イチゴ栽培における農作業支援ロボットのための植物体の識別

抄録

This paper presents a method for identifying strawberry plants (fruits, flowers, calyxes, trusses) with the aim of developing a multi-functional agricultural work assist robot for small-scale facility. We focus on semantic segmentation by deep learning. Some pre-trained CNNs (ResNet-18, ResNet-50, Xception and MobileNetV2) are compared as the initial value of weights for feature extraction in DeepLabV3+. In this study, the ResNet-50 is used as the backbone network of DeepLabV3+. In addition, we propose a method of performing post-processing based on the shape characteristics of plants on the results of semantic segmentation. The proposed method is evaluated using the images obtained in the actual strawberry farm, and its accuracy is evaluated by mean IoU. The effectiveness of the proposed method is shown by comparing with and without post-processing. The maximum was 0.731 for fruits and the minimum was 0.294 for trusses (0.643 and 0.199 respectively without post-processing).