Multi-Crop Plant Leaf Disease Detection Using Lite Models
Abstract
India, being an agricultural nation, significantly relies on agriculture for its economic growth. Farmers in India are facing several problems with plant diseases that directly impact crop yield. Plant diseases reduce both crop yield and quality. Detecting and treating plant diseases is crucial for achieving a high-quality crop yield. Identifying plant leaf disorders with the human eye is challenging and time-consuming. Our research will help farmers identify diseases even in their early stages with high accuracy and low time. We utilized a Convolutional Neural Network (CNN) trained on a large dataset for plant disease detection. Raw photos of normal leaves as well as sick leaves of various species abound in the dataset. The framework extracts features from the leaf image and classifies them based on the color, size, symptoms of the disease, and several factors and identifies the disease in real time. Adding more images and using EfficientNet-Lite has increased accuracy and efficiency with lower computational power. This model has higher potential than conventional transfer learning models. The suggested study outperforms the several machine learning tools currently in use in terms of accuracy. The results of this study show that the proposed model achieved 93% accuracy, indicating superior performance compared to traditional methods. The system's real-time recognition function allows early intervention and minimizes harvest loss. Future research should focus on expanding data records to improve inference period optimization, generalization, and further integration of environmental factors.
Keywords:
Convolutional Neural Networks, Deep Learning in Agriculture, EfficientNet-Lite, InceptionV3, Leaf Disease Classification, PlantVillage Dataset
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References
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Agarwal, M., Singh, A., Arjaria, S., Sinha, A., & Gupta, S. (2020). ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Computer Science, 167, 293-301. https://doi.org/10.1016/j.procs.2020.03.225
Al Bashish, D., Braik, M., & Bani-Ahmad, S. (2011). Detection and classification of leaf diseases using K-means-based segmentation and. Information Technology Journal, 10(2), 267-275. https://doi.org/10.3923/itj.2011.267.275
Ashwinkumar, S., Rajagopal, S., Manimaran, V., & Jegajothi, B. J. M. T. P. (2022). Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Materials Today: Proceedings, 51, 480-487. https://doi.org/10.1016/j.matpr.2021.05.584
Atila, Ü., Uçar, M., Akyol, K., & Uçar, E. (2021). Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61. https://doi.org/10.1016/j.ecoinf.2020.101182
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020, August). End-to-end object detection with transformers. In European conference on computer vision (pp. 213-229). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-58452-8_1
Dey, A. K., Sharma, M., & Meshram, M. R. (2016). Image processing based leaf rot disease, detection of betel vine (Piper BetleL.). Procedia Computer Science, 85, 748-754. https://doi.org/10.1016/j.procs.2016.05.262
Falaschetti, L., Manoni, L., Di Leo, D., Pau, D., Tomaselli, V., & Turchetti, C. (2022). A CNN-based image detector for plant leaf diseases classification. HardwareX, 12, https://doi.org/10.1016/j.ohx.2022.e00363
Harakannanavar, S. S., Rudagi, J. M., Puranikmath, V. I., Siddiqua, A., & Pramodhini, R. (2022). Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3(1), 305-310. https://doi.org/10.1016/j.gltp.2022.03.016
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141). https://doi.org/10.1109/tpami.2019.2913372
Jiang, P., Chen, Y., Liu, B., He, D., & Liang, C. (2019). Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. Ieee Access, 7, 59069-59080. https://doi.org/10.1109/access.2019.2914929
Jiang, Z., Dong, Z., Jiang, W., & Yang, Y. (2021). Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning. Computers and Electronics in Agriculture, 186. https://doi.org/10.1016/j.compag.2021.106184
Kaggle.(n.d.).PlantVillage dataset. Available at: https://www.kaggle.com/datasets/plantvillage-dataset
