DOI: https://doi.org/10.1109/INCIT.2017.8257854
https://doi.org/10.1109/INCIT.2017.82578...
), irrigation system (Arantes et al., 2018Arantes AdM, Donato SLR, De Siqueira DLd, Coelho EF (2018) Gas exchange in “Pome” banana plants grown under different irrigation systems. Engenharia Agricola 38(2):197–207. DOI: https://doi.org/10.1590/1809-4430-Eng.Agric.v38n2p197-207/2018
https://doi.org/10.1590/1809-4430-Eng.Ag...
), fertigation (de Andrade Neto et al., 2017de Andrade Neto TMD, Coelho EF, Silva ACPD (2017) Calcium nitrate concentrations in fertigation for “terra” banana production. Engenharia Agricola 37(2):385–393. DOI: https://doi.org/10.1590/1809-4430-Eng.Agric.v37n2p385-393/2017
https://doi.org/10.1590/1809-4430-Eng.Ag...
), among others. Thus, DL algorithms and computer-vision techniques work together to recognize banana quality fruit.

Sanaeifar et al. (2016)Sanaeifar A, Bakhshipour A, De La Guardia M (2016) Prediction of banana quality indices from color features using support vector regression. Talanta 148:54–61. DOI: https://doi.org/10.1016/j.talanta.2015.10.073
https://doi.org/10.1016/j.talanta.2015.1...
implemented a computer-vision system with ML algorithms to evaluate banana color features during the shelf life. The color features in different color spaces, including RGB, HSV, and L*a*b*, were selected as the inputs for the predictive models. Meanwhile, the quality indices, including firmness, total soluble solids, and pH, were chosen as outputs. The regression model compared the performance of SVR and the back-propagation MLP algorithm.

Olaniyi et al. (2017)Olaniyi EO, Adekunle AA, Odekuoye T, Khashman A (2017) Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations. Journal of Food Process Engineering 40(6):1–10. DOI: https://doi.org/10.1111/jfpe.12575
https://doi.org/10.1111/jfpe.12575...
developed an automatic system for the classification of healthy and defective bananas. The study implemented RBF, SVM and an ANN with back-propagation optimization and compared their performance models. The identification system used GLCM (gray level co-matrix) texture features such as contrast, energy, homogeneity, and entropy for training and testing the ML model classifier.

Piedad et al. (2018)Piedad EJ, Larada JI, Pojas GJ, Ferrer LVV (2018) Postharvest classification of banana (Musa acuminata) using tier-based machine learning. Postharvest Biology and Technology 145:93–100. DOI: https://doi.org/10.1016/j.postharvbio.2018.06.004
https://doi.org/10.1016/j.postharvbio.20...
introduced an ML model for estimating banana quality attributes. RGB color images and the length of the top middle finger of the banana tier were considered features for classifying banana tiers into four classes: extra class (export-quality fruit), class I (high-value domestic fruit), class II (local trade and consumption), and reject class (local trade with low cost). The authors used ML methods, such as MLP, SVM, and RF, and compared their classification accuracy.

Le et al. (2019)Le T-T, Lin C-Y, Piedad EJ (2019) Deep learning for noninvasive classification of clustered horticultural crops – A case for banana fruit tiers. Postharvest Biology and Technology 156. DOI: https://doi.org/10.1016/j.postharvbio.2019.05.023:110922
https://doi.org/10.1016/j.postharvbio.20...
developed a model to classify banana fruit tiers into two labels: normal and reject for sale. The study implemented a mask region-based CNN, called Mask R-CNN. This algorithm offers to detect banana images while at the same time generating a mask separating the fruit from its background. The model improved its performance after data augmentation was applied.

Ucat & Cruz (2019)Ucat RC, Cruz JCD (2019) Postharvest grading classification of cavendish banana using deep learning and Tensorflow. In: International Symposium on Multimedia and Communication Technology (ISMAC). Proceedings... DOI: https://doi.org/10.1109/ISMAC.2019.8836129
https://doi.org/10.1109/ISMAC.2019.88361...
presented a CNN model for grading the classification of post-harvest bananas. The banana was graded into four classes (size of a banana hand) according to the finger-size requirements (number of defects, diameter of the finger, and finger length). The study proposed image processing for physical-feature extraction from images (color and grayscale) and the HSV color space. The model was more effective for finger-size value extraction than for surface-defect value extraction.

8) Fruit recognition:

In recent years, human visual perception has been simulated using DL models to separate different fruits or vegetables in order to reduce cost and error (Sugadev et al., 2020Sugadev M, Sucharitha K, Sheeba IR, Velan B (2020) Computer vision based automated billing system for fruit stores. In: 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT). Proceedings. DOI: https://doi.org/10.1109/ICSSIT48917.2020.9214101
https://doi.org/10.1109/ICSSIT48917.2020...
). According to (Xue et al., 2020Xue G, Liu S, Ma Y (2020) A hybrid deep learning-based fruit classification using attention model and convolution autoencoder. Complex and Intelligent Systems Oct 1-11. DOI: https://doi.org/10.1007/s40747-020-00192-x
https://doi.org/10.1007/s40747-020-00192...
), an accurate fruit classification is considered of interest in the fresh supply chain, factories, and supermarkets. This domain area covers publications that introduced predictive models for object recognition of fruits (shape, texture, color, and other features) using deep neural networks, image processing, and computer vision.

Diattakan et al. (2017) presented a classifier for three banana cultivars using the morphological profile as a scale-invariant shape analysis. The authors developed the model using separately seven ML algorithms: SVM, DT, NB, AODE, BN, LGR, and ANN. The BN was the best classifier.

Mureşan and Oltean (2018)Mureşan H, Oltean M (2018) Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica 10(1):26–42. DOI: https://doi.org/10.2478/ausi-2018-0002
https://doi.org/10.2478/ausi-2018-0002...
developed a CNN architecture for fruit recognition. The authors used a large dataset containing images of a wide variety of fruits. The CNN classified 60 labels of fruits in total; however, only two labels belong to type of bananas: green and red banana.

