Using deep learning for rice leaf diseases detection using YOLOv5
Số 2 (81) 2023
Nguyễn Trọng Các, Trinh Cong Dong, Mac Tuan Anh, Giap Dang Khanh, Nguyen Thanh Huong, Nguyen Trong Cac, and Bui Dang T
Tạp chí NCKH - Đại học Sao Đỏ

Rice is a popular food crop in Asian countries, and its yield is very susceptible to diseases. There are many reasons that cause diseases on rice, the most common can be mentioned bacteria and viruses. Early detection of foliar diseases can help prevent the spread of disease and provide timely solutions, deep learning algorithms can do this quickly and get relatively good results. In this article, we propose an approach to identify rice leaf diseases using CNN (Convolutional Neural Network) methods. YOLOv5 algorithms is applied to detect diseases via analyzing the classified images. We apply the proposed approach for the dataset including 4141 images from many sources on the internet such as Kaggle, Google, Mendeley. Experimental results show the desired performances of the proposed approach.

Rice leaf diseases; Deep Learning; Artificial Intelligence; Bacterial blight; Blast leaf; YOLOv5.
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