Abstract
In the process of vegetable growth, leaf area phenotypic information measurement is an effective means to evaluate the growth status of vegetables. Due to the irregular leaf shape, the accuracy of leaf area measurement is low, and real-time field measurement is difficult to achieve. According to the field situation, a real-time phenotypic measurement system for vegetable leaves was designed. The vegetable leaves are collected in real-time by the detection vehicle, and the YOLACT segmentation model is used to realize the real-time measurement of vegetable leaves. By introducing the Res2Net module after adding the ECA attention mechanism into the model backbone network Resnet50, the network receptive field is expanded and the segmentation performance of the network on the leaves is improved. In the field experiment, the segmentation accuracy and detection accuracy of vegetable leaves reached 41.51% and 39.39%, respectively, and the segmentation speed was 23.10 frame/s. The results show that the designed real-time phenotypic measurement system of vegetable leaves not only meets the accurate area measurement of irregular vegetable leaves, but also realizes the real-time requirement of vegetable leaf area measurement, and improves the reliability of leaf phenotypic information to evaluate the growth status of vegetables.