Xue et al. (2020)Xue G, Liu S, Ma Y (2020) A hybrid deep learning-based fruit classification using attention model and convolution autoencoder. Complex and Intelligent Systems Oct 1-11. DOI: https://doi.org/10.1007/s40747-020-00192-x
https://doi.org/10.1007/s40747-020-00192...
presented a hybrid deep-learning-based fruit image classification approach. The authors developed a CNN model (CAE-ADN) to classify fruits. The model was tested using datasets with labels of fruit such as apple, banana, carambola, guava, kiwi, mango, etc.

Sugadev et al. (2020)Sugadev M, Sucharitha K, Sheeba IR, Velan B (2020) Computer vision based automated billing system for fruit stores. In: 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT). Proceedings. DOI: https://doi.org/10.1109/ICSSIT48917.2020.9214101
https://doi.org/10.1109/ICSSIT48917.2020...
introduced a predictive model for identifying fruits such as Granny Smith apples, papaya, and banana. A CNN architecture was used in the study. Thus, the authors developed a real-time prototype to automate the billing process in fruit shops to reduce billing time compared to conventional billing techniques.

9) Forecasting of crop yield:

Estimating a future scenario by considering the behavior of past events is the based idea of forecasting (Rathod and Mishra, 2018Rathod S, Mishra GC (2018) Statistical models for forecasting mango and banana yield of Karnataka, India. Journal of Agricultural Science and Technology 20(4):803–816. Available: http://jast.modares.ac.ir/article-23-19768-en.html
http://jast.modares.ac.ir/article-23-197...
). If farmers know in advance their crop yield, they will make planning more effectively and efficiently for storing, pricing and marketing (de Souza et al., 2019de Souza AV, Bonini Neto A, Cabrera Piazentin J, Dainese Junior BJ, Perin Gomes E, Dos Santos Batista Bonini C, Ferrari Putti F (2019) Artificial neural network modelling in the prediction of bananas’ harvest. Scientia Horticulturae 257. DOI: https://doi.org/10.1016/j.scienta.2019.108724:108724
https://doi.org/10.1016/j.scienta.2019.1...
). In that context, ML models using regression techniques or statistical models are trained for estimating yield of banana harvest.

Rathod and Mishra (2018)Rathod S, Mishra GC (2018) Statistical models for forecasting mango and banana yield of Karnataka, India. Journal of Agricultural Science and Technology 20(4):803–816. Available: http://jast.modares.ac.ir/article-23-19768-en.html
http://jast.modares.ac.ir/article-23-197...
proposed a hybrid model using time-series data to forecast the yields of bananas and mangoes. This model employed ARIMA (statistical analysis model) and TDNN, and nonlinear SVR was used for nonlinear modeling. Thus, the experimental results attained better performance with a hybrid model that considered both ARIMA and non-linear SVR algorithms.

Rebortera and Fajardo (2019b)Rebortera M, Fajardo A (2019b) Forecasting banana harvest yields using deep learning. In: International Conference on System Engineering Technology (ICSET). Proceedings…p380-384. DOI: https://doi.org/10.1109/ICSEngT.2019.8906427
https://doi.org/10.1109/ICSEngT.2019.890...
introduced a DL-based model to forecast banana harvest yield. The model employed time-series data for the number of bunches cuts. The experimental results demonstrated that the AI technique, RNN-LSTM, had better performance compared to conventional models such as ARIMA. In another study, Rebortera and Fajardo (2019a)Rebortera M, Fajardo A (2019a) An enhanced deep learning approach in forecasting banana harvest yields. International Journal of Advanced Computer Science and Applications 10(9):275–280. DOI: https://doi.org/10.14569/IJACSA.2019.0100935
https://doi.org/10.14569/IJACSA.2019.010...
proposed a multiple LSTM model for forecasting banana harvest yields. The study compared the experimental results from three LSTM-based models: simple layer, multiple layers, and an enhanced LSTM layer. For forecasting, the enhanced model outperformed single-and multiple-layer models.

de Souza et al. (2019)de Souza AV, Bonini Neto A, Cabrera Piazentin J, Dainese Junior BJ, Perin Gomes E, Dos Santos Batista Bonini C, Ferrari Putti F (2019) Artificial neural network modelling in the prediction of bananas’ harvest. Scientia Horticulturae 257. DOI: https://doi.org/10.1016/j.scienta.2019.108724:108724
https://doi.org/10.1016/j.scienta.2019.1...
used an MLP to estimate the banana harvest period (number of days) in subtropical regions. The authors analyzed the relationship between climatic variables during the banana bunch gestation period to predict the time of banana harvest.

de Lima Neto et al. (2020)de Lima Neto AJ, Deus JAL, Rodrigues Filho VA, Natale W, Parent LE (2020) Nutrient diagnosis of fertigated “prata” and “cavendish” banana (Musa spp.) at plot-scale. Plants 9(11):1467. DOI: https://doi.org/10.3390/plants9111467
https://doi.org/10.3390/plants9111467...
implemented an ML model to classify banana crop yields (high or low) at a local scale. Soil nutrients diagnosis was fundamental in the study. The classifier used information about the type of banana cultivar, soil-nutrient composition, and time of harvest as features. The study compared the performance of two ML classifier: ANN and RF algorithms.

10) Estimation of drying process parameters:

Optimization of the outcome parameters is considered relevant in the culinary banana drying process (Siddiq et al., 2020Siddiq M, Ahmed J, Lobo MG (2020) Handbook of banana production, postharvest science, processing technology, and nutrition. Hoboken, Wiley, 284 p. DOI: https://doi.org/10.1002/9781119528265
https://doi.org/10.1002/9781119528265...
). Particularly, phenolic compounds (Vu et al., 2018Vu HT, Scarlett CJ, Vuong QV (2018) Phenolic compounds within banana peel and their potential uses: A review. Journal of Functional Foods 40:238–248. DOI: https://doi.org/10.1016/j.jff.2017.11.006
https://doi.org/10.1016/j.jff.2017.11.00...
) and their antioxidant properties (Segundo et al., 2017Segundo C, Román L, Lobo M, Martinez MM, Gómez M (2017) Ripe banana flour as a source of antioxidants in layer and sponge cakes. Plant Foods for Human Nutrition 72(4):365–371. DOI: https://doi.org/10.1007/s11130-017-0630-5
https://doi.org/10.1007/s11130-017-0630-...
) have been the most studied. Thus, ML models can conduct to model and forecast a future phenomenon based on factors which influence it, using regression technique and correlation analysis.

