Crop pest image recognition based on the improved ViT method

The crop pests and diseases in agriculture is one of the most important reason for the reduction of bulk grain and oil crops and the decline of fruit and vegetable crop quality, which threaten macroeconomic stability and sustainable development. However, the recognition method based on manual and instruments has been unable to meet the needs of scientific research and production due to its strong subjectivity and low efficiency. The recognition method based on pattern recognition and deep learning can automatically fit image features, and use features to classify and predict images. This study introduced the improved Vision Transformer (ViT) method for crop pest image recognition. Among them, the region with the most obvious features can be effectively selected by block partition. The self-attention mechanism of the transformer can better excavate the special solution that is not an obvious lesion area. In the experiment, data with 7 classes of examples are used for verification. It can be illustrated from results that this method has high accuracy and can give full play to the advantages of image processing and recognition technology, accurately judge the crop diseases and pests category, provide method reference for agricultural diseases and pests identification research, and further optimize the crop diseases and pests control work for agricultural workers in need.