Abstract in English:ABSTRACT The seed germination and vigor evaluation are essential for the sowing sector to measure the performance of different seed lots and improve the efficiency of storage and sowing processes. However, the analysis of various tests to determine seed quality generates a large amount of information, making it almost impossible for humans to perform a quick and effective quality control analysis. Therefore, the objective of this study was to evaluate the differences in the physiological quality of soybean seeds in different cultivars using machine learning techniques to rank the lots based on their quality. Three cultivars were used, and the analysis was germination, accelerated aging, tetrazolium treatment, seedling emergence, and 1000 seed weight from 65 lots were measured. The lots were evaluated in two phases, one immediately after harvest and the other after six months of storage. Random forest, multi-layer perceptron, J48, and classification via regression classifiers were used, aided by the feature resampler technique. Random forest and classification via regression obtained the highest accuracy, and the random forest technique obtained the best results. Therefore, it is possible to classify soybean seed lots with great accuracy and precision using artificial intelligence and machine learning techniques.
Abstract in English:ABSTRACT This study is dedicated to the development of a methodology based on supervised machine learning for soybean classification and justified as technological innovation to predict whether soybean classification is in the standard or non-standard established by normative instruction No. 11/2007 of the Ministry of Agriculture, Livestock, and Food Supply (MAPA). This study aimed to develop a website using supervised machine learning to classify soybeans, providing an assertive decision-making process in real-time. A technological tool was created to assist the farmer and the storage unit in the classification of soybeans, considering the perceived reality and potential instruments consistent with the reality of the area. Therefore, a website in Python language was created using the Pandas, Pandas Profiling, Seaborn, Matplotlib, NumPy, Scikit-learn, PyCaret, and Streamlit libraries. In the end, the system could predict whether the soybean is in the standard or non-standard established by the soybean classification normative. In this sense, the results showed the robustness and precision of the proposed new methodology.
Abstract in English:ABSTRACT Leaf chemical analysis is one of the ways to assess plant development. However, this type of assessment is expensive and time-consuming. The variation of nutrient content in the leaves modifies the proportion of light reflected and absorbed by plants at different wavelengths. Being able to relate the color reflected by the leaves with their phosphorus (P) content and using this data as input into an artificial neural network (ANN) can be an alternative for its determination. For this, it is necessary to establish which colors are most correlated with the different nutrients. Therefore, the phosphorus content in tomato leaves was evaluated in this study, according to four treatments (0.25, 50, 75, and 100% of the P doses). Different vegetation indices were also evaluated using images of mini-tomato leaves through a principal component analysis to determine which ones would be suitable to serve as an input to an ANN (multilayer perceptron). DGCI (Dark Green Color Index) and Bn (Normalized Blue) were the indices most related to P content. The neural network obtained 90% accuracy in the classification after training using both sides of the leaves.
Abstract in English:ABSTRACT This paper aimed to develop predictive models to determine total soluble solids, firmness, and ripening stages of ‘Pacovan’ bananas, using Vis-NIR spectroscopy and machine learning algorithms. A total of 384 bananas were divided into different days of storage (0, 3, 6, 9, 12, 15, 18, and 21 days) at two temperatures (25°C and 20°C). Bananas were subjected to spectral analysis using a spectrometer operating in spectral range of 350 – 2500 nm. Physicochemical parameters of quality, total soluble solids, and firmness were determined by reference analyses. Different machine learning algorithms were used to develop regression models and supervised classification. The best model for total soluble solids was the Random Forest with variable selection, showing an R2cv of 0.90 and RMSECV of 2.31. The best model for firmness was the Support Vector Machine with variable selection, showing an R2cv of 0.84 and RMSECV of 7.98. The best classification model for different ripening stages was the Multilayer Perceptron with variable selection, which achieved the precision of 74.22%. Therefore, Vis-NIR spectroscopy associated with machine learning algorithms is a promising tool for monitoring the quality and ripening stages of ‘Pacovan’ bananas.
