An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things (IoT) technology has evolved, smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments. In this paper, we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots, which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters. First, the speeded-up robust feature (SURF) extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system. Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image, where the edge contour and the height information of crop row are fused to extract the navigation parameters (θ, d) based on the model of a smart agricultural robot. Finally, the five navigation network instruction sets are designed based on the navigation angle θ and the lateral distance d, which represent the basic movements for a certain type of smart agricultural robot working in a field. Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations, and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.