| dc.description.abstract | This study developed an innovative CMC (CNN-Momentum Contrast-Centroid Loss) model, combining the techniques of Convolutional Neural Networks (CNN), self-supervised contrastive learning, and Centroid Loss, aimed at the identification of corn leaf diseases. Initially, RGB-enhanced feature processing was applied to the image data of corn leaves to provide richer visual information and improve the model′s recognition and classification capabilities. Subsequently, ResNet18 was used for pre-training, learning universal visual feature representations through contrastive learning. Based on MoCo′s momentum contrast learning mechanism, the key encoder is maintained and updated to keep the feature consistency of the query encoder. The combination of InfoNCE loss function and Centroid Loss forms a balanced total loss function, further enhancing intra-class compactness and inter-class separability in the feature space.
Experimental results show that the CMC model has significant advantages in handling complex and variable agricultural image data. Compared to traditional convolutional neural networks and other self-supervised learning models, the CMC model more effectively identifies different types of corn leaf diseases. Centroid-Based Enhancement played an important role in this study. By introducing Centroid Loss, the CMC model can better learn the centroid of each category, bringing similar samples closer in the feature space while keeping different categories separated, thereby enhancing intra-class aggregation and inter-class differentiation.
To further enhance the performance of the CMC model, future research will expand the dataset and the range of diseases, including images taken at different growth stages and under various environmental conditions, and explore the identification of other crop diseases. Additionally, interdisciplinary collaboration in smart agriculture, such as integrating drones, agricultural robots, and mobile applications (APPs), will significantly improve the efficiency and accuracy of agricultural monitoring and disease diagnosis, promoting the development of smart agriculture. These improvements will greatly enhance the model′s practicality and accuracy, providing advanced technical support for agricultural monitoring and disease diagnosis. | en_US |