Deep Learning Predicts Rapid Over-softening and Shelf Life in Persimmon Fruits
نویسندگان
چکیده
In contrast to the progress in research on physiological disorders relating shelf life fruit crops, it has been difficult non-destructively predict their occurrence. Recent high-tech instruments have gradually enabled non-destructive predictions for various some while there are still issues terms of efficiency and costs. Here, we propose application a deep neural network (or simply learning) simple RGB images severe disorder persimmon, rapid over-softening. With 1,080 ‘Soshu’ persimmon fruits, three convolutional networks (CNN) were examined over-softened fruits with binary classification date softening. All CNN models worked successfully controls > 80% accuracy using multiple criteria. Furthermore, prediction values confidence) correlated Although features by learning thought be black box conventional standards, recent feature visualization methods “explainable” allowed identification relevant regions original images. We applied Grad-CAM, Guided backpropagation, layer-wise relevance propagation (LRP), find early symptoms CNNs fruits. The focus tended color unevenness surface fruit, especially peripheral regions. These results suggest that frameworks could potentially provide new insights into which researchers unaware.
منابع مشابه
Efffect of storage temperature and container type on storage and shelf life of persimmon fruits
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ژورنال
عنوان ژورنال: The Horticulture Journal
سال: 2022
ISSN: ['2189-0110', '2189-0102']
DOI: https://doi.org/10.2503/hortj.utd-323