A Computer Vision-Based Anti-Lodging Control System for Agricultural Machinery

The innovation of computer vision algorithms has driven the emergence of intelligent new agricultural machinery production modes. With the development of agricultural modernization, technology-guided upgrading of agricultural equipment has become a critical component in alleviating the contradiction between labor shortages and yield demands. This study focused on the application of computer vision algorithm fusion technology in the intelligent transformation of agricultural machinery, aiming to mitigate the consequences of crop lodging as a significant yield-reduction factor in agricultural production. Through model comparison and data analysis, a binocular vision measurement system optimized for farmland environments and a YOLOv8 image deep learning algorithm were determined, while hardware construction completed the modified upgrade platform for traditional agricultural machinery. Compared with existing technologies, the system integrated diverse visual data to create a comprehensive end-to-end platform featuring improved binocular depth and measurement algorithms, lodging region identification with enhanced measurement sensitivity and accuracy, a selfstabilizing weighted-averaging data platform with path prediction, universal mechanical control drives, and user-friendly operation. Using binocular cameras and target cameras as image capture sources combined with vehicle-mounted sensors, the system calculated and executed orderly control over the harvester header height and throttle powertrain to achieve lodged crop harvesting. Simulations and experiments from laboratory to field environments preliminarily verified the recognition accuracy and control performance of the algorithm, reduced crop losses by more than 73.5%.