Determining Strawberries’ Varying Maturity Levels by Utilizing Image Segmentation Methods of Improved DeepLabV3+

Abstract

Aiming to determine the inaccurate image segmentation of strawberries with varying maturity levels due to fruit adhesion and stacking, this study proposed a strawberry image segmentation method based on the improved DeepLabV3+ model. The technique introduced the attention mechanism into the backbone network and the atrous spatial pyramid pooling module of the DeepLabV3+ network, adjusted the weights of feature channels in the neural network propagation process through the attention mechanism to enhance the feature information of strawberry images, reduced the interference of environmental factors, and improved the accuracy of strawberry image segmentation. The experimental results showed that the proposed method can accurately segment images of strawberries with different maturities; the mean pixel accuracy and mean intersection over union of the model were 90.9% and 83.05%, respectively, and the frames per second (FPS) was 7.67. The method can effectively reduce the influence of environmental factors on strawberry image segmentation and provide an effective approach for accurate operation of strawberry picking robots.