A Deep Learning Approach for Real-Time Detection and Classification of Crop Leaf Diseases to Support Sustainable Farming Practices
Abstract
Early and correct diagnosis of the crop leaf diseases is essential to guarantee agricultural output, reduce yield damages and to sustain agriculture. Traditional diagnostic techniques or disease diagnostics requiring manual examination are manual, subjective and cannot be applied in large-scale or real-time agricultural monitoring. Despite the recent progress in the field of deep learning, which has shown that high classification rates can be achieved with the help of deep learning in the field of plant disease identification, most of the present methods are only able to perform image-level classification, are not able to localize the disease, and cannot be deployed in real-time in the field. This study introduces a deep learning system of real-time detection and classification of crop leaf diseases that combines effective object detection and disease classification in a single system. The strategy proposed uses a one-stage detection model and an optimized convolutional backbone, data augmentation, and transfer learning to balance the accuracy, robustness, and computational efficiency with the proposed strategy. With standard performance metrics and real-time inference analysis the framework is tested on a curated dataset of about 6,500 samples of crop leaf images of five representative classes including healthy and diseased ones. Experimental data indicate good and consistent performance in terms of disease-wise and a false positive rate of 95.6 and F1-score of 95.4 respectively. The normalized confusion which is depicted in the normalized confusion matrix is highly dominant on the diagonal meaning that there is no inter-class confusion and the sensitivity is certain in all the categories of the disease. The presence of the correct localization of the symptomatic area of the leaves in various visual conditions with the help of qualitative detection is proved. The unified detection classification design proves to be effective as verified by comparative and ablation studies, and real-time assessment demonstrates an inference rate of 26.3 FPS, which is appropriate to be used in edge-based and in-field implementation. All in all the proposed framework will help in closing the gap between laboratory models of high accuracy and deployable real-time agricultural solutions. The method allows localizing the disease and diagnosing it within a short time, which contributes to the development of sustainable and precision farming systems, facilitates early intervention, specific treatment, minimizes the use of chemicals, and enhances crop management.
Keywords:
Crop Leaf Disease Detection, Deep Learning, Plant Disease Classification, Precision Agriculture, Real-Time Object Detection, Sustainable Farming
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References
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Boulent, J., Foucher, S., Théau, J., & St-Charles, P. L. (2019). Convolutional neural networks for the automatic identification of plant diseases. Frontiers in Plant Science, 10, 941. https://doi.org/10.3389/fpls.2019.00941
Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299-315. https://doi.org/10.1080/08839514.2017.1315516
Chen, J., Chen, J., Zhang, D., Sun, Y., &Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393. https://doi.org/10.1016/j.compag.2020.105393
Chouhan, S. S., Kaul, A., Singh, U. P., & Jain, S. (2018). Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. Ieee Access, 6, 8852-8863. https://doi.org/10.1109/ACCESS.2018.2800685
Chouhan, S. S., Singh, U. P., & Jain, S. (2020). Applications of Computer vision in plant pathology: A survey. Archives of Computational Methods in Engineering, 27, 611-632.https://doi.org/10.1007/s11831-019-09324-0
Dangi, V., Goswami, C., & Chakrabarti, P. (2025). Developing a conceptual framework for soil property analysis and crop yield prediction using machine learning techniques. International Journal of Innovative Technology and Interdisciplinary Sciences, 8(3), 513-536. https://doi.org/10.15157/IJITIS.2025.8.3.513-536
Devi, A. G., Begum, S. S., Kocharla, S., Madhavi, P., Gorikapudi, S., &Tirumalasetti, N. R. (2026). Multi-Task Deep Learning Framework for Segmentation and Severity Estimation of Leaf Diseases in Multi-Crop Environments. International Journal of Innovative Technology and Interdisciplinary Sciences, 9(1), 210-237. https://doi.org/10.15157/ijitis.2026.9.1.210-237
El-Behery, H., Attia, A. F., & Rezk, N. G. (2026). An explainable deep learning framework for few shot crop disease detection in rice and sugarcane using CNN based feature extraction. Scientific Reports, 16(1), 8272. https://doi.org/10.1038/s41598-026-37501-2
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311-318. https://doi.org/10.1016/j.compag.2018.01.009
Fuentes, A., Yoon, S., Kim, S. C., & Park, D. S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17(9), 2022. https://doi.org/10.3390/s17092022
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. https://doi.org/10.48550/arXiv.1704.04861
Howard, A., Sandler, M., Chu, G., Chen, L. C., Chen, B., Tan, M.,…… & Adam, H. (2019). Searching for MobileNetV3. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 1314–1324. https://doi.org/10.1109/ICCV.2019.00140
