DETECTION OF COMMON SCAB ON POTATO TUBERS USING SEMANTIC SEGMENTATION ON COLOR AND NEAR INFRARED IMAGES

Abstract

s automation of crop sorting has been widely implemented due to the decrease in the farming population, complete elimination of potato common scab tubers is required especially in the sorting of seed potatos. In this study, we aimed to detect the area of common scab on the surface of potato tubers by inputting two types of images: a conventional color image and a near-infrared (NIR) image at 960 nm. The common scab areas were manually labeled, and the segmentation model based on semantic segmentation was compared with a conventional model based on principal component analysis and support vector machines (PCA-SVM). The results showed that semantic segmentation showed higher accuracy than PCA-SVM, and the common scab areas were almost detected. In addition, higher segmentation accuracy was obtained with four inputs of RGB and NIR images than with only color images, suggesting the potential of NIR image input for common scab segmentation.