Predicting the ripening time of ‘Hass’ and ‘Shepard’ avocado fruit by hyperspectral imaging
نویسندگان
چکیده
Abstract Predicting the ripening time of avocado fruit accurately could improve storage and decrease food waste. No reasonable method exists for predicting postharvest during transport, or retail display. Here, hyperspectral imaging ranging from 388 to 1005 nm with 462 bands was applied 316 ‘Hass’ 160 ‘Shepard’ mature, unripe predict how many days it took individual become ripe. Three models were developed using partial least squares regression (PLSR), deep convolutional neural network (DCNN) DCNN classification. Our PLSR provided coefficients determination (R 2 ) 0.76 0.50 root mean squared errors (RMSE) 1.20 1.13 fruit, respectively. The DCNN-based produced similar results R 0.77 0.59, RMSEs 1.43 0.94 prediction accuracies classification models, respectively, 67.28% 1.52 64.06% 1.03 ‘Shepard’. study demonstrates that spectral reflectance skin provides adequate information and, thus, has potential processing reduce losses fruit.
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ژورنال
عنوان ژورنال: Precision Agriculture
سال: 2023
ISSN: ['1385-2256', '1573-1618']
DOI: https://doi.org/10.1007/s11119-023-10022-y