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Tracking and Counting of Tomato at Different Growth Period Using an Improving YOLO-Deepsort Network for Inspection Robot

文献类型： 外文期刊

作者： Ge, Yuhao 1 ; Lin, Sen 1 ; Zhang, Yunhe 1 ; Li, Zuolin 1 ; Cheng, Hongtai 1 ; Dong, Jing 2 ; Shao, Shanshan 3 ; Zhang, Jin 4 ; Qi, Xiangyu 1 ; Wu, Zedong 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

2.Nongxin Sci & Technol Beijing Co Ltd, Beijing 100097, Peoples R China

3.Heze Zhengbang Holding Grp Co Ltd, Heze 274007, Peoples R China

4.China Datang Overseas Investment Co Ltd, Beijing 100052, Peoples R China

关键词： facility agriculture; deep learning; lightweight optimization; yield forecast; object tracking; tomato

期刊名称：MACHINES （ 影响因子：2.899； 五年影响因子：3.09 ）

ISSN：

年卷期： 2022 年 10 卷 6 期

页码：

收录情况： SCI

摘要： To realize tomato growth period monitoring and yield prediction of tomato cultivation, our study proposes a visual object tracking network called YOLO-deepsort to identify and count tomatoes in different growth periods. Based on the YOLOv5s model, our model uses shufflenetv2, combined with the CBAM attention mechanism, to compress the model size from the algorithm level. In the neck part of the network, the BiFPN multi-scale fusion structure is used to improve the prediction accuracy of the network. When the target detection network completes the bounding box prediction of the target, the Kalman filter algorithm is used to predict the target's location in the next frame, which is called the tracker in this paper. Finally, calculate the bounding box error between the predicted bounding box and the bounding box output by the object detection network to update the parameters of the Kalman filter and repeat the above steps to achieve the target tracking of tomato fruits and flowers. After getting the tracking results, we use OpenCV to create a virtual count line to count the targets. Our algorithm achieved a competitive result based on the above methods: The mean average precision of flower, green tomato, and red tomato was 93.1%, 96.4%, and 97.9%. Moreover, we demonstrate the tracking ability of the model and the counting process by counting tomato flowers. Overall, the YOLO-deepsort model could fulfill the actual requirements of tomato yield forecast in the greenhouse scene, which provide theoretical support for crop growth status detection and yield forecast.

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