A lightweight crop pest identification method based on multi-head attention

Objective To solve the problems that the current pest identification method has many parameters, a large amount of calculation and is difficult to deploy embedded devices at the edge, so as to realize accurate identification of crop pests and diseases, and improve crop yield and quality.

Method A lightweight convolutional neural network called multi-head attention to convolutional neural network (M2CNet) was proposed. M2CNet adopted hierarchical pyramid structure. Firstly, a local capture block was constructed by combining depth separable residual and cyclic fully connected residual to capture short-range information. Secondly, a lightweight global capture block was constructed by combining global subsampling attention and lightweight feedforward network to capture long-distance information. Three variants, namely M2CNet-S, M2CNet-B, and M2CNet-L, were proposed by M2CNet to meet different edge deployment requirements.

Result M2CNet-S/B/L had parameter sizes of 1.8M, 3.5M and 5.8M, and floating point operations of 0.23G, 0.39G, and 0.60G, respectively. M2CNet-S/B/L achieved top5 accuracy greater than 99.7% and top1 accuracy greater than 95.9% in PlantVillage disease dataset, and top5 accuracy greater than 88.4% and top1 accuracy greater than 67.0% in IP102 pest dataset, outperforming models of the same level in comparison.

Conclusion Effective identification of crop diseases and pests can be achieved by this method, and it provides valuable references for edge engineering deployment.