Guiné et al. (2015)Guiné RPF, Barroca MJ, Gonçalves FJ, Alves M, Oliveira S, Mendes M (2015) Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry 168:454–459. DOI: https://doi.org/10.1016/j.foodchem.2014.07.094
https://doi.org/10.1016/j.foodchem.2014....
presented a model to estimate two output parameters, antioxidant activity, and phenolic compound content, for the banana drying process. The banana variety, dryness state, and order of extraction were considered as input variables. In the study, a regression model was developed for each parameter using an ANN algorithm.

Khawas et al. (2016)Khawas P, Dash KK, Das AJ, Deka SC (2016) Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm. Drying Technology 34(4):491–503. DOI: https://doi.org/10.1080/07373937.2015.1060605
https://doi.org/10.1080/07373937.2015.10...
estimated the quality of the overcome process evaluated using four output parameters for vacuum drying of culinary bananas: rehydration ratio, scavenging activity, non-enzymatic browning (color), and hardness (texture). The researchers considered only three input features for the regression model: drying temperature, sample slice thickness, and pretreatment. The study compared the performance of the predictive model using the MLP algorithm, which was optimized using GA.

11) Forecasting of banana production:

Forecasting banana harvest yield is a valuable mechanism for planning future production in the agricultural sector (Rebortera & Fajardo, 2019aRebortera M, Fajardo A (2019a) An enhanced deep learning approach in forecasting banana harvest yields. International Journal of Advanced Computer Science and Applications 10(9):275–280. DOI: https://doi.org/10.14569/IJACSA.2019.0100935
https://doi.org/10.14569/IJACSA.2019.010...
). Thus, forecasting production offers assistance in planning the future and decision-making process for sustainable growth of a country (Rathod and Mishra, 2018Rathod S, Mishra GC (2018) Statistical models for forecasting mango and banana yield of Karnataka, India. Journal of Agricultural Science and Technology 20(4):803–816. Available: http://jast.modares.ac.ir/article-23-19768-en.html
http://jast.modares.ac.ir/article-23-197...
). A study on forecasting of banana production has been reported in the literature (Rehman et al. 2018Rehman A, Deyuan Z, Hussain I, Iqbal MS, Yang Y, Jingdong L (2018) Prediction of major agricultural fruits production in Pakistan by using an econometric analysis and machine learning technique. International Journal of Fruit Science 18(4):445–461. DOI: https://doi.org/10.1080/15538362.2018.1485536
https://doi.org/10.1080/15538362.2018.14...
). This study from an Asian country introduced a regression model to estimate fruit production such as banana, apple, citrus, pears, and grapes. The researchers employed annual time-series data and implemented MLReg to forecast banana production (in tons).

ML and DL algorithms

AI has been implemented in agriculture in recent years (Benos et al., 2021Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021) Machine learning in agriculture: A comprehensive updated review. Sensors 21(11):1–55. DOI: https://doi.org/10.3390/s21113758
https://doi.org/10.3390/s21113758...
; Jha et al., 2019Jha K, Doshi A, Patel P, Shah M (2019) A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture 2:1–12. DOI: https://doi.org/10.1016/j.aiia.2019.05.004
https://doi.org/10.1016/j.aiia.2019.05.0...
; Kamilaris & Prenafeta-Boldú, 2018Kamilaris A, Prenafeta-Boldú FX (2018) Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147:70–90. DOI: https://doi.org/10.1016/j.compag.2018.02.016
https://doi.org/10.1016/j.compag.2018.02...
; Liakos et al., 2018Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D (2018) Machine learning in agriculture: A review. Sensors 18(8):2674. DOI: https://doi.org/10.3390/s18082674
https://doi.org/10.3390/s18082674...
; Sharma et al., 2020Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A (2020) A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Computers and Operations Research 119. DOI: https://doi.org/10.1016/j.cor.2020.104926:104926
https://doi.org/10.1016/j.cor.2020.10492...
). Numerous ML and DL algorithms exist (Mohri et al., 2018Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of machine learning (2nd ed.). Available: https://d1rkab7tlqy5f1.cloudfront.net/EWI/Over. Cambridge, MIT Press, 427p.
https://d1rkab7tlqy5f1.cloudfront.net/EW...
). These techniques are categorized by learning methods, such as supervised, unsupervised, and reinforcement (Elavarasan et al., 2018Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture 155:257–282. DOI: https://doi.org/10.1016/j.compag.2018.10.024
https://doi.org/10.1016/j.compag.2018.10...
; Mohri et al., 2018Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of machine learning (2nd ed.). Available: https://d1rkab7tlqy5f1.cloudfront.net/EWI/Over. Cambridge, MIT Press, 427p.
https://d1rkab7tlqy5f1.cloudfront.net/EW...
; Rehman et al., 2019Rehman TU, Mahmud MS, Chang YK, Jin J, Shin J (2019) Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture 156:585–605. DOI: https://doi.org/10.1016/j.compag.2018.12.006
https://doi.org/10.1016/j.compag.2018.12...
). Some of these require a powerful computing environment, such as DL learning techniques (Patrício & Rieder, 2018Patrício DI, Rieder R (2018) Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture 153:69–81. DOI: https://doi.org/10.1016/j.compag.2018.08.001
https://doi.org/10.1016/j.compag.2018.08...
; Zhou et al., 2017Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: Opportunities and challenges. Neurocomputing 237:350–361. DOI: https://doi.org/10.1016/j.neucom.2017.01.026
https://doi.org/10.1016/j.neucom.2017.01...
).

After the review, we identified more than 30 algorithms that belong to supervised and unsupervised learning, as listed in Table 3. Researchers and practitioners have applied supervised algorithms, such as ANN, SVM, RF, and CNN, and unsupervised algorithms, such as KNN, KM, and PCA. Moreover, we discovered only a few models that used unsupervised learning techniques. However, reinforcement learning was not found during the review process.

TABLE 3.
ML & DL algorithms implemented along the BSC.

