YOLOv8 Assisted Precision Agriculture Approach for Tea Leaf Disease Detection

SANJEET BORAH; Kandarpa Kalita; Prof. Laxmi Narayan Sethi; Dr. Avinash Kumar

doi:10.56093/ijas.v96i7.174068

Authors

SANJEET BORAH Assam University
Kandarpa Kalita Assam Down Town University
Prof. Laxmi Narayan Sethi Assam University
Dr. Avinash Kumar Assam University

https://doi.org/10.56093/ijas.v96i7.174068

Keywords:

Disease detection, Deep learning models, YOLOv8 architecture, Precision agriculture, Computer vision techniques.

Abstract

Tea (Camellia sinensis) is a major agricultural crop whose yield and quality are highly affected by foliar diseases that impair plant health and reduce market value. Many diseases exhibit similar early symptoms, making manual field inspection slow, inconsistent, and prone to misdiagnosis under variable field conditions. These limitations create a strong need for accurate, scalable, and automated disease detection methods to support effective tea crop management. To overcome these challenges, this study introduces a YOLOv8-based deep learning framework capable of automatically detecting and localizing multiple tea leaf diseases using images captured under natural field conditions. The dataset includes seven major disease classes and a healthy leaf category, each annotated using normalized YOLO bounding-box formats and enhanced through diverse augmentation strategies to strengthen model generalization. The YOLOv8-s model was trained for 100 epochs using a composite loss function that combines Complete IoU (CIoU) for accurate bounding-box regression with classification and objectness losses. Model performance was assessed using standard detection metrics. The trained model achieved a mAP@0.5 of 97.68%, a mAP@0.5:0.95 of 97.68%, precision of 93.61%, and recall of 95.31%, reflecting substantial improvement over the training period. Further diagnostic evaluations including confusion matrices, precision–recall curves, and confidence-based performance analyses confirmed the model’s robustness, even for visually similar disease symptoms such as Brown Blight and Tea Algal Leaf Spot. Overall, the proposed YOLOv8 framework demonstrates high accuracy and computational efficiency for tea leaf disease detection. Its strong performance under real field variability highlights its potential for integration into automated plantation monitoring systems, UAV-assisted crop surveillance, and mobile-based advisory tools to support precision agriculture.

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Author Biographies

SANJEET BORAH, Assam University

Research Scholar, Department of Agricultural Engineering, Assam University, Silchar and Assistant Professor, Faculty of Agricultural Sciences and Technology, Assam down town University, Guwahati
Kandarpa Kalita, Assam Down Town University

Faculty of Computer Technology, Assam down town University, Assistant Professor
Prof. Laxmi Narayan Sethi, Assam University

Professor, Department of Agricultural Engineering, Assam University, Silchar
Dr. Avinash Kumar, Assam University

Assistant Professor, Department of Agricultural Engineering, Assam University, Silchar

References

Abbas, A., Zhang, Z., Zheng, H., Alami, M. M., Alrefaei, A. F., Abbas, Q., Naqvi, S. A. H., Rao, M. J., Mosa, W. F. A., Abbas, Q., Hussain, A., Hassan, M. Z., & Zhou, L. (2023). Drones in Plant Disease Assessment, Efficient Monitoring, and Detection: A Way Forward to Smart Agriculture. Agronomy, 13(6), 1524. https://doi.org/10.3390/agronomy13061524.

Bhatt, P. V., Sarangi, S., & Pappula, S. (2019). Detection of diseases and pests on images captured in uncontrolled conditions from tea plantations. Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 11008, 73–82. https://doi.org/10.1117/12.2518868.

Datta, S., & Gupta, N. (2023). A novel approach for the detection of tea leaf disease using deep neural network. Procedia Computer Science, 218, 2273-2286. https://doi.org/10.1016/j.procs.2023.01.203.

Heng, Q., Yu, S., & Zhang, Y. (2024). A new AI-based approach for automatic identification of tea leaf disease using deep neural network based on hybrid pooling. Heliyon, 10(5). https://doi.org/10.1016/j.heliyon.2024.e26465.

Li, Y., Lu, Y., Liu, H., Bai, J., Yang, C., Yuan, H., & Xiao, Q. (2024). Tea leaf disease and insect identification based on improved MobileNetV3. Frontiers in Plant Science, 15, 1459292. https://doi.org/10.3389/fpls.2024.1459292.

Pandey, A. K., Sinniah, G. D., Babu, A., & Tanti, A. (2021). How the Global Tea Industry Copes with Fungal Diseases-Challenges and Opportunities. Plant Disease, 105(7), 1868–1879. https://doi.org/10.1094/PDIS-09-20-1945-FE.

Panthee, D. R., Pandey, A., & Paudel, R. (2024). Multiple Foliar Fungal Disease Management in Tomatoes: A Comprehensive Approach. International Journal of Plant Biology, 15(1), 69–93. https://doi.org/10.3390/ijpb15010007.

Preanto, S. A., Ahad, M. T., Emon, Y. R., Mustofa, S., & Alamin, M. (2024). A Semantic Segmentation Approach on Sweet Orange Leaf Diseases Detection Utilizing YOLO (arXiv:2409.06671). arXiv. https://doi.org/10.48550/arXiv.2409.06671.

Rahman, H., Ahmad, I., Jon, P. H., Salam, A., & Rabbi, M. F. (2024). Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network. Scientific reports, 14(1), 14097. https://doi.org/10.1038/s41598-024-62058-3.

Srilatha, A., & Praveen, P. (2025). CNN-Based Plant Leaf Disease Identification and Classification for Sustainable Agriculture. 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI), 1459–1465. https://doi.org/10.1109/ICMCSI64620.2025.10883522.

Talukdar, B., Chakravartty, P. K., & Sharma, B. (2023). Tea Industry of Assam and India: Statistical Analysis and Its Future Trends. Journal of Advanced Zoology, 44(1), 118–128. https://doi.org/10.53555/jaz.v44i1.4368.

Upadhyay, A., Chandel, N. S., Singh, K. P., Chakraborty, S. K., Nandede, B. M., Kumar, M., Subeesh, A., Upendar, K., Salem, A., & Elbeltagi, A. (2025). Deep learning and computer vision in plant disease detection: A comprehensive review of techniques, models, and trends in precision agriculture. Artificial Intelligence Review, 58(3), 92. https://doi.org/10.1007/s10462-024-11100-x.

Vilar-Andreu, M., García, L., Garcia-Sanchez, A.-J., Asorey-Cacheda, R., & Garcia-Haro, J. (2024). Enhancing Precision Agriculture Pest Control: A Generalized Deep Learning Approach with YOLOv8-Based Insect Detection. IEEE Access, 12, 84420–84434. https://doi.org/10.1109/ACCESS.2024.3413979.

Wang, C., Li, H., Deng, X., Liu, Y., Wu, T., Liu, W., Xiao, R., Wang, Z., & Wang, B. (2024). Improved You Only Look Once v.8 Model Based on Deep Learning: Precision Detection and Recognition of Fresh Leaves from Yunnan Large-Leaf Tea Tree. Agriculture, 14(12), 2324. https://doi.org/10.3390/agriculture14122324.

Xiaoxiao, S., Shaomin, M. U., Yongyu, X. U., Zhihao, C. A. O., & Tingting, S. U. (2018, December). Image recognition of tea leaf diseases based on convolutional neural network. In 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) (pp. 304-309). IEEE Xplore. https://doi.org/ 10.1109/SPAC46244.2018.8965555.