CHERRY SEGMENTATION AND IDENTIFICATION BASED ON DeepLabV3 IN COMPLEX ORCHARD ENVIRONMENT

Abstract

Aiming at the problems of less research on cherry segmentation and identification, with slow recognition speed and low classification accuracy in agricultural products, a method based on DeepLabV3 was proposed to realize the rapid segmentation and identification of cherry in complex orchard environment. Complex environment mainly includes front lighting, back lighting, cloudy and rainy days, single fruit, multi fruit, fruit overlap, and branch and leaf occlusion. This model proposed the Atrous Spatial Pyramid Pooling (ASPP) module to effectively extract multi-scale contextual information, and solved the problem of target segmentation at multiple scales. The obtained data was divided into training, validation and testing sets in 7:1:2 ratios, and the residual network 50 (ResNet50) was selected as backbone of the DeepLabV3. Experimental results show that the algorithm in this paper can segment cherry quickly and accurately, the mean intersection over union (MIoU) was 91.06%, the mean pixel accuracy (MPA) was 93.05%, and the kappa coefficient was 0.89, which was better than fully convolutional networks (FCN), SegNet, DeepLabV1 and DeepLabV2. It is demonstrated that this study can provide technical support for intelligent segmentation of agricultural products.