A Shine Muscat Grape Berry Detection and Grape Cluster Compactness Estimation for Assessment of Grape Quality Based on Instance Segmentation Methods

Abstract

Highlights Mask R-CNN ResNet-101 was analyzed to have the highest detection accuracy for grape berries. Grape cluster compactness is one of the important factors that determines the quality of grapes. Indicator DGC was used to intuitively calculate the compactness of grape cluster. An average error of 1.6 mm occurred with the verification of diameter estimation algorithm. Abstract. Consumption and cultivation of grapes are constantly increasing because they are known as alkaline foods, which relieve fatigue by accelerating carbohydrate metabolism in the human body. Among these grapes, a Shine Muscat doesn’t have the bitter taste of ordinary grapes, has low acidity, and has a crunchy texture, which results in growing interest in modern society. As the consumption of Shine Muscat increases, an automated process to save labor and time becomes essential. Particularly, one challenge that needs to be solved is the determination of grape quality based solely on visual evaluations by experts, which can result in inconsistent pricing for consumers. In this study, an algorithm was adopted to detect the grape berries from a bunch of single grapes by acquiring RGB images of the harvested Shine Muscat. Mask R-CNN, a convolutional neural network-based image segmentation technique, was used with various backbones to compare and evaluate the performance of each model. Results showed that Mask R-CNN ResNet 101 had the highest AP (Average Precision) value of 0.961 among all models. In addition, indexes such as the size (diameter) of each grape berry, the area of the grape on the image, and the area of the empty space that are required to find the compactness of the grape cluster are obtained. In particular, it was analyzed that the average error value (mm) and percent error (%) of the diameter estimation algorithm developed in this study were 1.60 mm and 4.79%, respectively. Keywords: Keywords., Density of Grape Cluster (DGC), Diameter estimation, Grape berry, Grape cluster compactness, Mask R-CNN, Object detection.