Reducing Complexity in Leaf Disease Classification Using a Lightweight Convolutional Neural Network

Roney Nogueira de Sousa; Saulo Anderson Freitas de Oliveria; Pedro Pedrosa Reboucas Filho

doi:10.5753/jbcs.2026.5712

Authors

Roney Nogueira de Sousa Federal Institute of Education, Science and Technology of Ceará https://orcid.org/0009-0000-9771-332X
Saulo Anderson Freitas de Oliveria Federal Institute of Education, Science and Technology of Ceará https://orcid.org/0000-0001-6362-6113
Pedro Pedrosa Reboucas Filho Federal Institute of Education, Science and Technology of Ceará https://orcid.org/0000-0002-1878-5489

DOI:

https://doi.org/10.5753/jbcs.2026.5712

Keywords:

Plant disease identification, Lightweight CNN, LDPNet, Deep learning, Agriculture

Abstract

Agricultural production faces significant annual losses due to plant diseases, with economic impacts exceeding 40 million dollars and contributing to acute hunger affecting over 281.6 million people in 2023. The timely and accurate identification of plant diseases through leaf image analysis is crucial to mitigate these losses and ensure global food security. This study proposes a lightweight Convolutional Neural Network model, inspired by the MobileNet architecture, designed to classify various plant leaf diseases efficiently. Leveraging a publicly available dataset, this research focuses on developing a model that balances high performance with computational efficiency, making it suitable for real-world applications in resource-constrained environments. The proposed model, named LDPNet, achieved an outstanding accuracy of 99.62%, alongside precision, recall, F1-Score, and AUC metrics of 99.16%, 99.08%, 99.11%, and 99.99%, respectively. A comprehensive comparative analysis was conducted against MobileNetV2 and a reference model from previous research, highlighting the superior performance of LDPNet in terms of both accuracy and efficiency. The results demonstrate that the proposed architecture not only maintains high classification performance but also significantly reduces the number of parameters, making it a practical and scalable solution for plant disease identification. This study contributes to the growing field of agricultural technology by providing a robust, lightweight, and efficient tool for early disease detection, with the potential to enhance crop management and reduce economic losses in agriculture.

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References

Angelotti, F., Hamada, E., and Bettiol, W. (2024). A comprehensive review of climate change and plant diseases in brazil. Plants, 13(17). DOI: 10.3390/plants13172447.

Bharate, A. A. and Shirdhonkar, M. S. (2017). A review on plant disease detection using image processing. In 2017 International Conference on Intelligent Sustainable Systems (ICISS), pages 103-109. DOI: 10.1109/ISS1.2017.8389326.

de Oliveira, C. M., dos Santos Pereira, T., Mouro, L. P., and Canale, M. C. (2026). A disease complex: crop losses and economic impact of corn stunt diseases on brazilian corn production. Crop Protection, 202:107549. DOI: 10.1016/j.cropro.2026.107549.

de Sousa, R. N. and de Sousa, R. N. (2023). Development of a convolutional neural network architecture for classifying foliar diseases in plants. In Anais do XVI Encontro Unificado de Computação do Piauí, pages 1-8, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/enucompi.2023.26610.

Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(7). Available at:[link].

Falaschetti, L., Manoni, L., Di Leo, D., Pau, D., Tomaselli, V., and Turchetti, C. (2022). A cnn-based image detector for plant leaf diseases classification. HardwareX, 12:e00363. DOI: 10.1016/j.ohx.2022.e00363.

Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture, 145:311-318. DOI: 10.1016/j.compag.2018.01.009.

Food and Agriculture Organization of the United Nations (FAO) (2024). The hidden health crisis: How plant diseases threaten global food security. FAO One Health Highlights. Available at:[link].

Funck, F. C. (2019). Detectando a ferrugem asiática na folha da soja utilizando redes neurais convolucionais. B.S. thesis, Universidade Tecnológica Federal do Paraná. Available at:[link].

Gai, Y. and Wang, H. (2024). Plant disease: A growing threat to global food security. Agronomy, 14(8). DOI: 10.3390/agronomy14081615.

