Rating pome fruit quality traits using deep learning and image processing

Abstract

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, is a tool designed for rating fruit using machine‐learning and image‐processing to address rater bias and improve resolution. Additionally, Granny supports backward compatibility by providing ratings compatible with long‐established standards and references, promoting research program continuity. Current Granny ratings include starch content assessment, rating levels of peel defects, and peel color analyses. Integrative analyses enhanced by Granny's improved resolution and reduced bias, such as linking fruit outcomes to global scale ‐omics data, environmental changes, and other quantitative fruit quality metrics like soluble solids content and flesh firmness, will further enrich our understanding of fruit quality dynamics. Lastly, Granny is open‐source and freely available.

Keywords

• machine learning
• pome fruit
• trait prediction