Table 4 lists the most commonly used AI techniques in the reviewed publications along the BSC. On the one hand, ML algorithms, SVM appeared in 22 publications (42% of all publications), while KNN, ANN (for classification), and RF were used in nine, eight, and six publications, respectively. Moreover, ML algorithms such as ANN (for regression), MLP, LGR, LSTM, and RBF appeared in three publications on average. The top-2 most popular AI-application areas (Figure 4) have implemented object recognition or classification tasks (46% of total publications).

TABLE 4.
Top 5 most-used techniques.

On the other hand, DL algorithms have been frequently implemented by researchers. CNN appeared in 18 publications (35%). Popular CNN architectures (such as AlexNet, GoogLeNet, VGG16, VGG-19, ResNet-34, ResNet-50, LeNet89, LeNet-5, Retina-Net, Yolov3, Yolov4, CAE-AND, and Mask R-CNN) were used to train the predictive model in the BVC domain. These studies typically used a large dataset of images (GRB, multispectral and hyperspectral).

In this manner, SVM, CNN, KNN, ANN, and RF, were frequently selected for the machine-training process. SVM (ML method) and CNN (DL method) were the most implemented methods used to solve prediction banana tasks. These two methods appeared in 22 and 18 publications, respectively. SVM was used in the learning process of the classification or detection of crop type, soil quality, leaf diseases, pest incidence, bunch maturity, quality grading, and fruit recognition. Several CNN architectures have been implemented in predictive models for classifying crop type, leaf diseases, ripeness stages, age bunch, and fruit recognition.

Additionally, the hybrid approach has expanded over the last five years (Abiodun et al., 2018Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: A survey. Heliyon 4(11):e00938. DOI: https://doi.org/10.1016/j.heliyon.2018.e00938
https://doi.org/10.1016/j.heliyon.2018.e...
; Elavarasan et al., 2018Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: A survey. Computers and Electronics in Agriculture 155:257–282. DOI: https://doi.org/10.1016/j.compag.2018.10.024
https://doi.org/10.1016/j.compag.2018.10...
; Zhou et al., 2017Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: Opportunities and challenges. Neurocomputing 237:350–361. DOI: https://doi.org/10.1016/j.neucom.2017.01.026
https://doi.org/10.1016/j.neucom.2017.01...
). Hybrid models combine two or more learning algorithms to achieve better performance (effectiveness and efficiency), compared to the conventional ML or DL techniques. Thus, hybrid AI-based models were discovered for banana-outcome variables, with optimal results: CNN and CAE-ADN (Xue et al., 2020Xue G, Liu S, Ma Y (2020) A hybrid deep learning-based fruit classification using attention model and convolution autoencoder. Complex and Intelligent Systems Oct 1-11. DOI: https://doi.org/10.1007/s40747-020-00192-x
https://doi.org/10.1007/s40747-020-00192...
); nonlinear SVR and ARIMA (Rathod & Mishra, 2018Rathod S, Mishra GC (2018) Statistical models for forecasting mango and banana yield of Karnataka, India. Journal of Agricultural Science and Technology 20(4):803–816. Available: http://jast.modares.ac.ir/article-23-19768-en.html
http://jast.modares.ac.ir/article-23-197...
); ANN, and GA (Khawas et al., 2016Khawas P, Dash KK, Das AJ, Deka SC (2016) Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm. Drying Technology 34(4):491–503. DOI: https://doi.org/10.1080/07373937.2015.1060605
https://doi.org/10.1080/07373937.2015.10...
).

Performance metrics

Regarding the performance of the learning process, metrics are essential to evaluate the model. For classification models, researchers usually reported accuracy and precision as the performance metrics. Meanwhile, for regression models, studies reported the RMSE and MSE in the majority of publications.

The review findings show that AI-based models for BSC have reported a wide range of performance results. Due to researchers have trained more than two algorithms, even hybrid algorithms, and have selected the best performance. In this study, we have presented the best performance models reported (metrics and their values) by each selected publication.

Table 3 shows that some ML algorithms obtained an average accuracy close to 90% along the BSC. Classification models for soil, disease, and fruit recognition have reported accuracies between 84% and 90%. Meanwhile, predictive models for ripeness and quality grading have attained over 94% of accuracy. For the classification task, the CNN and SVM algorithms performed better than other algorithms. Conversely, the regression models reported numerous error values. Some models obtained error values close to zero, whereas others reported high error values. Thus, an efficient learning algorithm depends on large amounts of data (Zhou et al., 2017Zhou L, Pan S, Wang J, Vasilakos AV (2017) Machine learning on big data: Opportunities and challenges. Neurocomputing 237:350–361. DOI: https://doi.org/10.1016/j.neucom.2017.01.026
https://doi.org/10.1016/j.neucom.2017.01...
), feature engineering techniques used (Van Klompenburg et al., 2020)Van Klompenburg T, Kassahun A, Catal C (2020) Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture 177. DOI: https://doi.org/10.1016/j.compag.2020.105709:105709
https://doi.org/10.1016/j.compag.2020.10...
, among others.

Proposed framework and future challenges

AI-BSC performance framework

The review findings confirmed that AI-driven technologies improved BSC challenges. We recapitulated all findings from the literature to develop an AI-BSC performance application framework. Thus, it is useful to propose a framework and define future challenges to be considered by researchers, practitioners, policymakers, and other decision-makers in the BSC. Figure 5 presents the proposed framework of AI-based models for bananas which covers four components: stages of the BSC, application areas, ML and DL algorithms, and impacts.

FIGURE 5.
Proposed framework: AI for bananas.

The first component of this framework is the BSC stage. It represents the major agricultural task along the BSC: pre-harvest, harvest, post-harvest, processing, and retail. The review revealed that diverse challenges were addressed at each stage. Thus, the second component covered the challenges faced in the 11 application areas. For example, data on crop type, soil, disease, and pest are used to improve the decision-making process in pre-harvest using ML and DL models. The generated data are fundamental factors considered in AI-based models. In the pre-harvest stage, IoT sensor networks, weather stations, and digital cameras are used to generate data. Furthermore, a computer-vision system is required to capture and store data (such as RGB, multispectral, and hyperspectral images) in the harvest and post-harvest stages. Conversely, time-series data collected from sources including plantations, food processing, and macro-economic reports are used to forecast crop yield, process parameters, and production, respectively.

