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• W4220809330 endingPage "106814" @default.
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• W4220809330 abstract "The contents of photosynthetic pigment, which directly affect the growth of crops, could be evaluated with spectral and multispectral imaging technologies in an accurate and rapid way. For commodity germplasm resources on the market, the estimation of photosynthetic pigments and soil and plant analyzer development (SPAD) value was accomplished using the two techniques combined with machine learning. The spectrometer used in this study employed 781 bands from 320 nm to 1100 nm, and a multispectral imaging camera was used to acquire images in visible and near-infrared. Convolutional neural network (CNN), multiple linear regression (MLR) and generalized linear model (GLM) were used to establish the machine learning models, which established by preprocessed spectral data or 4-channel multispectral images. For estimating photosynthetic pigments (chlorophyll a, chlorophyll b, total chlorophyll and carotenoids), the GLM model established by spectral data was the optimal among all the models. For the SPAD optimal estimation model, the GLM model established by the spectral data and CNN model established by the multispectral images were fair. The R2 and RMSE of the CNN model validation set in estimating SPAD were 0.87 and 2.31, respectively. The R2 and RMSE of the GLM model validation set in estimating SPAD were 0.88 and 2.39, respectively. By combining two techniques with different machine learning methods, a comprehensive analysis of photosynthetic pigments and SPAD was accomplished in this paper." @default.
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• W4220809330 date "2022-04-01" @default.
• W4220809330 modified "2023-10-11" @default.
• W4220809330 title "Spectral technology and multispectral imaging for estimating the photosynthetic pigments and SPAD of the Chinese cabbage based on machine learning" @default.
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• W4220809330 doi "https://doi.org/10.1016/j.compag.2022.106814" @default.
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