Hybrid deep learning approach for cotton leaf disease detection and management using fine-tuned VGG16 and Inception v3 models
Agricultural productivity is frequently threatened by crop diseases, leading to substantial economic losses and hindering the adoption of sustainable farming practices. Early detection and timely intervention are therefore critical to mitigate these risks. This paper presents a novel hybrid deep learning method (HDLM) for accurate classification of cotton leaf diseases by integrating the strengths of two fine-tuned deep learning models—Visual Geometry Group 16 and Inception v3—through a stacking ensemble strategy that combines their predictions at the output level. The model was trained and validated on a carefully curated dataset of 3,000 images, manually labeled into six classes: Aphids, Armyworm, Bacterial Blight, Powdery Mildew, Target Spot, and Healthy Leaves, with 2,400 images used for training and 600 reserved for validation. To enhance generalization and robustness against real-world variations in leaf orientation, illumination, and background, extensive data augmentation techniques were applied, including rotations, flips, zooming, translations, and brightness adjustments. The proposed HDLM achieved a classification accuracy of 98.56%, significantly outperforming benchmark models such as AlexNet, DenseNet-121, ResNet-50, LeNet-5, and a 7-layer convolutional neural network, which achieved accuracies of 90–95%. In addition, the model incorporates a disease management recommendation system that provides actionable guidance to farmers to mitigate diseases and improve crop yields. This research demonstrates the efficacy of ensemble deep learning techniques in plant disease detection, providing a scalable, robust, and practical solution for precision agriculture. Future work should focus on expanding the dataset with heterogeneous sources, integrating advanced augmentation strategies, and exploring real-time feedback mechanisms to further enhance model adaptability, predictive performance, and applicability across diverse agricultural environments.
Aggarwal, R., Aggarwal, E., Jain, A., Choudhury, T., & Kotecha, K. (2023). An automation perception for cotton crop disease detection using machine learning. In 2023 7th International Symposium on Innovative Approaches in Smart Technologies (ISAS) (pp. 1–6). IEEE. https://doi.org/10.1109/ISAS60782.2023.10391530
Alfarisy, A. A., Chen, Q., & Guo, M. (2018). Deep learning based classification for paddy pests & diseases recognition. In ICMAI ‘18: Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence (pp. 21–25). Association for Computing Machinery. https://doi.org/10.1145/3208788.3208795
Amin, H., Darwish, A., Hassanien, A. E., & Soliman, M. (2022). End-to-end deep learning model for corn leaf disease classification. IEEE Access, 10, 31103–31115. https://doi.org/10.1109/ACCESS.2022.3159678
Bedi, P., & Gole, P. (2021). Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artificial Intelligence in Agriculture, 5, 90–101. https://doi.org/10.1016/j.aiia.2021.05.002
Chaudhary, P., Chaudhari, A. K., Cheeran, A. N., & Godara, S. (2012). Color transform based approach for disease spot detection on plant leaf. International Journal of Computer Science and Telecommunications, 3(6), 65–70.
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
Cheng, X., Zhang, Y., Chen, Y., Wu, Y., & Yue, Y. (2017). Pest identification via deep residual learning in complex background. Computers and Electronics in Agriculture, 141, 351–356. https://doi.org/10.1016/j.compag.2017.08.005
Chopkar, P., Wanjari, M., Jumle, P., Chandankhede, P., Mungale, S., & Shaikh, M. S. (2024). A comprehensive review on cotton leaf disease detection using machine learning model. Grenze International Journal of Engineering and Technology, June, 239–245.
Dsouza, H. (2023, November 10). India’s cotton production recovers in 2022–23 season. Textile Insights. Available from: https://textileinsights.in/indias-cotton-production-recovers-in-2022-23-season/ [Last accessed on 13 August, 2024].
