MSCR-FuResNet: A Three-Residual Network Fusion Model Based on Multi-Scale Feature Extraction and Enhanced Channel Spatial Features for Close-Range Apple Leaf Diseases Classification under Optimal Conditions

MSCR-FuResNet: A Three-Residual Network Fusion Model Based on Multi-Scale Feature Extraction and Enhanced Channel Spatial Features for Close-Range Apple Leaf Diseases Classification under Optimal Conditions

Blog Article

The precise and automated diagnosis of apple leaf diseases is essential for maximizing apple Fall Protection - Mats yield and advancing agricultural development.Despite the widespread utilization of deep learning techniques, several challenges persist: (1) the presence of small disease spots on apple leaves poses difficulties for models to capture intricate features; (2) the high similarity among different types of apple leaf diseases complicates their differentiation; and (3) images with complex backgrounds often exhibit low contrast, thereby reducing classification accuracy.To tackle these challenges, we propose a three-residual fusion network known as MSCR-FuResNet (Fusion of Multi-scale Feature Extraction and Enhancements of Channels and Residual Blocks Net), which consists of three sub-networks: (1) enhancing detailed feature extraction through multi-scale feature extraction; (2) improving the discrimination of similar features by suppressing insignificant channels Bureau and pixels; and (3) increasing low-contrast feature extraction by modifying the activation function and residual blocks.The model was validated with a comprehensive dataset from public repositories, including Plant Village and Baidu Flying Paddle.

Various data augmentation techniques were employed to address class imbalance.Experimental results demonstrate that the proposed model outperforms ResNet-50 with an accuracy of 97.27% on the constructed dataset, indicating significant advancements in apple leaf disease recognition.