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., ... &Kalenichenko, D. (2018). Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2704-2713. https://doi.org/10.1109/CVPR.2018.00286
Kamilaris, A., &Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016
Lakshmi, T. S., Ayyappa, Y., Aruna, V., Rani, D. R., Mary Kamala Kumari, P., & Phani Praveen, S. (2024). Advanced medicinal plant recognition with convolutional neural networks. International Journal of Advancement in Life Sciences Research, 7(4), 87-97. https://doi.org/10.31632/ijalsr.2024.v07i04.008
Li, Y., Nie, J., & Chao, X. (2020). Do we really need deep CNN for plant diseases identification? Computers and Electronics in Agriculture, 178, 105803. https://doi.org/10.1016/j.compag.2020.105803
Lu, 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. https://doi.org/10.1016/j.compag.2017.09.012
Ma, 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. https://doi.org/10.1016/j.compag.2018.08.048
Mandava, R., & Sravanthi, G. L. (2026). Quantum Machine Learning Algorithms for Optimizing Complex Data Classification Tasks. Journal of Transactions in Systems Engineering, 4(1), 538-559. https://doi.org/10.15157/JTSE.2026.4.1.538-559
Mehdipour, S., Mirroshandel, S. A., & Tabatabaei, S. A. (2025). Vision transformers in precision agriculture: A comprehensive survey. Intelligent Systems with Applications, 29, 200617. https://doi.org/10.1016/j.iswa.2025.200617
Mohanty, S. P., Hughes, D. P., &Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
Pandian, J. A., Kumar, V. D., Geman, O., Hnatiuc, M., Arif, M., & Kanchanadevi, K. (2022). Plant disease detection using deep convolutional neural network. Applied Sciences, 12(14), 6982. https://doi.org/10.3390/app12146982
Picon, 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. https://doi.org/10.1016/j.compag.2018.04.002
Praveen, S. P., Anusha, P. V., Akarapu, R. B., Kocharla, S., Penubaka, K. K. R., Shariff, V., & Dewi, D. A. (2025). AI-powered diagnosis: Revolutionizing healthcare with neural networks. Journal of Theoretical and Applied Information Technology, 103(3). https://www.jatit.org/volumes/Vol103No3/16Vol103No3.pdf
Praveen, S. P., Prema, K., Bommanaboina, Y., Meharaj, S., Swaroop, T. V., & Vallabhaneni, S. C. (2025). Multi-Crop Plant Leaf Disease Detection Using Lite Models. International Journal of Advancement in Life Sciences Research. 8(2), 113-128. https://doi.org/10.31632/ijalsr.2025.v08i02.009
Rangarajan, A. K., Purushothaman, R., & Ramesh, A. (2018). Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Computer Science, 133, 1040-1047. https://doi.org/10.1016/j.procs.2018.07.070
Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767. https://doi.org/10.48550/arXiv.1804.02767
Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), 468. https://doi.org/10.3390/plants8110468
Shahi, T. B., Xu, C. Y., Neupane, A., & Guo, W. (2023). Recent advances in crop disease detection using UAV and deep learning techniques. Remote Sensing, 15(9), 2450. https://doi.org/10.3390/rs15092450
Shariff, V., Paritala, C., &Ankala, K. M. (2025). Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation. Scientific Reports, 15(1), 15640. https://doi.org/10.1038/s41598-025-98731-4
Sharma, P., Hans, P., & Gupta, S. C. (2020). Classification of plant leaf diseases using machine learning and image preprocessing techniques. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 480–484). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/Confluence47617.2020.9057889
Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S., & Batra, N. (2020). PlantDoc: A Dataset for Visual Plant Disease Detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (CoDS COMAD 2020). Association for Computing Machinery, New York, NY, USA, 249–253. https://doi.org/10.1145/3371158.3371196
Sirisha, U., Nakka, R., Janaswami Hymavathi, S., Praveen, S. P., & Karras, D. A. (2025). Enhancing cassava leaf disease detection through traditional segmentation and attention-driven deep learning approaches. International Journal of Advancement in Life Sciences Research,8(4), 138-157. https://doi.org/10.31632/ijalsr.2025.v08i04.011
Sirisha, U., Sharma, K., Praveen, P., Parashar, D., &Tirumanadham, N. K. M. K. (2026). A lightweight residual dilated cnn–transformer framework for efficient rice leaf disease classification. Springer Nature in Research Square.https://doi.org/10.21203/rs.3.rs-9038735/v1
Sladojevic, 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, 3289801. https://doi.org/10.1155/2016/3289801
Tan, M., & Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research. 97, 6105-6114. https://proceedings.mlr.press/v97/tan19a/tan19a.pdf
Thatha, V. N., Kumari, P. M. K., Sirisha, U., Manoj, V. V. R., & Surapaneni, P. P. (2024). GLAD: Advanced attention mechanism-based model for grape leaf disease detection. Ingenierie des Systemesd'Information, 29(2), 687- 695. https://doi.org/10.18280/isi.290230
Too, 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. https://doi.org/10.1016/j.compag.2018.03.032
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Praveen, S., Arava, K., Nagamani, T., Hymavathi, J. and Kamalrudin, M. (2026) “A Deep Learning Approach for Real-Time Detection and Classification of Crop Leaf Diseases to Support Sustainable Farming Practices”, International Journal of Advancement in Life Sciences Research, 9(3), pp. 165-188. doi: https://doi.org/10.31632/ijalsr.2026.v09i03.013.
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