Abstract
Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time‐consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR‐combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non‐destructive estimation of mango leaf macro‐ and micro‐nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.
Original language | American English |
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Article number | 641 |
Pages (from-to) | 1-24 |
Number of pages | 24 |
Journal | Remote Sensing |
Volume | 13 |
Issue number | 4 |
DOIs | |
State | Published - 2 Feb 2021 |
Bibliographical note
Funding Information:Acknowledgments: The authors are thankful to the Indian Council of Agricultural Research, New Delhi, India, and Director of Indian Council of Agricultural Research (ICAR)–Central Coastal Agricultural Research Institute, Old Goa, Goa 403402, India for providing support for this research.
Funding Information:
Funding: This research was funded by Science and Engineering Research Board, Department of Science and Technology, Ministry of Science and Technology, Government of India, Grant Number ECR/2017/000282 under the scheme Early Career Research Award. Katja Berger is funded within the EnMAP scientific preparation program under the DLR Space Administration with resources from the German Federal Ministry of Economic Affairs and Energy, grant number 50EE1923.
Publisher Copyright:
© 2021 by the author. Licensee MDPI, Basel, Switzerland.
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DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
Keywords
- Chemometrics
- Hyperspectral remote sensing
- Multivariate modeling
- Precision nutrient management
- VNIR spectroscopy
- multivariate modeling
- hyperspectral remote sensing
- chemometrics
- precision nutrient management