Abstract
The utilization of deep learning-based models for automatic plant leaf disease detection has been established for many years. Such methods have been successfully integrated in the agriculture domain, aiding the swift and accurate identification of various diseases. However, the unavailability of annotated data, the variability of systems, and the lack of an efficient model for real-time use remain unresolved challenges. The goal of this work was to develop a deep learning-based model for crop disease detection and recognition system for real-field scenarios. For this, we trained lightweight versions of the YOLOv5, YOLOv7, YOLOv8 and compared their detection performance. Experiments were carried out on a self-collected dataset containing 3136 real-field images of apples ( healthy and diseased ) and 567 images of PlantDoc dataset. Results revealed that the prediction accuracy of YOLOv8 was superior to others on AdamW optimizer. The results were further validated by deploying it on Raspberry Pi 4.
Reference29 articles.
1. I. Attri, L. K. Awasthi, T. P. Sharma, and P. Rathee, “A re- view of deep learning techniques used in agriculture,” Ecological Informatics, p. 102217, 2023.
2. M. Y. Raza, R. Wu, and B. Lin, “A decoupling process of pakistan’s agriculture sector: Insights from energy and economic perspectives,” Energy, vol. 263, p. 125658, 2023.
3. M. Z. Siddiqui, A. Elahi, and K. Khan, “Implementation of technology for modern management of agriculture field impacting the socio-economic condition of pak- istan,” Journal of Computing & Biomedical Informatics, vol. 5, no. 02, pp. 338–346, 2023.
4. S. K. Noon, M. Amjad, M. A. Qureshi, and A. Mannan, “Use of deep learning techniques for identification of plant leaf stresses: A review,” Sustainable Computing: In- formatics and Systems, vol. 28, p. 100443, 2020.
5. G. Farjon, L. Huijun, and Y. Edan, “Deep-learning-based counting methods, datasets, and applications in agricul- ture: A review,” Precision Agriculture, vol. 24, no. 5, pp. 1683–1711, 2023.