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Article

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

by
Zhen Li
1,2,3,
Yuehuai Zhou
1,
Chonghai Zhao
1,
Yuanhang Guo
1,
Shilei Lyu
1,2,3,
Jiayu Chen
1,
Wei Wen
3,4 and
Ying Huang
1,5,*
1
College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
2
Pazhou Lab, Guangzhou 510330, China
3
Mechanization Laboratory of National Modern Agriculture (Citrus) Industrial Technology System, South China Agricultural University, Guangzhou 510642, China
4
Engineering Fundamental Teaching and Training Center, South China Agricultural University, Guangzhou 510642, China
5
Automatic Control School, Liuzhou Railway Vocational Technical College, Liuzhou 545616, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(10), 6059; https://doi.org/10.3390/app13106059
Submission received: 7 March 2023 / Revised: 26 April 2023 / Accepted: 14 May 2023 / Published: 15 May 2023
(This article belongs to the Section Agricultural Science and Technology)
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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.
Keywords: orchard transporter; cargo-carrying situation; RGB-D; YOLO; 3D vision technology orchard transporter; cargo-carrying situation; RGB-D; YOLO; 3D vision technology

Share and Cite

MDPI and ACS Style

Li, Z.; Zhou, Y.; Zhao, C.; Guo, Y.; Lyu, S.; Chen, J.; Wen, W.; Huang, Y. Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data. Appl. Sci. 2023, 13, 6059. https://doi.org/10.3390/app13106059

AMA Style

Li Z, Zhou Y, Zhao C, Guo Y, Lyu S, Chen J, Wen W, Huang Y. Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data. Applied Sciences. 2023; 13(10):6059. https://doi.org/10.3390/app13106059

Chicago/Turabian Style

Li, Zhen, Yuehuai Zhou, Chonghai Zhao, Yuanhang Guo, Shilei Lyu, Jiayu Chen, Wei Wen, and Ying Huang. 2023. "Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data" Applied Sciences 13, no. 10: 6059. https://doi.org/10.3390/app13106059

APA Style

Li, Z., Zhou, Y., Zhao, C., Guo, Y., Lyu, S., Chen, J., Wen, W., & Huang, Y. (2023). Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data. Applied Sciences, 13(10), 6059. https://doi.org/10.3390/app13106059

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