Fujita, E., Kawasaki, Y., Uga, H., Kagiwada, S., & Iyatomi, H. (2016). Basic investigation on a robust and practical plant diagnostic system. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 989–992). IEEE. https://doi.org/10.1109/ICMLA.2016.0178
Islam, M. M., Talukder, M. A., Sarker, M. R. A., Uddin, M. A., Akhter, A., Sharmin, S., Mamun, M. S. A., & Debnath, S. K. (2023). A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture. Intelligent Systems with Applications, 20, 200278. https://doi.org/10.1016/j.iswa.2023.200278
Karlekar, A., & Seal, A. (2020). SoyNet: Soybean leaf diseases classification. Computers and Electronics in Agriculture, 172, 105342. https://doi.org/10.1016/j.compag.2020.105342
Kaya, A., Keceli, A. S., Catal, C., Yalic, H. Y., Temucin, H., & Tekinerdogan, B. (2019). Analysis of transfer learning for deep neural network based plant classification models. Computers and Electronics in Agriculture, 158, 20–29. https://doi.org/10.1016/j.compag.2019.01.041
Kshirsagar, P. R., Jagannadham, D. B. V., Ananth, M. B., Mohan, A., Kumar, G., & Bhambri, P. (2022). Machine learning algorithm for leaf disease detection. AIP Conference Proceedings, 2393(1), 020087. https://doi.org/10.1063/5.0074122
Kumar, M., Hazra, T., & Tripathy, S. S. (2017). Wheat leaf disease detection using image processing. International Journal of Latest Technology in Engineering and Management & Applied Science, 6(4), 73–76.
Liu, B., Tan, C., Li, S., He, J., & Wang, H. (2020). A data augmentation method based on generative adversarial networks for grape leaf disease identification. IEEE Access, 8, 102188–102198. https://doi.org/10.1109/ACCESS.2020.2998839
Nigam, S., & Jain, R. (2020). Plant disease identification using deep learning: A review. Indian Journal of Agricultural Sciences, 90(2), 249–257. https://doi.org/10.56093/ijas.v90i2.98996
Pang, J., Bai, Z. Y., Lai, J. C., & Li, S. K. (2011). Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing. In L. Sun, L. Thomas, D. Ford, & J. Hanjun (Eds.), Proceedings of 2011 International Conference on Image Analysis and Signal Processing (pp. 590–594). IEEE. https://doi.org/10.1109/IASP.2011.6109113
Parikh, A., Raval, M. S., Parmar, C., & Chaudhary, S. (2016). Disease detection and severity estimation in cotton plant from unconstrained images. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp. 594–601). IEEE. https://doi.org/10.1109/DSAA.2016.81
Rajasekar, V., Venu, K., Jena, S. R., Varthini, R. J., & Ishwarya, S. (2021). Detection of cotton plant diseases using deep transfer learning. Journal of Mobile Multimedia, 18(2), 307– 324. https://doi.org/10.13052/jmm1550-4646.1828
Saleem, M. H., Potgieter, J., & Arif, K. M. (2020). Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers. Plants, 9(10), 1319. https://doi.org/10.3390/plants9101319
Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41–49. https://doi.org/10.1016/j.inpa.2016.10.005
Thivya Lakshmi, R. T., Katiravan, J., & Visu, P. (2024). CoDet: A novel deep learning pipeline for cotton plant detection and disease identification. Automatika, 65(2), 662–674. https://doi.org/10.1080/00051144.2024.2317093
Wang, D., Khosla, A., Gargeya, R., Irshad, H., & Beck, A. H. (2016). Deep learning for identifying metastatic breast cancer. arXiv. https://doi.org/10.48550/ARXIV.1606.057
Zhang, J., Rao, Y., Man, C., Jiang, Z., & Li, S. (2021). Identification of cucumber leaf diseases using deep learning and small sample size for agricultural Internet of Things. International Journal of Distributed Sensor Networks, 17(4), 15501477211007407. https://doi.org/10.1177/15501477211007407
Zhang, J. H., Kong, F. T., Wu, J. Z., Han, S. Q., & Zhai, Z. F. (2018). Automatic image segmentation method for cotton leaves with disease under natural environment. Journal of Integrative Agriculture, 17(8), 1800–1814. https://doi.org/10.1016/S2095-3119(18)61915-X
Zhang, J., & Zhang, W. (2010). Support vector machine for recognition of cucumber leaf diseases. In H. Xu (Ed.), Proceedings of the 2010 2nd International Conference on Advanced Computer Control (pp. 264–266). IEEE. https://doi.org/10.1109/ICACC.2010.548724
Zhu, W., Chen, H., Ciechanowska, I., & Spaner, D. (2018). Application of infrared thermal imaging for the rapid diagnosis of crop disease. IFAC-PapersOnLine, 51(17), 424– 430. https://doi.org/10.1016/j.ifacol.2018.08.184