Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Improving Biomass and Grain Yield Prediction of Wheat Genotypes on Sodic Soil Using Integrated High-Resolution Multispectral, Hyperspectral, 3D Point Cloud, and Machine Learning Techniques

Sodic soils adversely affect crop production over extensive areas of rain-fed cropping worldwide, with particularly large areas in Australia. Crop phenotyping may assist in identifying cultivars tolerant to soil sodicity. However, studies to identify the most appropriate traits and reliable tools to...

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Bibliographic Details
Main Authors: Malini Roy Choudhury, Sumanta Das, Jack Christopher, Armando Apan, Scott Chapman, Neal W. Menzies, Yash P. Dang
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Remote Sensing
Subjects:
phenotyping
vegetation indices
crop height
machine learning
biomass and grain yields
sodic soil
Online Access:https://www.mdpi.com/2072-4292/13/17/3482

Internet

https://www.mdpi.com/2072-4292/13/17/3482

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