# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 1 Introduction  
Citrus and other Lingnan fruits are produced in mountain orchards in South China. Owing to the topography, the orchards are primarily distributed in the hills and mountains. The manual transportation of agricultural materials in mountain orchards is not only labor-intensive but also inefficient [1; 2; 3; 4]. To increase efficiency, monorail transporters for mountain orchards have an efficient structure, are highly reliable and safe, and are economically beneficial, so they are being widely promoted in hilly areas in China [5; 6; 7; 8]. However, as shown in Figure 1, most mountain orchards have large slopes and poor site conditions, so they are unsuitable for the manual supervision of transportation safety. Determining the transportation situation, such as not exceeding the rated capacity, is vital for safety, transporter scheduling, and operational efficiency. Therefore, we need to accurately detect the carried cargo and study the determination of the load volume of the transporter. In mountain orchards, transporters mainly carry goods such as fruit, fertilizers, and small agricultural machinery. The working environment is complex, the light changes,and the cargo overlaps. To accurately determine the cargo from 2D images, researchers have conducted studies with classic and deep-learning-based detection algorithms. For example, [PERSON] et al. [9] constructed a multi-camera-based vision system with an eye-in-hand 2D camera and a TOF camera to depalletize boxes using automatic grabbing. The images were processed using the Canny operator and Hough transform to detect the gaps between the boxes and pallets. When the surface patterns and textures of the boxes were rich, many edges were detected, resulting in detection errors. [PERSON] et al. [10] designed a cigarette parcel stacking system based on machine vision. The system extracted the Harris corner features and used the RANSAC algorithm to eliminate the mismatched points to detect cigarette parcels. The accuracy was high in a specific lighting environment but was seriously affected by light changes. Since 2012, with the development of the convolutional neural network, a deep-learning-based detection algorithm has been developed. For example, [PERSON] et al. [11] optimized RetinaNet by embedding the offset prediction to improve the performance in classification and localization, achieving the high-accuracy segmentation and detection of cartons in warehouses and logistics. [PERSON] et al. [12] introduced the ZF + RPN structure into the backbone of faster-RCNN to recognize and locate cargo in storage environments, but the height and volume of the cargo could not be determined using RGB images. [PERSON] et al. [13] introduced the MobileNetv3 network to optimize the backbone of YOLOv4 and used the attention mechanism to efficiently detect goods in warehouse environments. Therefore, the traditional algorithms are not ideal for object detection in mountain orchards. Single-stage detectors based on deep learning are more robust and can meet the detection needs in mountain orchards. To determine the cargo load volume from 3D information, RGB-D images, LiDAR devices, and 3D point cloud models have been used. For example, [PERSON] et al. [14] proposed a method for measuring the cargo volume in train carriages based on LiDAR. The method uses the relative motion between the radar and the train to perform dynamic scanning and obtain a depth map. A Delaunay triangular network is then constructed through projection to segment the point clouds on the carrier body plane to determine the load capacity. [PERSON] et al. [15] proposed a detection algorithm based on RGB-D images that uses 3D point cloud data and segmentation methods to determine the cargo's location; in combination with the grasping robot's running posture, cargo can be automatically loaded and unloaded in a warehouse. However, the method is hard to deploy on an embedded device. [PERSON] et al. [16] proposed a visual system for cartons based on RGB-D images in the logistics industry.