Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar

As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model appl...

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Bibliographic Details
Main Authors: Kensuke Kawamura, Tomohiro Nishigaki, Andry Andriamananjara, Hobimiarantsoa Rakotonindrina, Yasuhiro Tsujimoto, Naoki Moritsuka, Michel Rabenarivo, Tantely Razafimbelo
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
deep learning
Madagascar
oxalate-extractable soil P
visible and near-infrared spectroscopy
Online Access:https://www.mdpi.com/2072-4292/13/8/1519

Internet

https://www.mdpi.com/2072-4292/13/8/1519

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