A two-stage image segmentation method for harvest order decision of wood ear mushroom

Abstract

This study proposes a method for determining the appropriate harvesting order for densely growing wood ear mushrooms by recognizing their growth stages and harvesting priorities from depth images obtained from a stereo camera. We aim to minimize crop damage and improve the quality of harvested crops during the harvesting of densely growing crops using a robot arm. The proposed two-stage method consists of two models—one of the models to recognize priority harvest regions, and the other model to identify individual wood ear mushroom regions and growth stages. The final harvesting order is determined based on the outputs of these models. The models were trained using simulated CGI data of wood ear mushroom growth. The experimental results show that the appropriate harvesting order can be outputted in 57.5% of the cases for the 40 sets of test data. The results show that it is possible to determine the harvesting order of dense wood ear mushrooms based solely on depth images. However, there is still room for improvement in operations in actual environments. Further work is needed to enhance the method’s robustness and accuracy.