Abstract in English:ABSTRACT Due to the increase in water and energy tariffs, in addition to the limited amount of these resources, the automation of irrigation can help farmers to increase the production of agricultural crops. Therefore, the objective of this research was to evaluate different irrigation managements between manual and automatic in the production of arugula in a protected environment, in order to determine the productive potential in the cultivation of vegetables. The experiment was conducted in randomized blocks with four irrigation management strategies, divided into automatic and manual managements: automatic irrigation management via soil (IAS); automatic irrigation management via climate (IAC); manual irrigation management via soil (IMS) and manual irrigation management via climate (IMC). The treatments were applied to the arugula ( Eruca Sativa L. ) crop during two production cycles, and their effect on biophysical aspects of plants and irrigation water productivity was evaluated. For the fresh mass variable, the IAC (17.75 g plant-1), IAS (12.38 g plant-1), and IMC (8.63 g plant-1) treatments, in the 1st cycle, were statistically similar to each other, whereas in the 2nd cycle, only the IAC (16.29 g plant-1) and IAS (19.80 g plant-1) treatments had this statistical similarity. Automatic managements can be recommended based on this research, however, considering the financial difficulties of the small farmer, IMC may be a desirable option in unfavorable economic conditions.
Abstract in English:ABSTRACT Locomotor problems are a challenge for commercial poultry, but current methods used to assess the bone structure of chickens are few and laborious. The objective of this study is to present software for the automatic extraction of morphometric characteristics of broiler chicken’s locomotor bones throughout the life cycle, by applying computer vision techniques. 112 samples from the tibia and 112 from the femur of commercial chickens were used, subdivided by age (0, 7, 14, 21, 28, 35, and 42 days). The images were digitally processed to extract bone morphometric properties (area, length, and perimeter). New software was created, including the proposed processing and algorithms for obtaining the morphometric characteristics. Classification models (artificial neural networks, ANN, and k-nearest neighbors’ algorithm, KNN) were developed to classify bones according to age and type. The results of the software were satisfactory, the sample bank could be handled correctly, a high applicability to test images from other sources was determined. For the classification of bones, the ANN method was more accurate than KNN. The information obtained in this study opens new possibilities for evaluative studies of broiler locomotive systems.
Abstract in English:ABSTRACT Bean is among the most consumed and produced crops in Brazil. Given the high demand for food, the search for technologies and controllers to increase the efficiency of agricultural systems has grown. This study aimed to model artificial neural network (ANN) architectures to predict mechanical efficiencies in the semi-mechanized bean harvest. We used a multilayer perceptron network with three inputs (harvest moisture, threshing rotor rotation, and feed rate), two hidden layers of neurons, and one output (efficiency). We evaluated the efficiency in the header, separation on the threshing rotor, cleaning of sieves, and the total efficiency of the machine. ANN was processed by a scripted algorithm to model the network, alternate the number of neurons in hidden layers, as well as to select, test, and validate ANN with less error. ANN was validated by comparing its results with the experimental data. The architectures selected to predict efficiencies were 3-8-15-1 for the header, 3-9-7-1 for the thresher and separation, 3-5-11-1 for cleaning, and 3-15-10-1 for the total operation. ANN predicted satisfactory results with errors below 1% and a high hit rate, thus being valid to predict the efficiencies in the semi-mechanized bean harvest.
Abstract in English:ABSTRACT The use of wood is widespread in rural constructions, and the truss systems stand out among its various applications. This specific system has several typologies and requires a thorough study to determine the most advantageous model for each project. The present study aims to apply the Computational Intelligence concepts to determine the minimum viable cross-section of a Howe truss. For the computational simulation, methods like Finite Elements were used to obtain the loads and the Firefly Algorithm for the optimization process, focusing on minimizing the total weight of the structural part. Studies were conducted varying the spans of the elements and the height to span ratio. The design assumptions for establishing the optimization method’s constraints follow the recommendations of the Brazilian standard for wood design, ABNT NBR 7190 (1997). Weights between 95.42 kg and 653.57 kg were obtained, and all optimization processes presented feasible solutions for the design constraints.
Abstract in English:ABSTRACT The analysis of water quality for irrigation assists in solving problems with irrigation equipment, such as obstruction in localized systems, being fundamental in precision irrigation. This study aimed to develop and evaluate an affordable multiparametric probe, as well as the performance in the remote data transmission by Bluetooth classic and Wi-Fi. The probe was based on the Arduino Nano platform. The sensors consisted of a pH (potential of hydrogen) sensor, a turbidity sensor (TSW30), and a total dissolved solids sensor. Bluetooth classic (HC-06 module) and ESP8266 module (ESP-01) were implemented for wireless transmission. A fuzzy inference system was used to evaluate the performance of sending data, using the variables bit error rate (BER) and percentage efficiency (Ef). The low-cost multiparametric probe allowed the measurement of pH, turbidity, and total dissolved solids. The Wi-Fi standard (IEEE 802.11 g/b/n), via ESP8266 version 01, presented the best results of consistency and efficiency of information transmission, according to fuzzy modeling.
