Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Abstract Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include da...

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Autores principales:	Nicola Falco, Haruko M. Wainwright, Baptiste Dafflon, Craig Ulrich, Florian Soom, John E. Peterson, James Bentley Brown, Karl B. Schaettle, Malcolm Williamson, Jackson D. Cothren, Richard G. Ham, Jay A. McEntire, Susan S. Hubbard
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6014d1af1a23475981365e3fdca8733a
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spelling	oai:doaj.org-article:6014d1af1a23475981365e3fdca8733a2021-12-02T13:27:04ZInfluence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery10.1038/s41598-021-86480-z2045-2322https://doaj.org/article/6014d1af1a23475981365e3fdca8733a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86480-zhttps://doaj.org/toc/2045-2322Abstract Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.Nicola FalcoHaruko M. WainwrightBaptiste DafflonCraig UlrichFlorian SoomJohn E. PetersonJames Bentley BrownKarl B. SchaettleMalcolm WilliamsonJackson D. CothrenRichard G. HamJay A. McEntireSusan S. HubbardNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nicola Falco Haruko M. Wainwright Baptiste Dafflon Craig Ulrich Florian Soom John E. Peterson James Bentley Brown Karl B. Schaettle Malcolm Williamson Jackson D. Cothren Richard G. Ham Jay A. McEntire Susan S. Hubbard Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
description	Abstract Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.
format	article
author	Nicola Falco Haruko M. Wainwright Baptiste Dafflon Craig Ulrich Florian Soom John E. Peterson James Bentley Brown Karl B. Schaettle Malcolm Williamson Jackson D. Cothren Richard G. Ham Jay A. McEntire Susan S. Hubbard
author_facet	Nicola Falco Haruko M. Wainwright Baptiste Dafflon Craig Ulrich Florian Soom John E. Peterson James Bentley Brown Karl B. Schaettle Malcolm Williamson Jackson D. Cothren Richard G. Ham Jay A. McEntire Susan S. Hubbard
author_sort	Nicola Falco
title	Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
title_short	Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
title_full	Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
title_fullStr	Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
title_full_unstemmed	Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery
title_sort	influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of uav and ground-based geophysical imagery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6014d1af1a23475981365e3fdca8733a
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Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

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