The third component is related to the learning algorithms. The study discovered that supervised, unsupervised, and hybrid learning techniques were used to train and test AI-based models to address challenges and improve the BSC performance. Although reinforcement learning was not presented in the literature, it represents an opportunity to begin research.

The last component addresses the impacts and benefits of the BSC performance. From this survey, we identified the contributions of ML and DL models to solving complex problems and achieving sustainable performance in all stages of the BSC. In the pre-harvest stage, specifically in crop and soil monitoring and management, research provides a beneficial solution for automating the tracking of productivity by remote sensing monitoring (Neupane et al., 2019Neupane B, Horanont T, Hung ND (2019) Deep learning based banana plant detection and counting using high-resolution red–green-blue (RGB) images collected from unmanned aerial vehicle (UAV). PLOS ONE 14(10):e0223906. DOI: https://doi.org/10.1371/journal.pone.0223906
https://doi.org/10.1371/journal.pone.022...
; Ringland et al., 2019Ringland J, Bohm M, Baek SR (2019) Characterization of food cultivation along roadside transects with Google Street View imagery and deep learning. Computers and Electronics in Agriculture 158:36–50. DOI: https://doi.org/10.1016/j.compag.2019.01.014
https://doi.org/10.1016/j.compag.2019.01...
), accurate information on food security (Sinha et al., 2020Sinha P, Robson A, Schneider D, Kilic T, Mugera HK, Ilukor J, Tindamanyire JM (2020) The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda. ISPRS Journal of Photogrammetry and Remote Sensing 167:85–103. DOI: https://doi.org/10.1016/j.isprsjprs.2020.06.023
https://doi.org/10.1016/j.isprsjprs.2020...
; Zhao et al., 2019Zhao H, Chen Z, Jiang H, Jing W, Sun L, Feng M (2019) Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time - A Case Study in Zhanjiang, China. Remote Sensing 11(22):2673. DOI: https://doi.org/10.3390/rs11222673
https://doi.org/10.3390/rs11222673...
), sustainability of agricultural land use (David & Guico, 2019David AIC, Guico MLC (2019) Presence or absence of Fusarium oxysporum f. sp. cubense tropical Race 4 (TR4) classification using machine learning methods on soil properties IEEE. Region 10 Annual International Conference. (TENCON). IEEE, Jeju, Proceedings… DOI: https://doi.org/10.1109/TENCON.2018.8650116
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; Yuan et al., 2020Yuan J, Wen T, Zhang H, Zhao M, Penton CR, Thomashow LS, Shen Q (2020) Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt. ISME Journal 14(12):2936–2950. DOI: https://doi.org/10.1038/s41396-020-0720-5
https://doi.org/10.1038/s41396-020-0720-...
), and integrated nutrient management (Cevallos et al., 2020Vite Cevallos H, Carvajal Romero H, Barrezueta Unda S (2020) Aplicación de algoritmos de aprendizaje automático para clasificar la fertilidad de un suelo bananero. Revista Conrado 16(72):15–19. Available: https://conrado.ucf.edu.cu/index.php/conrado/article/view/1202
https://conrado.ucf.edu.cu/index.php/con...
; Vigneswaran & Selvaganesh, 2020Vigneswaran E, Selvaganesh M (2020) Decision support system for crop rotation using. 4th International Conference on Inventive Systems and Control (ICISC). Machine Learning, Proceedings… DOI: https://doi.org/10.1109/ICISC47916.2020.9171120
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; Yuan et al., 2020Yuan J, Wen T, Zhang H, Zhao M, Penton CR, Thomashow LS, Shen Q (2020) Predicting disease occurrence with high accuracy based on soil macroecological patterns of Fusarium wilt. ISME Journal 14(12):2936–2950. DOI: https://doi.org/10.1038/s41396-020-0720-5
https://doi.org/10.1038/s41396-020-0720-...
). The literature on the detection and classification of banana diseases has positive impact on automatic disease detection (Amara et al., 2017Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. In: Proceedings of Gesellschaft für Informatik Datenbanksysteme für business, Technologie und Web (BTW Workshop). Alemania, dblp computer science bibliography. p.79-88. Proceedings… Available: https://dblp.org/rec/conf/btw/AmaraBA17.html. Accessed Jan 08, 2020.
https://dblp.org/rec/conf/btw/AmaraBA17....
; Aruraj et al., 2019Aruraj A, Alex A, Subathra MSP, Sairamya NJ, Thomas George S, Vinodh Ewards SE (2019) Detection and classification of diseases of banana plant using local binary pattern and support vector machine. In: International Conference on Signal Processing and Communication (ICSPC). Proceedings... DOI: https://doi.org/10.1109/ICSPC46172.2019.8976582
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; Ferentinos, 2018Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145:311–318. DOI: https://doi.org/10.1016/j.compag.2018.01.009
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; Singh & Misra, 2017Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture 4(1):41–49. DOI: https://doi.org/10.1016/j.inpa.2016.10.005
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; Tsai et al., 2019Tsai C-F, Chen Y-C, Tsai C-E (2019) Real life image recognition of Panama disease by an effective deep learning approach. International Conference on Machine Learning and Cybernetics. Proceedings…1–5. DOI: https://doi.org/10.1109/ICMLC48188.2019.8949269
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), increasing productive efficiency (Campos Calou et al., 2020Calou VBC, Teixeira AdS, Moreira LCJ, Lima CS, de Oliveira JB, de Oliveira MRR (2020) The use of UAVs in monitoring yellow sigatoka in banana. Biosystems Engineering 193:115–125. DOI: https://doi.org/10.1016/j.biosystemseng.2020.02.016
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), healthy production (Athiraja & Vijayakumar, 2021Athiraja A, Vijayakumar P (2021) Banana disease diagnosis using computer vision and machine learning methods. Journal of Ambient Intelligence and Humanized Computing 12(6):6537–6556. DOI: https://doi.org/10.1007/s12652-020-02273-8
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), enhancement of crop yield, reduction of losses (Almeyda et al., 2020Almeyda E, Paiva J, Ipanaque W (2020) Pest incidence prediction in organic banana crops with machine learning techniques. In: Proceedings of IEEE Engineering International Research Conference (EIRCON). Proceedings…p1-4. DOI: https://doi.org/10.1109/eircon51178.2020.9254034
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; Chaudhari & Patil, 2020Chaudhari V, Patil M (2020) Banana leaf disease detection using K-means clustering and Feature extraction techniques. In: IEEE International Conference on Advances in Computing, Communication & Materials (ICACCM). Proceedings…p126–130. DOI: https://doi.org/10.1109/icaccm50413.2020.9212816
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; Criollo et al., 2020Criollo A, Mendoza M, Saavedra E, Vargas G (2020) Design and evaluation of a convolutional neural network for banana leaf diseases classification. In: Proceedings of IEEE Engineering International Research Conference (EIRCON). Proceedings…p1-4. DOI: https://doi.org/10.1109/eircon51178.2020.9254072
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), and prevention of revenue losses for farmers (Liao et al., 2019Liao W, Ochoa D, Gao L, Zhang B, Philips W (2019) Morphological analysis for banana disease detection in close range hyperspectral remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Proceedings…p.3697-3700. DOI: https://doi.org/10.1109/IGARSS.2019.8899087
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). Additionally, researchers declare that intelligence models help to save time and human effort toward manual monitoring (Aruraj et al., 2019Aruraj A, Alex A, Subathra MSP, Sairamya NJ, Thomas George S, Vinodh Ewards SE (2019) Detection and classification of diseases of banana plant using local binary pattern and support vector machine. In: International Conference on Signal Processing and Communication (ICSPC). Proceedings... DOI: https://doi.org/10.1109/ICSPC46172.2019.8976582
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) and provide valuable assistance to agronomists (Ferentinos, 2018Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145:311–318. DOI: https://doi.org/10.1016/j.compag.2018.01.009
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), which contributes to operational flexibility in crop monitoring (Selvaraj et al., 2020Gomez Selvaraj M, Vergara A, Montenegro F, Alonso Ruiz H, Safari N, Raymaekers D, Ocimati W, Ntamwira J, Tits L, Omondi AB, Blomme G (2020) Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS Journal of Photogrammetry and Remote Sensing 169:110–124. DOI: https://doi.org/10.1016/j.isprsjprs.2020.08.025
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).

