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

Abstract

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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