Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data

Abstract

To create a digital unmanned orchard with automation of “picking, load and transportation” in the hills and mountains, it is vital to determine a cargo-carrying situation and monitor the real-time transport conditions. In this paper, a cargo-carrying analysis system based on RGB-D data was developed, taking citrus transportation as the scenario. First, the improved YOLOv7-tiny object detection algorithm was used to classify and obtain 2D coordinate information on the carried cargo, and a region of interest (ROI) was obtained from the coordinate information for cargo height measurement. Second, 3D information was driven by 2D detection results using fewer computing resources. A depth map was used to calculate the height values in the ROI using a height measurement model based on spatial geometry, which obtained the load volume of the carried cargo. The experimental results showed that the improved YOLOv7 model had an accuracy of 89.8% and an average detection time of 63 ms for a single frame on the edge-computing device. Within a horizontal distance of 1.8 m from the depth camera, the error of the height measurement model was ±3 cm, and the total inference time of the overall method was 75 ms. The system lays a technical foundation for generating efficient operation paths and intelligently scheduling transport equipment, which promote the intelligent and sustainable development of mountainous agriculture.