Deep Learning-Based Automated Plant Leaf Disease Detection and Classification System Using Convolutional Neural Networks
Keywords:
Deep Learning, Plant Disease Classification, Convolutional Neural Networks (CNNs), Leaf Disease Detection, Agricultural Automation, Image ProcessingAbstract
Agricultural sector is one of the pillars of food security in the globe but is constantly affected by plant diseases, which may destroy crops, decrease productivity and cause serious losses to the economy. The conventional approaches of disease detection are usually based on manual examination by specialists, which may be time-consuming, labour-intensive and are likely to be subject to human error. In order to overcome these shortcomings, this project presents a new deep learning-based solution in the automated classification of various leaf diseases in a wide variety of plant species. Using the power of convolutional neural networks (CNNs), the system will be able to identify and classify plant leaf diseases accurately in order to detect them early and intervene in time. The model itself is trained on a vast amount of data, covering a broad range of diseases the model is used on the crops of apples, blueberries, cherries, corn, grapes, peaches, peppers, potatoes, raspberries, soybeans, squash, strawberries, and tomatoes, which makes it applicable in a variety of different agricultural contexts. The system has an easy to use graphical interface that has been created with CustomTkinter and TkinterDnD, enabling users to add pictures with either drag-and-drop or file browsing. After uploading an image, the system pre-processes it, i.e. resizing and normalizing the data to the input specifications of a deep learning modelThe interface will be easy to use and user friendly and thus can be used by both farmers and agricultural professionals and even researchers. The project does not just show the possibilities of deep learning in transforming the process of detecting plant diseases, but also teaches the need to implement the latest technologies in agriculture. By automating the disease identification process, the system will cut operational costs used in disease identification, minimize chances of misdiagnosis and, the system will help in responding to the disease outbreaks in a shorter period of time. In addition, the capability of the system to process numerous diseases in different plants species makes it a powerful tool in advancement of crop management and agricultural production. This work eventually leads to the larger objective of sustainable agriculture as it may offer an effective, reliable, and scalable way of classifying plant diseases, thus contributing to the worldwide aim of creating food security and decreasing crop losses amid the increasing agricultural difficulties.
References
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
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
Barbedo, J. G. A. (2018). Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers and Electronics in Agriculture, 153, 46–53. https://doi.org/10.1016/j.compag.2018.08.013
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
Zhang, 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. https://doi.org/10.1016/j.compag.2019.03.012
Hughes, D. P., & Salathé, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.
Arribas, J. I., Sánchez-Ferrero, G. V., Ruiz-Ruiz, G., & Gómez-Gil, J. (2020). Leaf classification in sunflower crops by computer vision and neural networks. Computers and Electronics in Agriculture, 178, 105742. https://doi.org/10.1016/j.compag.2020.105742
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
Chakraborty, S., & Newton, A. C. (2011). Climate change, plant diseases and food security: An overview. Plant Pathology, 60(1), 2–14. https://doi.org/10.1111/j.1365-3059.2010.02411.x
Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., & González-Moreno, P. (2020). A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sensing, 12(19), 3164. https://doi.org/10.3390/rs12193164
Ryan, M., & Stahl, B. C. (2020). Artificial intelligence ethics guidelines for developers and users: Clarifying their content and normative implications. Journal of Information, Communication and Ethics in Society, 19(1), 61–86. https://doi.org/10.1108/JICES-12-2019-0138
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
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
Zhang, X., Qiao, Y., Meng, F., Fan, C., & Zhang, M. (2021). Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access, 9, 32982–32994. https://doi.org/10.1109/ACCESS.2021.3059656
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
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712. https://doi.org/10.1007/s11119-012-9274-5
Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming, and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90–91, 100315. https://doi.org/10.1016/j.njas.2019.100315
Kamilaris, A., Fonts, A., & Prenafeta-Boldú, F. X. (2019). The rise of blockchain technology in agriculture and food supply chains. Trends in Food Science & Technology, 91, 640–652. https://doi.org/10.1016/j.tifs.2019.07.034
Wang, G., Sun, Y., & Wang, J. (2020). Automatic image-based plant disease severity estimation using deep learning. Computational Intelligence and Neuroscience, 2020, 2919856. https://doi.org/10.1155/2020/2919856
Van der Wal, R., Sharma, N., Mellish, C., Robinson, A., & Siddharthan, A. (2016). The role of automated feedback in training and retaining biological recorders for citizen science. Conservation Biology, 30(3), 550–561. https://doi.org/10.1111/cobi.12705
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Engineering Science & Humanities
This work is licensed under a Creative Commons Attribution 4.0 International License.
