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• W3010933766 startingPage "368" @default.
• W3010933766 abstract "Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms—random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)—in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 μg cm−2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves." @default.
• W3010933766 created "2020-03-23" @default.
• W3010933766 creator A5004016354 @default.
• W3010933766 creator A5026260210 @default.
• W3010933766 creator A5041510839 @default.
• W3010933766 date "2020-03-17" @default.
• W3010933766 modified "2023-10-16" @default.
• W3010933766 title "Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms" @default.
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• W3010933766 doi "https://doi.org/10.3390/plants9030368" @default.
• W3010933766 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7154821" @default.
• W3010933766 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32192044" @default.
• W3010933766 hasPublicationYear "2020" @default.
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