Abstract in English:ABSTRACT Eucalyptus (Eucalyptus urograndis) production has significantly advanced over the past few years in Brazil, especially with regard to acreage and productivity. Machine learning has made significant advances in most varied fields of agrarian sciences. In this context, this study aimed to use physicochemical variables of the soil as well as climatic and dendrometric variables of eucalyptus to predict its height using the random forest algorithm. The study was conducted in the municipality of Três Lagoas, in Mato Grosso do Sul, Brazil. The original database consisted of 49 soil physicochemical variables collected at 0–0.20 m and 0.20–0.40 m, two dendrometric and four climatic variables, and one response variable related to the height of eucalyptus. A correlation matrix was applied to select variables. Furthermore, modeling was performed using the random forest algorithm, which performed well (r = 0.98, R2 = 0.96) in predicting the height of eucalyptus. Overall, the most important variables to predict the eucalyptus plant height included diameter at breast height (DBH), phosphorus content (P1), gravimetric moisture (GM1) at a soil depth between 0.00 m and 0.20 m, and exchangeable aluminum content (Al2) between 0.20 m to 0.40 m of soil.
Abstract in English:ABSTRACT The cultivation of soy and cotton is of great importance in the Brazilian economic scenario, both of which move billions of reais per year in exports. Weed management is important for obtaining optimal yields. Among the plants that have gained resistance and tolerance are those of the genus Ipomoea spp. These plants affect soybean and cotton crops throughout their cycle, thereby affecting their productivity. In this context, the objective of this work was to develop an embedded system for the selective spraying of rope and viola in cotton and soybean crops using algorithms for the classification and detection of objects in real time (Faster R-CNN and YOLOv3). This project was developed at the Agricultural Machinery Laboratory of the Federal University of Rondonópolis. The algorithms were trained to detect three classes (soybean, viola, and cotton) and were evaluated in terms of precision and sensitivity in the laboratory and field. Control results using faster R-CNN sprays demonstrated that real-time object detection algorithms for the selective control of weeds can be used for soybean and cotton crops.
Abstract in English:ABSTRACT Optimal solutions derived from linear programming models depend entirely on input parameters, which may present some imprecision because they come from estimates. Fuzzy linear programming allows the incorporation of these uncertainties in linear models, which can include the flexibility of resources, costs, goals, and constraints. This paper aimed to show new optimal solutions for a model to minimize the equivalent annual cost of micro-irrigation systems on sloping terrains. The Zimmermann-Werner fuzzy linear programming method, whose objective function is diffuse due to the restrictions of the hydraulic network being dispersed, was used. Sixty models were created and all solutions were satisfactory, with an annual cost of the irrigation system lower than the original model. The lowest value was US$ 238.74 ha-1, which occurred on the 3% slope. A reduction was observed in the annual cost due to the increased use of pipes with a 50-mm nominal diameter in the secondary line. Thus, fuzzy linear programming provided better solutions with small modifications to the irrigation system, while maintaining all hydraulic network requirements for proper system operation.
Abstract in English:ABSTRACT The growing demand for clean energy aimed at reducing greenhouse gas emissions associated with the oil crises has encouraged the search for biofuels, among which biodiesel has stood out in the gradual replacement of diesel. This study aimed to evaluate the performance of an agricultural tractor fueled with four types of biodiesels (peanut, sunflower, soybean, and waste frying oil) added to diesel at five proportions (0, 25, 50, 75, and 100% biodiesel, that is, B0, B25, B50, B75, and B100, respectively). The experiment was carried out at the Laboratory of Biofuel and Machinery Testing at FCAV–UNESP. A Valtra BM100 4×2 FWD tractor with a power of 74 kW (100 hp) was used. The drawbar pull force (DF), displacement velocity (V), drawbar power (DP), volumetric fuel consumption (VC), weight fuel consumption (WC), and specific fuel consumption (SC) were studied. The factors did not influence DF, V, and DP. The proportion factor influenced (p<0.01) the volumetric fuel consumption, in which diesel S50 was 14% more efficient than B100. Weight fuel consumption was influenced by the type of biodiesel in the blend. Diesel had the lowest specific fuel consumption (328 g kW h−1). The biodiesel fraction showed a direct relationship with the consumption parameters, with sunflower showing the lowest WC value in the B75 and B100 blends.
