Alpha-EIOU-YOLOv8: An Improved Algorithm for Rice Leaf Disease Detection

• Published in: AgriEngineering 2024-02, Vol.6 (1), p.302-317
• Main Authors: Trinh, Dong Cong, Mac, Anh Tuan, Dang, Khanh Giap, Nguyen, Huong Thanh, Nguyen, Hoc Thai, Bui, Thanh Dang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; blast leaf; Brown spot; Comparative studies; Datasets; Deep learning; Disease detection; Food plants; Food quality; leaf folder; Leafspot; Leaves; Machine learning; Medical imaging; Neural networks; Performance assessment; Performance enhancement; Plant diseases; Plants; Rice; rice leaf diseases; Seeds; Training; YOLOv8
• ISSN: 2624-7402; 2624-7402
• DOI: 10.3390/agriengineering6010018

Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crops. Recently, plant disease detection based on deep learning approaches has achieved better performance than current state-of-the-art methods. Hence, this paper utilized a convolutional neural network (CNN) to improve rice leaf disease detection efficiency. We present a modified YOLOv8, which replaces the original Box Loss function by our proposed combination of EIoU loss and α-IoU loss in order to improve the performance of the rice leaf disease detection system. A two-stage approach is proposed to achieve a high accuracy of rice leaf disease identification based on AI (artificial intelligence) algorithms. In the first stage, the images of rice leaf diseases in the field are automatically collected. Afterward, these image data are separated into blast leaf, leaf folder, and brown spot sets, respectively. In the second stage, after training the YOLOv8 model on our proposed image dataset, the trained model is deployed on IoT devices to detect and identify rice leaf diseases. In order to assess the performance of the proposed approach, a comparative study between our proposed method and the methods using YOLOv7 and YOLOv5 is conducted. The experimental results demonstrate that the accuracy of our proposed model in this research has reached up to 89.9% on the dataset of 3175 images with 2608 images for training, 326 images for validation, and 241 images for testing. It demonstrates that our proposed approach achieves a higher accuracy rate than existing approaches.