Lightweight Corn Seed Disease Identification Method Based on Improved ShuffleNetV2

Abstract

Assessing the quality of agricultural products is an essential step to reduce food waste. The problems of overly complex models, difficult to deploy to mobile devices, and slow real-time detection in the application of deep learning in agricultural product quality assessment requiring solutions. This paper proposes a lightweight method based on ShuffleNetV2 to identify phenotypic diseases in corn seeds and conduct experiments on a corn seed dataset. Firstly, Cycle-Consistent Adversarial Networks are used to solve the problem of unbalanced datasets, while the Efficient Channel Attention module is added to enhance network performance. After this, a 7×7 depthwise convolution is used to increase the effective receptive field of the network. The repetitions of basic units in ShuffleNetV2 are also reduced to lighten the network structure. Finally, experimental results indicate that the number of model parameters are 0.913 M, the computational volume is 44.75 MFLOPs and 88.5 MMAdd, and the recognition accuracy is 96.28%. The inference speed of about 9.71 ms for each image was tested on a mobile portable laptop with only a single CPU, which provides a reference for mobile deployment.

Keywords

• image classification
• lightweight neural networks
• zea mays
• ECA attention
• CycleGAN