ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits.

ABSTRACT: Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces "ScabyNet", an image processing approach combining color-morphology analysis with deep learning techniques. ScabyNet estimates tuber quality traits and accurately detects and quantifies CS severity levels from color images. It is presented as a standalone application with a graphical user interface comprising two main modules. One module identifies and separates tubers on images and estimates quality-related morphological features. In addition, it enables the extraction of tubers as standard tiles for the deep-learning module. The deep-learning module detects and quantifies the scab

SUBMITTER: Leiva F 

PROVIDER: S-EPMC10787732 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature