Research on agricultural machinery behavior pattern recognition method based on computer vision

This study focuses on the use of computer vision technology for agricultural machinery behavioral pattern recognition, based on deep learning and pattern recognition technology, a model architecture integrating ViT (VisionTransformer) and ResUnet is constructed to intelligently parse large-scale agricultural machinery trajectory data. The study explores the effective methods of agricultural machinery behavior pattern recognition in an all-round way, from the analysis and preprocessing of dataset, model construction and optimization to the evaluation of simulation results. Comparative experiments show that the proposed model significantly outperforms traditional methods in terms of accuracy, precision, recall and F1 score, providing precise data support for agricultural management and promoting the development of agriculture in the direction of intelligence and sustainability.