Evaluation of Surface Damage of Strawberry Grasped by Manipulator Based on Vision and Hyperspectral Data Analysis

Abstract

In the current study, an accurate strategy was developed to detect the surface damage of the strawberries, because the existing indicators for evaluating surface damage are single, resulting in low accuracy of judgment for damage detection. Strawberry surface damage can be divided into three specific categories: outline depression, surface oxidation, and surface damage. The methodologies employed in this study are experimental analysis and comparative validation. The outline, area of surface damage, and surface reflectance of strawberries were analyzed using visual and hyperspectral data. Principal component analysis was used to evaluate the damage characteristics comprehensively. A four-finger rigid manipulator having one degree of freedom was selected, and 280 samples were analyzed (the manipulator grasped 85% of them, and 15% were not treated). The accuracy of damage detection based on the outlines, area of surface damage, and surface reflectance of strawberries was 88.75%, 91.25%, and 75%, respectively. The evaluation method proposed in this paper improved detection accuracy by 10.79%, 7.76%, and 31.1%, respectively. Therefore, this method could contribute to the design of manipulators to further improve fruit production efficiency.