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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.

As the new generation of artificial intelligence (AI) continues to evolve, weather big data and statistical machine learning (SML) technologies complement each other and are deeply integrated to significantly improve the processing and forecasting accuracy of fishery weather. Accurate fishery weather services play a crucial role in fishery production, serving as a great safeguard for economic benefits and personal safety, enabling fishermen to carry out fishery production better, and contributing to the sustainable development of the fishery industry. The objective of this paper is to offer an understanding of the present state of research and development in SML technology for simulating and forecasting fishery weather. Specifically, we analyze the current state of research and technical features of SML in weather and summarize the applications of SML in simulation and forecasting of fishery weather, which mainly include three aspects: fishery weather scenario generation, fishery weather forecasting, and fishery extreme weather warning. We also illustrate the main technical means and principles of SML technology. Finally, we summarize the most advanced SML fields and provide an outlook on their application value in the field of fishery weather.