TY - JOUR
T1 - Monitoring the foliar nutrients status of mango using spectroscopy‐based spectral indices and plsr‐combined machine learning models
AU - Mahajan, Gopal Ramdas
AU - Das, Bappa
AU - Murgaokar, Dayesh
AU - Herrmann, Ittai
AU - Berger, Katja
AU - Sahoo, Rabi N.
AU - Patel, Kiran
AU - Desai, Ashwini
AU - Morajkar, Shaiesh
AU - Kulkarni, Rahul M.
N1 - Publisher Copyright:
© 2021 by the author. Licensee MDPI, Basel, Switzerland.
PY - 2021/2/2
Y1 - 2021/2/2
N2 - 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.
AB - 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.
KW - Chemometrics
KW - Hyperspectral remote sensing
KW - Multivariate modeling
KW - Precision nutrient management
KW - VNIR spectroscopy
UR - http://www.scopus.com/inward/record.url?scp=85101116449&partnerID=8YFLogxK
U2 - 10.3390/rs13040641
DO - 10.3390/rs13040641
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AN - SCOPUS:85101116449
SN - 2072-4292
VL - 13
SP - 1
EP - 24
JO - Remote Sensing
JF - Remote Sensing
IS - 4
M1 - 641
ER -