Predicting the ripening time of ‘Hass’ and ‘Shepard’ avocado fruit by hyperspectral imaging

Abstract

Predicting the ripening time of avocado fruit accurately could improve fruit storage and decrease food waste. No reasonable method exists for predicting the postharvest ripening time of avocado fruit during transport, storage or retail display. Here, hyperspectral imaging ranging from 388 to 1005 nm with 462 bands was applied to 316 ‘Hass’ and 160 ‘Shepard’ mature, unripe avocado fruit to predict how many days it took for individual fruit to become ripe. Three models were developed using partial least squares regression (PLSR), deep convolutional neural network (DCNN) regression and DCNN classification. Our PLSR models provided coefficients of determination (R2) of 0.76 and 0.50 and root mea..