During the harvest stage, the applications of ML and DL varied. The main benefit is automated non-destructive methods of ripening assessment with better accuracy (Chen et al., 2018Chen LY, Wu CC, Chou TI, Chiu SW, Tang KT (2018) Development of a dual MOS electronic nose/camera system for improving fruit ripeness classification. Sensors 18(10):3256. DOI: https://doi.org/10.3390/s18103256
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; Mohapatra et al., 2017Mohapatra A, Shanmugasundaram S, Malmathanraj R (2017) Grading of ripening stages of red banana using dielectric properties changes and image processing approach. Computers and Electronics in Agriculture 143:100–110. DOI: https://doi.org/10.1016/j.compag.2017.10.010
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; Pu et al., 2019Pu YY, Sun DW, Buccheri M, Grassi M, Cattaneo TMP, Gowen A (2019) Ripeness classification of Bananito fruit (Musa acuminata, AA): A comparison study of visible spectroscopy and hyperspectral imaging. Food Analytical Methods 12(8):1693–1704. DOI: https://doi.org/10.1007/s12161-019-01506-7
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; Saad et al., 2019Saad D, Rotzer S, Zimmermann M (2019) Set-based design in agile development: Developing a banana sorting module – A practical approach. IEEE International Conference on Industrial Engineering and Engineering Management 159–164. DOI: https://doi.org/10.1109/IEEM44572.2019.8978748
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; Zhu & Spachos, 2020Zhu L, Spachos P (2020) Food grading system using support vector machine and YOLOv3 methods. In: IEEE Symposium on Computers and Communications (ISCC). Proceedings… p1-6. DOI: https://doi.org/10.1109/ISCC50000.2020.9219589
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). Particularly, the authors confirmed that these models can minimize expenses from destructive traditional techniques (Zhang et al., 2018Zhang Y, Lian J, Fan M, Zheng Y (2018) Deep indicator for fine-grained classification of banana’s ripening stages. EURASIP Journal on Image and Video Processing 2018(1):1–10. DOI: https://doi.org/10.1186/s13640-018-0284-8
https://doi.org/10.1186/s13640-018-0284-...
), reduce mistakes made with the naked eye (giving uniform results) (Sabilla et al., 2019Sabilla IA, Wahyuni CS, Fatichah C, Herumurti D (2019) Determining banana types and ripeness from image using machine learning methods. In: IEEE International Conference of Artificial Intelligence and Information Technology (ICAIIT). Proceedings… DOI: https://doi.org/10.1109/ICAIIT.2019.8834490
https://doi.org/10.1109/ICAIIT.2019.8834...
; Vetrekar et al., 2019bVetrekar N, Ramachandra R, Raja KB, Gad RS (2019b) Multi-spectral imaging to detect artificial ripening of banana: A comprehensive empirical study. In: IEEE International Conference on Imaging Systems and Techniques (IST). Proceedings... DOI: https://doi.org/10.1109/IST48021.2019.9010525
https://doi.org/10.1109/IST48021.2019.90...
, 2019aVetrekar N, Ramachandra R, Raja KB, Gad RS (2019a) Multi-spectral imaging for artificial ripened banana detection. In: European Workshop on Visual Information Processing (EUVIP). Proceedings... DOI: https://doi.org/10.1109/EUVIP47703.2019.8946158
https://doi.org/10.1109/EUVIP47703.2019....
), and enhance the quality and safety of fruits for consumption (Adebayo et al., 2016Adebayo SE, Hashim N, Abdan K, Hanafi M, Mollazade K (2016) Prediction of quality attributes and ripeness classification of bananas using optical properties. Scientia Horticulturae 212:171–182. DOI: https://doi.org/10.1016/j.scienta.2016.09.045
https://doi.org/10.1016/j.scienta.2016.0...
, 2017Adebayo SE, Hashim N, Abdan K, Hanafi M, Zude-Sasse M (2017) Prediction of banana quality attributes and ripeness classification using artificial neural network. Acta Horticulturae 1152(1152):335–344. DOI: https://doi.org/10.17660/ActaHortic.2017.1152.45
https://doi.org/10.17660/ActaHortic.2017...
; Ni et al., 2020Ni J, Gao J, Deng L, Han Z (2020) Monitoring the change process of banana freshness by GoogLeNet. IEEE Access 8:228369–228376. DOI: https://doi.org/10.1109/ACCESS.2020.3045394:228369
https://doi.org/10.1109/ACCESS.2020.3045...
). Thus, it ensures the productivity, competitiveness, and quality standards of small-scale farmers and firms (Mazen & Nashat, 2019Mazen FMA, Nashat AA (2019) Ripeness classification of bananas using an artificial neural network. Arabian Journal for Science and Engineering 44(8):6901–6910. DOI: https://doi.org/10.1007/s13369-018-03695-5
https://doi.org/10.1007/s13369-018-03695...
). Furthermore, recent developments in the prediction of bunch age have provided efficient fruit-detection systems in the natural environment (Fu et al., 2020Fu L, Duan J, Zou X, Lin J, Zhao L, Li J, Yang Z (2020) Fast and accurate detection of banana fruits in complex background orchards. IEEE Access 8:196835–196846. DOI: https://doi.org/10.1109/ACCESS.2020.3029215:196835
https://doi.org/10.1109/ACCESS.2020.3029...
).

