Grading bunch tightness for grape by multiperspective imaging approach coupled with multivariate classification methods

Abstract

AbstractBunch's tightness is a significant trait in determining tablegrape and wine quality. But standard determination of tightness needs the trained technicians and complex manual measurements. This study is to validate the feasibility of multiperspective imaging analysis combined with multivariate modeling methods to grade the grape bunch compactness in a rapid, automatic, and objective approach. The detection system was developed by multiperspective projection imaging structure, where mono‐camera was used to simultaneously capture the whole surface of grape bunch by two mirrors' reflections. Through series of image processes, 23 features that described bunch regions, contours, the cluster density, and so forth, were digitalized and normalized for linear projections by principal component analysis (PCA) and partial least square (PLS), respectively. Then the projected latent components were obtained and were taken as input of the developed classifiers, to qualitatively assess the bunch's tightness. Results indicated these components compressed more information from PLS than PCA projection, and PLS‐support vector machine classifier outperformed best with 88.33% accuracy rate. It was concluded that the multiperspective simultaneously imaging approach coupled with image process and pattern recognition technology could be applied to evaluate the external tightness of grape bunch. Moreover, it has the potential to be a good reference for rapid grade of berry cluster.Practical applicationsThe proven performance of machine vision coupled with pattern recognition could be implemented in automatic measurement system to rapidly and objectively classify the tightness of postharvest grape bunch or other berry‐clustered fruit. This work could be a useful application for the specific consumptions to distinguish the tight from the loose on account of that the tightness plays a significant role in determining table grape and wine quality.