Rating pome fruit quality traits using deep learning and image processing.

ABSTRACT: Quality assessment of pome fruits (i.e. apples and pears) is used not only for determining the optimal harvest time but also for the progression of fruit-quality attributes during storage. Therefore, it is typical to repeatedly evaluate fruits during the course of a postharvest experiment. This evaluation often includes careful visual assessments of fruit for apparent defects and physiological symptoms. A general best practice for quality assessment is to rate fruit using the same individual rater or group of individual raters to reduce bias. However, such consistency across labs, facilities, and experiments is often not feasible or attainable. Moreover, while these visual assessments are critical empirical data, they are often coarse-grained and lack consistent objective criteria. Granny,

SUBMITTER: Nguyen NH 

PROVIDER: S-EPMC11461139 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature