Kawasaki et al., 2015Kawasaki, Y., Uga, H., Kagiwada, S., & Iyatomi, H. (2015). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. InAdvances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part II 11(pp. 638-645). Springer International Publishing.
|
Cucumber |
Own |
RGB- 800 images, Varied sizes; Resized (224 × 224px) |
Custom CNN |
94.90 |
Fujita et al., 2016Fujita, E., Kawasaki, Y., Uga, H., Kagiwada, S., & Iyatomi, H. (2016, December). Basic investigation on a robust and practical plant diagnostic system. In2016 15th IEEE international conference on machine learning and applications (ICMLA)(pp. 989-992). IEEE.
|
Cucumber |
Own |
RGB- 7,520 images; Resized (224 × 224px) |
CNN1 and CNN2 |
83.20 |
DeChant et al., 2017DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E. L., Yosinski, J., Gore, M. A., ... & Lipson, H. (2017). Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning.Phytopathology,107(11), 1426-1432.
|
Maize |
Own |
RGB - 1,834 images; Varied sizes; Resized (224 × 224 px) |
Pipeline of CNN |
96.70 |
Ramcharan et al., 2017Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., & Hughes, D. P. (2017). Deep learning for image-based cassava disease detection.Frontiers in plant science , 8, 1852.
|
Cassava |
Own |
RGB - 11,670 images; Varied sizes |
Inception V3 |
93.00 |
Picon et al., 2019Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild.Computers and Electronics in Agriculture ,161, 280-290.
|
Wheat |
Own |
RGB- 8,178 images; Resized (224 × 224px) |
ResNet50 |
87.00 |
Selvaraj et al., 2019Selvaraj, M. G., Vergara, A., Ruiz, H., Safari, N., Elayabalan, S., Ocimati, W., & Blomme, G. (2019). AI-powered banana diseases and pest detection.Plant methods,15, 1-11.
|
Banana |
Own |
RGB - 700 images |
ResNet, Inception V2, MobileNetV1 |
90.00 |
Hu et al., 2019Hu, G., Wu, H., Zhang, Y., & Wan, M. (2019). A low shot learning method for tea leaf’s disease identification.Computers and Electronics in Agriculture ,163, 104852.
|
Tea leaf |
Own |
RGB- 120 images |
C-DCGAN |
90.00 |
Ma et al., 2018Ma, J., Du, K., Zheng, F., Zhang, L., Gong, Z., & Sun, Z. (2018). A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network.Computers and Electronics in Agriculture ,154, 18-24.
|
Cucumber |
Own |
RGB-, 14,208 images, Size: 2592 × 1944px; Resized (800 × 600px) |
DCNN |
93.40 |
Zhang et al., 2019Zhang, S., Zhang, S., Zhang, C., Wang, X., & Shi, Y. (2019). Cucumber leaf disease identification with global pooling dilated convolutional neural network.Computers and Electronics in Agriculture ,162, 422-430.
|
Cucumber |
Own |
RGB- 800 images, Size: 2456 × 2058px; Resized (240 × 240px) |
GPDCNN, combined with the AlexNet model |
95.18 |
Saberi Anari, 2022Saberi Anari, M. (2022). A hybrid model for leaf diseases classification based on the modified deep transfer learning and ensemble approach for agricultural aiot-based monitoring.Computational Intelligence and Neuroscience,2022.
|
Apple, corn, cotton, grape, pepper, and rice |
PVD1+ UCI2
|
RGB- 90,000 images; Varied sizes; Resized (416 × 416px) |
Transfer learning and ensemble learning |
99.10 |
Chen et al., 2022Chen, R., Qi, H., Liang, Y., & Yang, M. (2022). Identification of plant leaf diseases by deep learning based on channel attention and channel pruning.Frontiers in plant science ,13, 1023515.
|
Peanuts, potatoes, apples, and 15 other crops |
PVD + Own |
RGB - 6033 images; Varied sizes; Resized (224×224px) RGB -54634 images; Varied sizes; Resized (224×224px) |
CACPNET (channel attention and channel pruning) |
99.70 |
Afifi et al., 2020Afifi, A., Alhumam, A., & Abdelwahab, A. (2020). Convolutional neural network for automatic identification of plant diseases with limited data.Plants,10(1), 28.
|
Coffee leaf |
Own + PVD |
- RGB- 54,305 images; Varied sizes |
ResNet18, ResNet34, and ResNet50 |
99.00 |
Sharif et al., 2018Sharif, M., Khan, M. A., Iqbal, Z., Azam, M. F., Lali, M. I. U., & Javed, M. Y. (2018). Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection.Computers and Electronics in Agriculture ,150, 220-234.
