Segmentation and weight prediction of grape ear based on SFNet-ResNet18

Abstract

In this paper, the segment and weight prediction problems are investigated for ear of grape based on deep learning technologies. The image datum is collected from ZaoHeiBao grape in a greenhouse by camera. The grape ear target segmentation model is constructed by cross combining three backbone networks (ResNet18, ResNet50, and ResNet101) and four deep learning semantic segmentation networks (SFNet, GCNet, EMANet, and Deeplabv3). The experimental results show that for the SFNet-ResNet18 model, whose structural size is 52.68MB, the mean Intersection over Union (mIoU) is [Formula: see text], the mean Pixel Accuracy (mPA) is [Formula: see text], and the average segmentation speed of the image ([Formula: see text]) is 0.217s. Therefore, the performance of the SFNet-ResNet18 model outperforms other combined network models and is selected to segment grape ears. Furthermore, on the basis of the segmentation results of grape ears by using the SFNet-ResNet18 model, the grape ear weight is predicted by adopting fractional regression model. The value of [Formula: see text] is 0.8903, which means that the selected model could accurately predict the weight of grape ears. The proposed method can not only segment the grape ears and accurately predict the weight of the grape ears, but also provide theoretical and technical support for grape yield prediction.

Keywords

• Ears of grape
• segmentation
• weight prediction
• SFNet-ResNet18