Similarly, in the post-harvest stage, overcoming the shortcomings of manual operator approaches is considered the main goal. In this manner, some impacts are listed by the authors owing to the quality and size standards of bananas for automatic grading systems: reduction of the stress of manual operators (Olaniyi et al., 2017Olaniyi EO, Adekunle AA, Odekuoye T, Khashman A (2017) Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations. Journal of Food Process Engineering 40(6):1–10. DOI: https://doi.org/10.1111/jfpe.12575
https://doi.org/10.1111/jfpe.12575...
), effective prediction of the quality indices of bananas during shelf life (Sanaeifar et al., 2016Sanaeifar A, Bakhshipour A, De La Guardia M (2016) Prediction of banana quality indices from color features using support vector regression. Talanta 148:54–61. DOI: https://doi.org/10.1016/j.talanta.2015.10.073
https://doi.org/10.1016/j.talanta.2015.1...
; Ucat & Cruz, 2019Ucat RC, Cruz JCD (2019) Postharvest grading classification of cavendish banana using deep learning and Tensorflow. In: International Symposium on Multimedia and Communication Technology (ISMAC). Proceedings... DOI: https://doi.org/10.1109/ISMAC.2019.8836129
https://doi.org/10.1109/ISMAC.2019.88361...
), and reduction of postharvest losses (Le et al., 2019Le T-T, Lin C-Y, Piedad EJ (2019) Deep learning for noninvasive classification of clustered horticultural crops – A case for banana fruit tiers. Postharvest Biology and Technology 156. DOI: https://doi.org/10.1016/j.postharvbio.2019.05.023:110922
https://doi.org/10.1016/j.postharvbio.20...
) (Piedad et al., 2018Piedad EJ, Larada JI, Pojas GJ, Ferrer LVV (2018) Postharvest classification of banana (Musa acuminata) using tier-based machine learning. Postharvest Biology and Technology 145:93–100. DOI: https://doi.org/10.1016/j.postharvbio.2018.06.004
https://doi.org/10.1016/j.postharvbio.20...
). Therefore, DL algorithms and computer-vision techniques work together causing efficient fruit recognition. For example, they enhance the accuracy of the banana cultivar detection system (Dittakan et al., 2017Dittakan K, Theera-Ampornpunt N, Witthayarat W, Hinnoy S, Klaiwan S, Pratheep T (2017) Banana cultivar classification using scale invariant shape analysis. In: International Conference on Information Technology (INCIT). Proceedings... DOI: https://doi.org/10.1109/INCIT.2017.8257854
https://doi.org/10.1109/INCIT.2017.82578...
) and automate the recognition of multi-class fruits (shape and size) (Mureşan & Oltean, 2018Mureşan H, Oltean M (2018) Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica 10(1):26–42. DOI: https://doi.org/10.2478/ausi-2018-0002
https://doi.org/10.2478/ausi-2018-0002...
; Sugadev et al., 2020Sugadev M, Sucharitha K, Sheeba IR, Velan B (2020) Computer vision based automated billing system for fruit stores. In: 3rd International Conference on Smart Systems and Inventive Technology (ICSSIT). Proceedings. DOI: https://doi.org/10.1109/ICSSIT48917.2020.9214101
https://doi.org/10.1109/ICSSIT48917.2020...
; Xue et al., 2020Xue G, Liu S, Ma Y (2020) A hybrid deep learning-based fruit classification using attention model and convolution autoencoder. Complex and Intelligent Systems Oct 1-11. DOI: https://doi.org/10.1007/s40747-020-00192-x
https://doi.org/10.1007/s40747-020-00192...
). To forecast crop yield, the review findings indicated that it provides effective decision-making for the monitoring and estimation of harvests for farmers (Lima Neto et al., 2020de Lima Neto AJ, Deus JAL, Rodrigues Filho VA, Natale W, Parent LE (2020) Nutrient diagnosis of fertigated “prata” and “cavendish” banana (Musa spp.) at plot-scale. Plants 9(11):1467. DOI: https://doi.org/10.3390/plants9111467
https://doi.org/10.3390/plants9111467...
; de Souza et al., 2019de Souza AV, Bonini Neto A, Cabrera Piazentin J, Dainese Junior BJ, Perin Gomes E, Dos Santos Batista Bonini C, Ferrari Putti F (2019) Artificial neural network modelling in the prediction of bananas’ harvest. Scientia Horticulturae 257. DOI: https://doi.org/10.1016/j.scienta.2019.108724:108724
https://doi.org/10.1016/j.scienta.2019.1...
; Rebortera & Fajardo, 2019aRebortera M, Fajardo A (2019a) An enhanced deep learning approach in forecasting banana harvest yields. International Journal of Advanced Computer Science and Applications 10(9):275–280. DOI: https://doi.org/10.14569/IJACSA.2019.0100935
https://doi.org/10.14569/IJACSA.2019.010...
, 2019bRebortera M, Fajardo A (2019b) Forecasting banana harvest yields using deep learning. In: International Conference on System Engineering Technology (ICSET). Proceedings…p380-384. DOI: https://doi.org/10.1109/ICSEngT.2019.8906427
https://doi.org/10.1109/ICSEngT.2019.890...
) and assistance to policymakers to plan the future more efficiently (Rathod & Mishra, 2018Rathod S, Mishra GC (2018) Statistical models for forecasting mango and banana yield of Karnataka, India. Journal of Agricultural Science and Technology 20(4):803–816. Available: http://jast.modares.ac.ir/article-23-19768-en.html
http://jast.modares.ac.ir/article-23-197...
).