Kotwal, J. G., Kashyap, R., & Shafi, P. M. (2024). Artificial driving based EfficientNet for automatic plant leaf disease classification. Multimedia Tools and Applications, 83(13), 38209-38240. https://doi.org/10.1007/s11042-023-16882-w
Li, J., Qiao, Y., Liu, S., Zhang, J., Yang, Z., & Wang, M. (2022). An improved YOLOv5-based vegetable disease detection method. Computers and Electronics in Agriculture, 202. https://doi.org/10.1016/j.compag.2022.107345
Meta-Album. (n.d.). PLT_VIL dataset. Available at: https://meta-album.github.io/datasets
Qi, J., Liu, X., Liu, K., Xu, F., Guo, H., Tian, X., ... & Li, Y. (2022). An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Computers and Electronics in Agriculture, 194. https://doi.org/10.1016/j.compag.2022.106780
Qian, S., Ning, C., & Hu, Y. (2021, March). MobileNetV3 for image classification. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) (pp. 490-497). IEEE. https://doi.org/10.1109/icbaie52039.2021.9389905
Rahman, K. N., Banik, S. C., Islam, R., & Al Fahim, A. (2025). A real time monitoring system for accurate plant leaves disease detection using deep learning. Crop Design, 4(1), https://doi.org/10.1016/j.cropd.2024.100092
Rao, U. S., Swathi, R., Sanjana, V., Arpitha, L., Chandrasekhar, K., & Naik, P. K. (2021). Deep learning precision farming: grapes and mango leaf disease detection by transfer learning. Global transitions proceedings, 2(2), 535-544. https://doi.org/10.1016/j.gltp.2021.08.002
Richard, B., Qi, A., & Fitt, B. D. (2022). Control of crop diseases through Integrated Crop Management to deliver climate‐smart farming systems for low‐and high‐input crop production. Plant Pathology, 71(1), 187-206. https://doi.org/10.1111/ppa.13493
Sai Reddy, B., & Neeraja, S. (2022). Plant leaf disease classification and damage detection system using deep learning models. Multimedia Tools and Applications, 81(17), 24021-24040. https://doi.org/10.1007/s11042-022-12147-0
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520). https://doi.org/10.1109/CVPR.2018.00474
Sharma, V., Tripathi, A. K., & Mittal, H. (2023). DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection. Ecological informatics, 75. https://doi.org/10.1016/j.ecoinf.2023.102025
Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., ... & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14, https://doi.org/10.3389/fpls.2023.1158933
Swapna, D., Sri, U. K., Himaja, V. S. N., Varshita, T. N., Gayatri, V., & Praveen, S. P. (2023, December). Crypto logistic network: food supply chain and micro investment using blockchain. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 908-915). IEEE. http://dx.doi.org/10.1109/ICACRS58579.2023.10404449
Tang, Z., He, X., Zhou, G., Chen, A., Wang, Y., Li, L., & Hu, Y. (2023). A precise image-based tomato leaf disease detection approach using PLPNet. Plant Phenomics, 5. https://doi.org/10.34133/plantphenomics.0042
Thatha, V. N., Kumari, P. M. K., Sirisha, U., Manoj, V. V. R., & Praveen, S. P. (2024). GLAD: Advanced attention mechanism-based model for grape leaf disease detection. ingénierie des systèmes d'information, 29(2). 687-695. https://doi.org/10.18280/isi.290230
Vallabhajosyula, S., Sistla, V., & Kolli, V. K. K. (2024). A novel hierarchical framework for plant leaf disease detection using residual vision transformer. Heliyon, 10(9). https://doi.org/10.1016/j.heliyon.2024.e29912
Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2023). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7464-7475). https://doi.org/10.48550/arXiv.2207.02696
Xu, X., Yang, G., Wang, Y., Shang, Y., Hua, Z., Wang, Z., & Song, H. (2024). Plant leaf disease identification by parameter-efficient transformer with adapter. Engineering Applications of Artificial Intelligence, 138. https://doi.org/10.1016/j.engappai.2024.109466
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How to Cite
S, P., K, P., Bommanaboina, Y., Meharaj, S., Tallam, V. S. and Vallabhaneni, S. C. (2025) “Multi-Crop Plant Leaf Disease Detection Using Lite Models”, International Journal of Advancement in Life Sciences Research, 8(2), pp. 113-128. doi: https://doi.org/10.31632/ijalsr.2025.v08i02.009.
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Research Articles
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