Hyperspectral image characterization and modeling for prediction of ipomeamarone content in sweet potato

In order to rapidly and accurately detect ipomeamarone using hyperspectral image technology sweet potato inoculated with Ceratocystis fimbriata (that can infect sweet potato with black spot disease, leading to the generation of ipomeamarone) was used as the research object, methods of hyperspectral...

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Veröffentlicht in:	Journal of food measurement & characterization 2024-09, Vol.18 (9), p.7762-7773
Hauptverfasser:	Hao, Yanqing, Yin, Yong, Yuan, Yunxia, Song, Jingkai, Li, Zhaozhou, Li, Fang, Pang, Linjiang, Yu, Huichun, Chen, Junliang
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive algorithms Adaptive sampling Algorithms Back propagation networks Black spot Ceratocystis fimbriata Chemistry Chemistry and Materials Science Chemistry/Food Science Disease control Engineering Food Science Hyperspectral imaging Information retrieval Ipomoea batatas Least squares method Machine learning Neural networks Original Paper Potatoes Prediction models Radial basis function Regression analysis Signal generation Support vector machines Sweet potatoes Vegetables Wavelengths
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