Gajjar, R., Gajjar, N., Thakor, V. J., Patel, N. P., and Ruparelia, S. (2022). Real-time detection and identification of plant leaf diseases using convolutional neural networks on an embedded platform. The Visual Computer, pages 1-16. DOI: 10.1007/s00371-021-02164-9.

Geetharamani, G. and Pandian, A. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 76:323-338. DOI: 10.1016/j.compeleceng.2019.04.011.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press. DOI: 10.1016/c2024-0-03581-3.

Hashemifar, S. and Zakeri, M. N. (2024). Deep identification of plant diseases. In 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pages 1-6. DOI: 10.1109/AISP61396.2024.10475267.

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR, abs/1704.04861. DOI: 10.48550/arxiv.1704.04861.

Huber, D. M. and Jones, J. B. (2013). The role of magnesium in plant disease. Plant and soil, 368:73-85. DOI: 10.1007/s11104-012-1476-0.

Husson, O., Sarthou, J.-P., Bousset, L., Ratnadass, A., Schmidt, H.-P., Kempf, J., Husson, B., Tingry, S., Aubertot, J.-N., Deguine, J.-P., et al. (2021). Soil and plant health in relation to dynamic sustainment of eh and ph homeostasis: A review. Plant and Soil, 466(1):391-447. DOI: 10.1007/s11104-021-05047-z.

KC, K., Yin, Z., Wu, M., and Wu, Z. (2019). Depthwise separable convolution architectures for plant disease classification. Computers and Electronics in Agriculture, 165:104948. DOI: 10.1016/j.compag.2019.104948.

Khirade, S. D. and Patil, A. B. (2015). Plant disease detection using image processing. In 2015 International conference on computing communication control and automation, pages 768-771. IEEE. DOI: 10.1109/iccubea.2015.153.

Kingma, D. P. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. DOI: 10.48550/arxiv.1412.6980.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6):84-90. DOI: 10.1145/3065386.

Li, Z., Yang, Y., Li, Y., Guo, R., Yang, J., and Yue, J. (2020). A solanaceae disease recognition model based on se-inception. Computers and Electronics in Agriculture, 178:105792. DOI: 10.1016/j.compag.2020.105792.

Lu, Y., Yi, S., Zeng, N., Liu, Y., and Zhang, Y. (2017). Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267:378-384. DOI: 10.1016/j.neucom.2017.06.023.

MOININA, A., LAHLALI, R., and BOULIF, M. (2019). Important pests, diseases and weather conditions affecting apple production in morocco: Current state and perspectives. Revue Marocaine des Sciences Agronomiques et Vétérinaires, 7(1). Available at:[link].

Pandian, J. A., Kanchanadevi, K., Kumar, V. D., Jasińska, E., Goňo, R., Leonowicz, Z., and Jasiński, M. (2022). A five convolutional layer deep convolutional neural network for plant leaf disease detection. Electronics, 11(8). DOI: 10.3390/electronics11081266.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510-4520. DOI: 10.1109/cvpr.2018.00474.

Sousa, R., Oliveira, S., and Filho, P. R. (2024). Desenvolvimento de um modelo convolucional leve para identificação de doenças foliares. In Anais Estendidos da XXXVII Conference on Graphics, Patterns and Images, pages 117-122, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sibgrapi.est.2024.31655.

Syamsuri, B. and Kusuma, G. P. (2019). Plant disease classification using lite pretrained deep convolutional neural network on android mobile device. Int. J. Innov. Technol. Explor. Eng, 9(2):2796-2804. DOI: 10.35940/ijitee.b6647.129219.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1-9. DOI: 10.1109/cvpr.2015.7298594.

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018). A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning - ICANN 2018, pages 270-279, Cham. Springer International Publishing. DOI: 10.1007/978-3-030-01424-7_27.

WHO, W. H. O. (2024). Risco de fome generalizada aumenta pelo quinto ano consecutivo. Available at:[link].

Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., and He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43-76. DOI: 10.1109/jproc.2020.3004555.