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Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network Ciência Rural
Xiaoyan,Wang; Zhiwei,Li; Wenjun,Wang; Jiawei,Wang.
ABSTRACT: Chlorophyll is a major factor affecting photosynthesis; and consequently, crop growth and yield. In this study, we devised a chlorophyll-content detection model for millet leaves in different stages of growth based on hyperspectral data. The hyperspectral images of millet leaves were obtained under a wavelength range of 380-1000 nm using a hyperspectral imager. Threshold segmentation was performed with near-infrared (NIR) reflectance and normalized difference vegetation index (NDVI) to intelligently acquire the regions of interest (ROI). Furthermore, raw spectral data were preprocessed using multivariate scatter correction (MSC). A correlation coefficient-successive projections algorithm (CC-SPA) was used to extract the characteristic...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Chlorophyll content; Multi-characteristic parameters fusion; Attention-CNN; Hyperspectral imaging technology..
Ano: 2020 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000300205
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