Abstract in English:ABSTRACT This study evaluated the electrical performance of a photovoltaic-thermal (PVT) system using water as a cooling fluid (PVT/w), with adaptation, in a photovoltaic module of a device for heating water without direct contact with the cell and with air as the secondary working fluid. The PVT/w system with forced and natural circulation was compared in a regime of thermal accumulation of hot water and supply by a boiler reservoir relative to the same PV panel with the original factory characteristics. The average system temperature, open circuit voltage, current and voltage with load, and generated electric power were analyzed during seven non-consecutive days, with ten repetitions every thirty minutes between 9:30 am and 2:00 pm in the city of Dourados-MS, Brazil, between June and July 2021. The PVT system with forced circulation (PVT/w_CB) presented the best electrical performance compared to the PVT system with natural circulation (PVT/w_SB), in the order of 3.7%.
Abstract in English:ABSTRACT New, efficient, low-cost techniques for image processing and alternative machine learning for seed processing are of academic and industrial interest. This study aims to identify fissures in bark and peeled rice seeds using X-ray and RGB image processing techniques and machine learning. Samples of three batches of rice seeds were used: a batch of seeds not subjected to drying (peeled seed), and the other two comprised of dried seeds, one containing seeds with husk and another containing huskless seeds; each sample comprised 100 seeds. Images in X-ray and RGB formats were provided in the sequence processed in ImageJ software and introduced in the machine learning software, where they were pre-processed using the appropriate filters and then classified by the J48 and linear discriminant analysis (LDA) classifiers. X-ray images obtained using differentiated equipment allow the identification of cracks in rice seeds using image processing techniques and the LDA classifier. Capturing images using RGB is a viable alternative. Using filters, either individually or in combination, may constitute an adequate alternative for rice seed classification.
Abstract in English:ABSTRACT The physicochemical properties of Jatropha curcas (JC) seed oils are related to the plant varieties and affect the biodiesel quality when it is used as feedstock. This work investigates the physicochemical properties and feasibility of mutated JC seed oil for biodiesel feedstock. Three mutated JC seed oils, from JC-150, JC-226 and JC-300, were successfully evaluated. The oil contents were determined by using gravimetry methods. The AV, FFA, IV and PV were determined by using titrimetric methods. Types of fatty acids were analyzed by using a GC-FID. The triacylglycerol (TAG) and PE compositions were determined by using a HPLC-ELSD. The results show that the oil contents of JC-150, JC-226 and JC-300 seeds were 48.3%, 45.8%, and 51.7%, respectively. The PE contents in JC-150, JC-226 and JC-300 were lower (approximately 33.4%, 46.9% and 96.4%) compared to the control. The oleic and linoleic acids were two main components of all samples, with compositions in the range 41.82-42.45% and 36.68-37.45%, respectively. The compositions of polyunsaturated and monounsaturated TAG were obtained in the range 71.60–76.22% and 19.62–24.53%, respectively. These results show that the properties of mutated JC seed oils meet with the requirements for biodiesel production.
Abstract in English: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.
Abstract in English:ABSTRACT A literature review on artificial intelligence in irrigation management was performed, using the Systematic Literature Review (SLR) method with explicit search criteria. More than 45,000 complete titles in 130 reference bases were consulted at once. A total of 38 primary studies were selected, which formed the basis of this review. The findings showed increasing use of Artificial Neural Networks (ANN) fed with climate and soil sensor data for irrigation management solutions. ANNs have been the most popular choice for solutions that require machine learning techniques. Fuzzy-logic-based technologies stood out in Decision Support Systems (SSD). Hybrid neuro-fuzzy approaches manage the best aspects contained in each of the two techniques (ANN and fuzzy logic). Moreover, autonomous wireless and networked sensors have been the most often used. Good chances of developing solutions for irrigation management point to the growing application of ANN-based machine learning, Support Vector Machine (SVM), and Random Forests techniques, using wireless sensor networks and computer vision with remote sensing images.
Abstract in English:ABSTRACT This paper describes the modeling, implementation, and evaluation of a control system based on an embedded fuzzy controller for application in irrigation systems. The motivation for the development of this system comes from the need to offer farmers resources that provide a reduction in water and electricity consumption in irrigation, which contributes to reducing production costs and preserving natural resources. The proposed control system aims to keep the water flow constant, as close as possible to the desired value and independent of load variations. A single-board computer, with the fuzzy controller software, a frequency inverter to control the speed of the irrigation motor pump system, and a flow sensor to measure the water flow, was used to implement the system. The proposed control system was evaluated in laboratory and field experiments to simulate real operating conditions. The results showed that the system presents satisfactory performance, representing a viable alternative for application in general irrigation systems.