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Method for the real-time detection of tomato ripeness using a phenotype robot and RP-YolactEdge

文献类型： 外文期刊

作者： Wang, Yuanqiao 1 ; Gou, Wenbo 2 ; Wang, Chuanyu 2 ; Fan, Jiangchuan 2 ; Wen, Weiliang 2 ; Lu, Xianju 2 ; Guo, Xinyu 3 ; Zhao, Chunjiang 1 ;

作者机构： 1.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Shaanxi, Peoples R China

2.China Natl Engn Res Ctr Informat Technol Agr NERCI, Beijing 100097, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

4.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China

关键词： instance segmentation; phenotype robot; tomato; greenhouse-based plant phenotyping; ripeness detection

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.2； 五年影响因子：2.5 ）

ISSN： 1934-6344

年卷期： 2024 年 17 卷 2 期

页码：

收录情况： SCI

摘要： In order to address the challenge of non-destructive detection of tomato fruit ripeness in controlled environments, this study proposed a real-time instance segmentation method based on the edge device. This method combined the principles of phenotype robots and machine vision based on deep learning. A compact and remotely controllable phenotype detection robot was employed to acquire precise data on tomato ripeness. The video data were then processed by using an efficient backbone and the FeatFlowNet structure for feature extraction and analysis of key-frame to non-key-frame mapping from video data. To enhance the diversity of training datasets and the generalization of the model, an innovative approach was chosen by using random enhancement techniques. Besides, the PolyLoss optimization technique was applied to further improve the accuracy of the ripeness multi-class detection tasks. Through validation, the method of this study achieved real-time processing speeds of 90.1 fps (RTX 3070Ti) and 65.5 fps (RTX 2060 S), with an average detection accuracy of 97% compared to manually measured results. This is more accurate and efficient than other instance segmentation models according to actual testing in a greenhouse. Therefore, the results of this research can be deployed in edge devices and provide technical support for unmanned greenhouse monitoring devices or fruit-picking robots in facility environments.

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