Finally, food processing and retail stages have reported significant impacts, such as optimizing electricity resources and time in the drying process of culinary bananas in order to increase quality of product (Guiné et al., 2015Guiné RPF, Barroca MJ, Gonçalves FJ, Alves M, Oliveira S, Mendes M (2015) Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments. Food Chemistry 168:454–459. DOI: https://doi.org/10.1016/j.foodchem.2014.07.094
https://doi.org/10.1016/j.foodchem.2014....
; Khawas et al., 2016Khawas P, Dash KK, Das AJ, Deka SC (2016) Modeling and optimization of the process parameters in vacuum drying of culinary banana (Musa ABB) slices by application of artificial neural network and genetic algorithm. Drying Technology 34(4):491–503. DOI: https://doi.org/10.1080/07373937.2015.1060605
https://doi.org/10.1080/07373937.2015.10...
), and predicting future agricultural productivity (Rehman et al., 2018Rehman A, Deyuan Z, Hussain I, Iqbal MS, Yang Y, Jingdong L (2018) Prediction of major agricultural fruits production in Pakistan by using an econometric analysis and machine learning technique. International Journal of Fruit Science 18(4):445–461. DOI: https://doi.org/10.1080/15538362.2018.1485536
https://doi.org/10.1080/15538362.2018.14...
).

In summary, the review findings confirm that AI-driven technologies support improvement in the challenges of the BSC. Thus, the use of ML and DL algorithms has a positive impact in the following aspects: economic (increase of production earnings, optimized operational costs, savings and optimization of resources, and accurate forecasting), agricultural management (enhanced agricultural productivity, optimized harvest yield, reduced post-harvest losses, good eating quality, effective ripening and fruit grading, efficient monitoring of resources like water and soil, automated process, and enhanced decision-making process), social (improved safety of fruit, increased customer satisfaction, business reputation, minimized stress from manual operations, and eased decision-making processes to farmers, managers, policymakers, etc.), and environmental (reduced environmental impacts such as optimizing the water footprint in pre-harvest, reduced food waste in post-harvest, minimized electricity and greenhouse gas emissions in food processing).

Future challenges

Possible future challenges and development directions are focused on the following aspects for sustainable AI-based models:

a) Improving the quality of data and computational resources:

Researchers must pay attention to technical challenges of data to enhance AI developments. It includes efforts to manage data security (generation, storage, data access, high-quality images, etc.), i.e., standardization of data collection from different sources. Additionally, to improve the learning process of the AI model, practitioners should have access to robust computational resources and Internet connectivity to easily and sustainably train and test ML and DL algorithms.

b) Performing real-time data analysis in natural environments:

Predictive models have been developed to solve real problems. Real-time data modeling is a trend in the field of AI. Thus, models can be trained by considering the real environmental conditions of crops (sunny and cloud conditions, or occlusion degree) preventing controlled environments or under-laboratory conditions.

c) Hybrid and transfer learning approaches to enhance the model:

To improve the efficiency of results, the hybrid approach is considered a trend to train the AI model. Thus, two or more learning algorithms can work together to improve the performance of the predictive model. Further, the advancements in transfer learning techniques allow a reduction in the workload of data, the model might train faster, with a small amount of data, and obtain better results. Particularly, DL models such as CNNs for image recognition are suitable for implementing transfer learning.

d) User-friendly predictive models:

Small-holder farmers are non-experts in ML and DL techniques. Therefore, researchers should develop easy-to-use and user-friendly applications of AI so that it could be incorporated in the daily routine of agricultural practices in a sustainable way. Moreover, the device interoperability should be considered too.

CONCLUSIONS

This study presented an overview of the recent developments in AI for bananas. Thus, this review described the application areas, highlighted ML and DL algorithms that have been implemented, listed the performance metrics that have been reported in literature, and explained the positive impacts declared by the researchers.

The review findings show that 11 AI-applications areas are available for BSC. According to the reviewed publications, the most active application areas for bananas are the following: ripeness, disease, crop type, quality grading, crop yield, and fruit recognition. Thus, publications belong to the stages at the beginning of the BSC (pre-harvest, harvest, and post-harvest), more than the latest stages (processing and retail).

The review identified several AI-based models that have used a wide range of ML and DL algorithms for regression, classification, and clustering tasks. Conventional and hybrid algorithms were used to train and test the models. Deep learning approaches have been widely used to resolve challenges facing the banana supply chain. Thus, transfer learning and data augmentation were used by researchers to improve the performance of their models.

Regarding the impacts on the BSC, the review findings confirmed that AI-driven technologies support improving the sustainability of the BSC. The reviewed publications have demonstrated a positive effect for BSC on the following aspects: economic, agricultural management, social, and environmental. Afterward, this research work presents the future challenges for AI in the global banana industry.

In that manner, this study highlights several points of consideration that could be useful for users of the BSC, such as small-holder farmers, producers, processors, and decision-makers. Additionally, this review article will help researchers, practitioners, and policymakers gain an understanding of AI applications for bananas.

Therefore, we are the first to present a comprehensive overview that explains the AI-based model for the global banana industry. This framework can be used to continue developing this research area and to expand the knowledge frontier in future.

ACKNOWLEDGMENTS

E. Almeyda would like to thank the Concytec and Universidad de Piura, Peru. The authors acknowledge financial support from the “Proyecto Concytec–Banco Mundial”, administer through by the executing unit ProCiencia [Contract N°06-2018-FONDECYT/BM]. The authors are also thankful to the Laboratorio de Sistemas Automáticos de Control at Universidad de Piura for providing facilities and logistics support in this research work.

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Edited by

Rouverson Pereira da Silva

Publication Dates

  • Publication in this collection
    01 Apr 2022
  • Date of issue
    2022

History

  • Received
    24 Aug 2021
  • Accepted
    28 Jan 2022
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