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NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States

Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision ag...

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Autores principales: He, Qinghai, Zhang, Haowen, Li, Tianhua, Zhang, Xiaojia, Li, Xiaoli, Dong, Chunwang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675699/
https://www.ncbi.nlm.nih.gov/pubmed/38005495
http://dx.doi.org/10.3390/s23229107
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author He, Qinghai
Zhang, Haowen
Li, Tianhua
Zhang, Xiaojia
Li, Xiaoli
Dong, Chunwang
author_facet He, Qinghai
Zhang, Haowen
Li, Tianhua
Zhang, Xiaojia
Li, Xiaoli
Dong, Chunwang
author_sort He, Qinghai
collection PubMed
description Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25–0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R(2)(C) = 0.979, R(2)(P) = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R(2)(C) = 0.977, R(2)(P) = 0.981, RPD = 7.222) and effective phosphorus (R(2)(C) = 0.969, R(2)(P) = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.
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spelling pubmed-106756992023-11-10 NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States He, Qinghai Zhang, Haowen Li, Tianhua Zhang, Xiaojia Li, Xiaoli Dong, Chunwang Sensors (Basel) Article Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25–0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R(2)(C) = 0.979, R(2)(P) = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R(2)(C) = 0.977, R(2)(P) = 0.981, RPD = 7.222) and effective phosphorus (R(2)(C) = 0.969, R(2)(P) = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies. MDPI 2023-11-10 /pmc/articles/PMC10675699/ /pubmed/38005495 http://dx.doi.org/10.3390/s23229107 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
He, Qinghai
Zhang, Haowen
Li, Tianhua
Zhang, Xiaojia
Li, Xiaoli
Dong, Chunwang
NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
title NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
title_full NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
title_fullStr NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
title_full_unstemmed NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
title_short NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States
title_sort nir spectral inversion of soil physicochemical properties in tea plantations under different particle size states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675699/
https://www.ncbi.nlm.nih.gov/pubmed/38005495
http://dx.doi.org/10.3390/s23229107
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