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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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 17; s. 3482
Hlavní autoři:	Roy Choudhury, Malini, Das, Sumanta, Christopher, Jack, Apan, Armando, Chapman, Scott, Menzies, Neal W., Dang, Yash P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.09.2021
Témata:	Adaptation Agricultural production Agriculture Algorithms Artificial neural networks Australia Biomass biomass and grain yields biomass production Cereal crops Constraints Crop growth crop height Crop production Crop yield Crops Cultivars data collection Flowering Gaussian process Genotypes Grain grain yield Learning algorithms mace Machine learning mechanical harvesting Neural networks normal distribution normalized difference vegetation index Normalized difference vegetative index phenotype Phenotyping Physiology Productivity Rain Regression regression analysis Remote sensing Seasons Sensors sodic soil Sodic soils Soil environment soil sodicity Soils Support vector machines Three dimensional models Unmanned aerial vehicles Vegetation Vegetation index vegetation indices Wheat yield forecasting Australia
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
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