Abstract
Plant diseases pose a serious challenge to agriculture by reducing crop yield and affecting food quality. Early detection and classification of these diseases are essential for minimising losses and improving crop management practices. This study applies Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to classify plant leaf diseases using a dataset containing 70,295 training images and 17,572 validation images across 38 disease classes. The CNN model was trained using the Adam optimiser with a learning rate of 0.0001 and categorical cross-entropy as the loss function. After 10 training epochs, the model achieved a training accuracy of 99.1% and a validation accuracy of 96.4%. The LSTM model reached a validation accuracy of 93.43%. Performance was evaluated using precision, recall, F1-score, and confusion matrix, confirming the reliability of the CNN-based approach. The results suggest that deep learning models, particularly CNN, enable an effective solution for accurate and scalable plant disease classification, supporting practical applications in agricultural monitoring.
References
Z. Saeed, A. Raza, A. H. Qureshi and M. Haroon Yousaf, "A Multi-Crop Disease Detection and Classification Approach using CNN," 2021 International Conference on Robotics and Automation in Industry (ICRAI), Rawalpindi, Pakistan, 2021, 1-6, doi: 10.1109/ICRAI54018.2021.9651409.
A. Kaur, V. Kukreja, M. Kumar, A. Choudhary and R. Sharma, "Multiclass Classification of Tomato Leaf Diseases using a Hybrid of LSTM and CNN Model," 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), Pune, India, 2024, 1-5, doi: 10.1109/I2CT61223.2024.10544287.
A. Shrotriya, A. Kumar Sharma, S. Prabhu and A. Kumar Bairwa, "An Approach Toward Classifying Plant-Leaf Diseases and Comparisons With the Conventional Classification," in IEEE Access, vol. 12, 117379-117398, 2024, doi: 10.1109/ACCESS.2024.3411013.
E. Moupojou, F. Retraint, H. Tapamo, M. Nkenlifack, C. Kacfah and A. Tagne, "Segment Anything Model and Fully Convolutional Data Description for Plant Multi-Disease Detection on Field Images," in IEEE Access, vol. 12, 102592-102605, 2024, doi: 10.1109/ACCESS.2024.3433495.
M. S. A. M. Al-Gaashani, R. Alkanhel, M. A. S. Ali, M. S. A. Muthanna, A. Aziz and A. Muthanna, "MSCPNet: A Multi-Scale Convolutional Pooling Network for Maize Disease Classification," in IEEE Access, vol. 13, 2025, 11423-11446, doi: 10.1109/ACCESS.2024.3524729.
A. Bhargava, A. Shukla, O. P. Goswami, M. H. Alsharif, P. Uthansakul and M. Uthansakul, "Plant Leaf Disease Detection, Classification, and Diagnosis Using Computer Vision and Artificial Intelligence: A Review," in IEEE Access, vol. 12, 37443-37469, 2024, doi: 10.1109/ACCESS.2024.3373001.
Liu, L., Qiao, S., Chang, J., Ding, W., Xu, C., Gu, J., Sun, T., & Qiao, H. (2024b). A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images. Heliyon, 10(7), e28264. https://doi.org/10.1016/j.heliyon.2024.e28264
Algani, Y. M. A., Caro, O. J. M., Bravo, L. M. R., Kaur, C., Ansari, M. S. A., & Bala, B. K. (2022). Leaf disease identification and classification using optimized deep learning. Measurement Sensors, 25, 100643. https://doi.org/10.1016/j.measen.2022.100643
Li, Lili, Shujuan Zhang, and Bin Wang. "Plant disease detection and classification by deep learning—a review." IEEE Access 9 (2021): 56683-56698.
Pacal, Ishak, Ismail Kunduracioglu, Mehmet Hakki Alma, Muhammet Deveci, Seifedine Kadry, Jan Nedoma, Vlastimil Slany, and Radek Martinek. "A systematic review of deep learning techniques for plant diseases." Artificial Intelligence Review 57, no. 11 (2024): 304.
Ametefe, D. S., Sarnin, S. S., Ali, D. M., Caliskan, A., Caliskan, I. T., Aliu, A. A., & John, D. (2024). Enhancing leaf disease detection accuracy through synergistic integration of deep transfer learning and multimodal techniques. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2024.09.006
K. M. Hosny, W. M. El-Hady, F. M. Samy, E. Vrochidou and G. A. Papakostas, "Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern," in IEEE Access, vol. 11, pp. 62307-62317, 2023, doi: 10.1109/ACCESS.2023.3286730.
Saleem, S., Sharif, M. I., Sharif, M. I., Sajid, M. Z., & Marinello, F. (2024). Comparison of Deep Learning Models for Multi-Crop Leaf Disease Detection with Enhanced Vegetative Feature Isolation and Definition of a New Hybrid Architecture. Agronomy, 14(10), 2230. https://doi.org/10.3390/agronomy14102230
Paymode, A. S., & Malode, V. B. (2022c). Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG. Artificial Intelligence in Agriculture, 6, 23–33. https://doi.org/10.1016/j.aiia.2021.12.002
S. Kashyap, T. Thaware, S. R. Sahu and M. R. K, "Multi-Crop Leaf Disease Detection using Deep Learning Methods," 2022 IEEE 19th India Council International Conference (INDICON), Kochi, India, 2022, 1-6, doi: 10.1109/INDICON56171.2022.10040099.