DiffuCNN: Tobacco Disease Identification and Grading Model in Low-Resolution Complex Agricultural Scenes

• Published in: Agriculture (Basel) 2024-02, Vol.14 (2), p.318
• Main Authors: Xiong, Huizhong, Gao, Xiaotong, Zhang, Ningyi, He, Haoxiong, Tang, Weidong, Yang, Yingqiu, Chen, Yuqian, Jiao, Yang, Song, Yihong, Yan, Shuo
• Format: Article
• Language: English
• Subjects: Ablation; Accuracy; Agricultural industry; Agricultural production; Algorithms; Artificial intelligence; Automation; Classification; Computer applications; Crop diseases; Deep learning; DiffuCNN; Diffusion; Feature recognition; Identification; Image enhancement; Image processing; Lesions; Machine learning; Mathematical optimization; Methods; Modules; Neural networks; object detection; Object recognition; Optimization; Optimization algorithms; Plant diseases; Recall; Tobacco; tobacco disease identification
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture14020318

A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution conditions. After detecting lesions, the grading of the disease severity is achieved through counting. The key features of DiffuCNN include a resolution enhancement module based on diffusion, an object detection network optimized through filter pruning, and the employment of the CentralSGD optimization algorithm. Experimental results demonstrate that DiffuCNN surpasses other models in precision, with respective values of 0.98 on precision, 0.96 on recall, 0.97 on accuracy, and 62 FPS. Particularly in counting tobacco lesions, DiffuCNN exhibits an exceptional performance, attributable to its efficient network architecture and advanced image processing techniques. The resolution enhancement module based on diffusion amplifies minute details and features in images, enabling the model to more effectively recognize and count tobacco lesions. Concurrently, filter pruning technology reduces the model’s parameter count and computational burden, enhancing the processing speed while retaining the capability to recognize key features. The application of the CentralSGD optimization algorithm further improves the model’s training efficiency and final performance. Moreover, an ablation study meticulously analyzes the contribution of each component within DiffuCNN. The results reveal that each component plays a crucial role in enhancing the model performance. The inclusion of the diffusion module significantly boosts the model’s precision and recall, highlighting the importance of optimizing at the model’s input end. The use of filter pruning and the CentralSGD optimization algorithm effectively elevates the model’s computational efficiency and detection accuracy.