Predictive spectral analysis using an end-to-end deep model from hyperspectral images for high-throughput plant phenotyping

•DeepRWC was developed for predicting the RWC from spectral data of maize plants.•DeepRWC incorporated a modified inception module to extract multi-scale features.•Proposed model outperformed the linear (PLSR) and non-linear (SVR) approaches.•Proposed model can eliminate the need of pre-processing o...

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Veröffentlicht in:	Computers and electronics in agriculture 2020-10, Vol.177, p.105713, Article 105713
Hauptverfasser:	Rehman, Tanzeel U., Ma, Dongdong, Wang, Liangju, Zhang, Libo, Jin, Jian
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Convolutional neural networks Datasets Deep learning Hyperspectral imaging Inception Infrared cameras Moisture content Plant phenotyping Regression analysis Spectra Spectral augmentation Spectral reflectance Spectrum analysis Support vector machines
Online-Zugang:	Volltext
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