Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that aims to determine the maturity level of fruits from images. The objective is to assess the degree of ripeness by identifying and distinguishing between various stages (e.g., unripe, ripe, and overripe).
데이터셋
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The data set covers recordings of ripening fruit with labels of destructive measurements (fruit flesh firmness, sugar content and overall ripeness). The labels are provided within three categories (firmness, sweetness and overall ripeness). Four measurement series were performed. Besides 1018 labeled recordings, the data set contains 4671 recordings without ripeness label. The data set contains recordings of: Avocados, Kiwis, Persimmons, Papayas, Mango Three different hyperspectral cameras were used: Specim FX 10, INNO-SPEC Redeye 1.7, Corning microHSI 410 Vis-NIR Hyperspectral Sensor