COMPARATIVE ANALYSIS OF DEEP LEARNING ARCHITECTURES FOR GRAPE CLUSTER INSTANCE SEGMENTATION

Abstract

The grape cluster identification and its segmentation for the sake of total weight prediction task of wine yard shows the need of segmentation atomization with better accuracy. The challenge of grape cluster segmentation is considered to provide solution using deep neural network models such as YOLO v3, Mask RCNN, U-net. This paper contributes in terms of the modified U-net model for the segmentation of grape clusters using training and testing strategy for the validation of the results. The results are obtained for the accuracy of the classification of pixels as part of grape cluster or outside of clusters and comparative results show improvement in segmentation using modified U-net. The accuracy, precision and recall analysis is performed and comparatively proposed model shows better results