Deep multi-scale dual-channel convolutional neural network for Internet of Things apple disease detection

It is difficult to identify similar apples diseases due to the complicated changes in color and texture of diseased parts. In order to solve this problem, an Internet of Things (IoT) system for apple disease detection based on deep multi-scale dual-channel convolutional neural network (DMCNN) was proposed in this paper. Firstly, the image was transformed into HSV and RGB color subspaces through color space transformation, the color and texture features of apple diseases were extracted respectively. Then, (1) The Color Analysis Subnet of HSV color subspace was proposed to extract the color features. (2) The Texture Analysis Subnet of RGB color subspace was proposed to extract the texture features. The attention mechanism optimized by double-factor weight was used to effectively improve the capability of texture feature extraction of this subnet. (3) DMCNN was constructed through a cross-fusing mechanism of homologous features. It can fuse the features that are extracted by color and texture analysis subnets, thereby improving its expression. Finally, an IoT detection system was constructed by combining hardware and detection model. The Experiments conducted on our self-collected database (Images were taken in natural light using a Nikon camera. After data enhancement, 1674 in total, 1341 training, 332 testing) and other scholars' database (After data enhancement, 3336 in total, 2669 training, 667 testing) show that the proposed DMCNN has attained a high detection rate that exceeds 99.5% on average.

Automated summary

This paper proposes an Internet of Things (IoT) system for apple disease detection based on deep multi-scale dual-channel convolutional neural network (DMCNN). It extracts color and texture features of apple diseases by transforming images into HSV and RGB color subspaces. The experiments show that the proposed DMCNN achieves a high detection rate of over 99.5%.