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YOMASK: An instance segmentation method for high-throughput phenotypic platform lettuce images

文献类型： 外文期刊

作者： Zhao, Yue 1 ; Li, Tao 2 ; Wen, Weiliang 2 ; Lu, Xianju 2 ; Yang, Si 2 ; Fan, Jiangchuan 2 ; Guo, Xinyu 2 ; Chen, Liping 1 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

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

关键词： Instance segmentation; Lettuce; High throughput; Plant phenotyping; Attention mechanism; Deep learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 230 卷

页码：

收录情况： SCI

摘要： In modern agricultural technology, the use of computer vision and deep learning methods for high-throughput phenotypic analysis of crops has become a key trend in improving agricultural production efficiency and accuracy. Especially in the area of instance segmentation, precise and efficient field crop image segmentation allows for faster and more accurate acquisition of crop field phenotypic traits, which is of significant value for disease identification, growth monitoring, and yield prediction, among other aspects. To this end, we propose a precise and efficient instance segmentation network named YOMASK. This network integrates various advanced technologies, including feature extraction, feature fusion, and attention mechanisms, optimizing the accuracy of the detection and segmentation process. Moreover, the role of each module in task execution is verified through visualization methods, enhancing the transparency and interpretability of the model's internal decision-making process. Tested on a high-throughput phenotyping platform (HTPP) for the instance segmentation task of lettuce, YOMASK exhibited outstanding performance, achieving a detection accuracy of 94.52 % and a segmentation accuracy of 95.41 %, with a model size of 19.9 MB and an inference speed of 103.9FPS. Compared to existing instance segmentation models such as Mask RCNN, SOLOv2, and YOLACT, YOMASK has shown significant improvements in both accuracy and efficiency, effectively detecting each lettuce instance in the image and generating high-quality segmentation masks for them. This research is of significant importance in the field of precision agriculture, especially in high-throughput phenotyping analysis and crop health monitoring.

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