Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS

Abstract Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), is an important and challenging task that affects farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure SHIs are in-lab wet chemistry or spec...

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Autores principales:	Md Abir Hossen, Prasoon K Diwakar, Shankarachary Ragi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ed14308089fb4e568d8533958f65a221
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spelling	oai:doaj.org-article:ed14308089fb4e568d8533958f65a2212021-12-02T17:24:00ZTotal nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS10.1038/s41598-021-90624-62045-2322https://doaj.org/article/ed14308089fb4e568d8533958f65a2212021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90624-6https://doaj.org/toc/2045-2322Abstract Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), is an important and challenging task that affects farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure SHIs are in-lab wet chemistry or spectroscopy-based methods, which require significant human input and effort, time-consuming, costly, and are low-throughput in nature. To address this challenge, we develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing solution (UMS) to estimate soil TN in an agricultural farm. TN is an important macro-nutrient or SHI that directly affects the crop health. Accurate prediction of soil TN can significantly increase crop yield through informed decision making on the timing of seed planting, and fertilizer quantity and timing. The ground-truth data required to train the AI approaches is generated via laser-induced breakdown spectroscopy (LIBS), which can be readily used to characterize soil samples, providing rapid chemical analysis of the samples and their constituents (e.g., nitrogen, potassium, phosphorus, calcium). Although LIBS was previously applied for soil nutrient detection, there is no existing study on the integration of LIBS with UAV multispectral imaging and AI. We train two machine learning (ML) models including multi-layer perceptron regression and support vector regression to predict the soil nitrogen using a suite of data classes including multispectral characteristics of the soil and crops in red (R), near-infrared, and green (G) spectral bands, computed vegetation indices (NDVI), and environmental variables including air temperature and relative humidity (RH). To generate the ground-truth data or the training data for the machine learning models, we determine the N spectrum of the soil samples (collected from a farm) using LIBS and develop a calibration model using the correlation between actual TN of the soil samples and the maximum intensity of N spectrum. In addition, we extract the features from the multispectral images captured while the UAV follows an autonomous flight plan, at different growth stages of the crops. The ML model’s performance is tested on a fixed configuration space for the hyper-parameters using various hyper-parameter optimization techniques at three different wavelengths of the N spectrum.Md Abir HossenPrasoon K DiwakarShankarachary RagiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Md Abir Hossen Prasoon K Diwakar Shankarachary Ragi Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS
description	Abstract Measuring soil health indicators (SHIs), particularly soil total nitrogen (TN), is an important and challenging task that affects farmers’ decisions on timing, placement, and quantity of fertilizers applied in the farms. Most existing methods to measure SHIs are in-lab wet chemistry or spectroscopy-based methods, which require significant human input and effort, time-consuming, costly, and are low-throughput in nature. To address this challenge, we develop an artificial intelligence (AI)-driven near real-time unmanned aerial vehicle (UAV)-based multispectral sensing solution (UMS) to estimate soil TN in an agricultural farm. TN is an important macro-nutrient or SHI that directly affects the crop health. Accurate prediction of soil TN can significantly increase crop yield through informed decision making on the timing of seed planting, and fertilizer quantity and timing. The ground-truth data required to train the AI approaches is generated via laser-induced breakdown spectroscopy (LIBS), which can be readily used to characterize soil samples, providing rapid chemical analysis of the samples and their constituents (e.g., nitrogen, potassium, phosphorus, calcium). Although LIBS was previously applied for soil nutrient detection, there is no existing study on the integration of LIBS with UAV multispectral imaging and AI. We train two machine learning (ML) models including multi-layer perceptron regression and support vector regression to predict the soil nitrogen using a suite of data classes including multispectral characteristics of the soil and crops in red (R), near-infrared, and green (G) spectral bands, computed vegetation indices (NDVI), and environmental variables including air temperature and relative humidity (RH). To generate the ground-truth data or the training data for the machine learning models, we determine the N spectrum of the soil samples (collected from a farm) using LIBS and develop a calibration model using the correlation between actual TN of the soil samples and the maximum intensity of N spectrum. In addition, we extract the features from the multispectral images captured while the UAV follows an autonomous flight plan, at different growth stages of the crops. The ML model’s performance is tested on a fixed configuration space for the hyper-parameters using various hyper-parameter optimization techniques at three different wavelengths of the N spectrum.
format	article
author	Md Abir Hossen Prasoon K Diwakar Shankarachary Ragi
author_facet	Md Abir Hossen Prasoon K Diwakar Shankarachary Ragi
author_sort	Md Abir Hossen
title	Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS
title_short	Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS
title_full	Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS
title_fullStr	Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS
title_full_unstemmed	Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS
title_sort	total nitrogen estimation in agricultural soils via aerial multispectral imaging and libs
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ed14308089fb4e568d8533958f65a221
work_keys_str_mv	AT mdabirhossen totalnitrogenestimationinagriculturalsoilsviaaerialmultispectralimagingandlibs AT prasoonkdiwakar totalnitrogenestimationinagriculturalsoilsviaaerialmultispectralimagingandlibs AT shankaracharyragi totalnitrogenestimationinagriculturalsoilsviaaerialmultispectralimagingandlibs
_version_	1718381002000171008

Total nitrogen estimation in agricultural soils via aerial multispectral imaging and LIBS

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