# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 6 Conclusions  
In this study, we designed a cargo-carrying analysis system for mountain orchard transporters based on RGB-D data. We used an Intel RealSense D455 depth camera to capture RGB-D images of the trailer. Then, the RGB images were used to train the improved YOLOv7-tiny algorithm to classify and obtain 2D coordinate information. We fused the 2D coordinate information and the depth map to determine the ROI for height measurement. We used a height measurement model to calculate the sampled points in the ROI to determine the number of layers in which the cargo was carried. Our conclusions were as follows:  
1. Compared to 3D data, 2D images are easily available and computationally efficient. In this study, we used a lightweight detection method to detect 2D images and produce the classification and localization. The 3D information was driven by 2D detection results to determine the load volume using fewer computing resources.
2. Focal loss and the EagleEye pruning method were used to improve YOLOv7-tiny, which allowed the model to converge more quickly and speed up detection. The mean average precision (mAP) of the proposed method was 89.8%, which was 8.2%, 6.9%, and 1.6% higher than those of Faster-RCNN, RetinaNet-Res50, and YOLOv5s, respectively. The model had 6.7 GFLOPs, and the detection speed was 63 ms/img on the embedded platform, a Jetson Nano.
3. The spatial-geometry-based height measurement model proposed in this paper had a height measurement error of less than 3 cm at a range of 1.8 m horizontally from the depth camera, which met the requirements for load analysis in mountain orchards.
4. The method determined the load volume when the transporter was carrying one or two layers of fruit baskets. The total time required by the method was 75 ms/img. The method provides technical support for achieving the accurate transport of cargo in mountain orchards and is valuable for rationally dispatching transport equipment and for operational safety.  
Conceptualization, Z.L. and Y.H.; methodology, Z.L. and Y.Z.; software, Y.Z., C.Z. and Y.G.; validation, Y.H., S.L. and C.Z.; formal analysis, Y.Z.; investigation, Z.L. and Y.Z.; resources, Y.Z. and J.C.; data curation, Y.Z.; writing--original draft preparation, Z.L. and Y.Z.; writing--review and editing, Z.L., Y.Z. and Y.H.; visualization, C.Z., Y.G., W.W. and J.C.; supervision, Z.L. and Y.H.; project administration, Z.L. and Y.H.; funding acquisition, Z.L., S.L. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (31971797 and 32271997); the Subtopics of National Key R&D Program Projects (2020 YFD1000107); the China Agriculture Research System of MOF and MARA (CARS-26); the General Program of the Guangdong Natural Science Foundation (2021A1515010923); and the Guangdong Provincial Strategy for Rural Revitalization ([PERSON] [2021] No. 37).  
Not applicable.  
The authors would like to thank the anonymous reviewers for their criticism and suggestions.  
The authors declare no conflict of interest.