|
Citrus |
Own + PVD |
RGB, 580 images; Varied sizes RGB - 1000 images -Size: 100x150px |
Hybrid method, scoring PCA, entropy and covariance vector given M-SVM |
97.0 -PVD, 89-PVD + Own, 90.4- Own |
Lu et al., 2017Lu, J., Hu, J., Zhao, G., Mei, F., & Zhang, C. (2017). An in-field automatic wheat disease diagnosis system.Computers and Electronics in Agriculture ,142, 369-379.
|
Rice |
PVD |
RGB- 9,230 images; Varied sizes; Resized (224 × 224px) |
Custom CNN |
95.48 |
Singh et al., 2022Singh, A. K., Sreenivasu, S. V. N., Mahalaxmi, U. S. B. K., Sharma, H., Patil, D. D., & Asenso, E. (2022). Hybrid feature-based disease detection in plant leaf using convolutional neural network, bayesian optimized SVM, and random forest classifier.Journal of Food Quality ,2022, 1-16.
|
Apple, corn, potato, tomato, and rice plants |
PVD |
RGB- 37,315 images; Varied sizes |
LeNet, ShuffleNet, AlexNet, EffNet, and MobileNet, |
96.10 |
Sladojevic et al., 2016Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks-based recognition of plant diseases by leaf image classification.Computational Intelligence and Neuroscience ,2016.
|
13 crop species |
Websites |
RGB- 30880- training and 2589-validation; Resized (256 × 256px) |
CaffeNet |
96.30 |
Mohanty et al., 2016Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection.Frontiers in plant science , 7, 1419.
|
14 crop species |
PVD |
RGB - 54,306 images; Varied sizes; Resized (256 × 256px) |
AlexNet and GoogleNet |
99.34 |
Brahimi et al., 2017Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep learning for tomato diseases: classification and symptoms visualization.Applied Artificial Intelligence,31(4), 299-315.
|
Tomato |
PVD |
RGB- 14,828 images; Varied sizes; Resized (256 × 256px) |
AlexNet and GoogleNet |
99.18 |
Amara et el., 2017Amara, J., Bouaziz, B., & Algergawy, A. (2017). A deep learning-based approach for banana leaf diseases classification.Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband.
|
Banana |
PVD |
RGB- 3700 images; Resized (60 x 60px) |
LeNet |
99.72 |
Too et al., 2019Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification.Computers and Electronics in Agriculture ,161, 272-279.
|
14 crop species |
PVD |
RGB- 54,306 images; Varied sizes; Resized (224×224px) |
ResNet with 50, 101, 152 Layers, VGG16, DenseNet and InceptionV4 |
99.75 |
Ferentinos, 2018Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis.Computers and Electronics in Agriculture ,145, 311-318.
|
25 crop species |
PVD |
RGB- 87,848 images; Varied sizes; Resized (256 × 256px) |
AlexNet, AlexNetOWTBn, GoogLeNet, Overfeat, VGG |
99.53 |
Abdu et al., 2020Abdu, A. M., Mokji, M. M., & Sheikh, U. U. (2020). Automatic vegetable disease identification approach using individual lesion features.Computers and Electronics in Agriculture,176, 105660.
|
Potato |
PVD |
RGB- 1,400 images; Varied sizes; Resized (256 × 256px) |
Proposed Algorithm |
99.0 |
Karthik et al., 2020Karthik, R., Hariharan, M., Anand, S., Mathikshara, P., Johnson, A., & Menaka, R. (2020). Attention embedded residual CNN for disease detection in tomato leaves.Applied Soft Computing,86, 105933.
|
Tomato |
PVD |
RGB- 95999 training images and 24001; validation images; Varied sizes |
CNN |
98.0 |
Khan et al., 2018Khan, M. A., Akram, T., Sharif, M., Awais, M., Javed, K., Ali, H., & Saba, T. (2018). CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features.Computers and Electronics in Agriculture ,155, 220-236.
|
Citrus |
CASC-IFW3
|
RGB - (Exp 1: 2688 images; Exp 2: 2679 images; Exp 3, 4, 5, and 6: 1200 images); Varied sizes |
VGG16, Caffe AlexNet |
98.6 |
Kamal et al., 2019Kamal, K. C., Yin, Z., Wu, M., & Wu, Z. (2019). Depthwise separable convolution architectures for plant disease classification.Computers and Electronics in Agriculture ,165, 104948.
|
55 crop species |
PVD |
RGB- 82,161 images; Varied sizes; Resized (224 x224) and (150x150) |
Modified MobileNet and Reduced MobileNet |
98.34 |