Apple disease leaf detection based on multi-scale feature fusion network

Accurate detection of apple leaf diseases is of great significance for improving apple production and quality. Aiming at the problem that existing apple leaf disease detection models cannot make full use of information for given images, resulting in poor detection performance, an apple disease leaf detection based on multi-scale feature fusion network was proposed. Vgg-16 network was firstly improved using depth-separable convolution, and improved network was used as a global feature extractor for apple leaf disease pictures. Secondly, Swin Transformer network was used as a local feature extractor. Next, the multi-scale feature fusion network was proposed to fuse local and global features to construct multi-scale features. Finally, the fusion multi-scale features were used as input to a fully connected network for the detection of apple disease leaves. The experimental results showed that the proposed model could achieve 93.98% accuracy, 94.11% precision, 93.93% recall and 94.62% F₁ value. Compared with the current mainstream apple disease leaf detection models, it was highly competitive in terms of detection accuracy and the amount of model parameters to be calculated.