Plant leaf disease recognition based on improved SinGAN and improved ResNet34 - Chen, Jiaojiao; Hu, Haiyang; Yang, Jianping (Frontiers in Artificial Intelligence, 2024)
The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture.However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy.This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction- methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: 1) ReSinGN generates highquality images at least 44.6 times faster in training speed compared to SinGAN; 2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images; 3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion; 4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it's similar to precision), recall, and F1score of 98.57%, 96.57%, 98.68%, 97.7%, and 98.17% respectively for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65%, 4.66%, 0.88%, 4.1%, and 2.47% respectively.