Detection of peach soluble solids based on near‐infrared spectroscopy with High Order Spatial Interaction network

Background Due to the scalability of deep learning technology, researchers have applied it to the non‐destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC...

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Vydáno v:	Journal of the science of food and agriculture Ročník 104; číslo 7; s. 4309 - 4319
Hlavní autoři:	Qi, Hengnian, Luo, Jiahao, Chen, Gang, Zhang, Jianyi, Chen, Fengnong, Li, Hongyang, Shen, Cong, Zhang, Chu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Chichester, UK John Wiley & Sons, Ltd 01.05.2024 John Wiley and Sons, Limited
Témata:	agriculture Algorithms attention module Brix value Clustering data collection Deep learning Destructive testing Fruits High Order Spatial Interaction Network Infrared spectroscopy Learning algorithms Machine learning markets Model accuracy Modules Near infrared radiation near-infrared spectroscopy Neural networks Peaches Performance prediction permutation algorithm Permutations prediction Predictions soluble solids content Spectral bands spectral clustering taste texture Wavelengths deep learning permutation algorithm High Order Spatial Interaction Network soluble solids content attention module spectral clustering
ISSN:	0022-5142, 1097-0010, 1097-0010
On-line přístup:	Získat plný text
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