Plant pest and disease lightweight identification model by fusing tensor features and knowledge distillation

Abstract

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Plant pest and disease management is an important factor affecting the yield and quality of crops, and due to the rich variety and the diagnosis process mostly relying on experts’ experience, there are problems of low diagnosis efficiency and accuracy. For this, we proposed a Plant pest and Disease Lightweight identification Model by fusing Tensor features and Knowledge distillation (PDLM-TK). First, a Lightweight Residual Blocks based on Spatial Tensor (LRB-ST) is constructed to enhance the perception and extraction of shallow detail features of plant images by introducing spatial tensor. And the depth separable convolution is used to reduce the number of model parameters to improve the diagnosis efficiency. Secondly, a Branch Network Fusion with Graph Convolutional features (BNF-GC) is proposed to realize image super-pixel segmentation by using spanning tree clustering based on pixel features. And the graph convolution neural network is utilized to extract the correlation features to improve the diagnosis accuracy. Finally, we designed a Model Training Strategy based on knowledge Distillation (MTS-KD) to train the pest and disease diagnosis model by building a knowledge migration architecture, which fully balances the accuracy and diagnosis efficiency of the model. The experimental results show that PDLM-TK performs well in three plant pest and disease datasets such as Plant Village, with the highest classification accuracy and F1 score of 96.19% and 94.94%. Moreover, the model execution efficiency performs better compared to lightweight methods such as MobileViT, which can quickly and accurately diagnose plant diseases.

Keywords

• image classification
• spatial tensor
• knowledge distillation
• light weighting
• graph convolutional neural networks