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Monitoring the foliar nutrients status of mango using spectroscopy-based novel spectral indices and PLSR-combined machine learning models

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Title Monitoring the foliar nutrients status of mango using spectroscopy-based novel spectral indices and PLSR-combined machine learning models
Not Available
 
Creator Mahajan GR
Das B
Murgaokar D
Herrmann I
Berger K
Sahoo RN
Patel KP
Desai A
Morajkar S
Kulkarni RM
 
Subject Chemometrics
Hyperspectral remote sensing
Multivariate modeling
Precision nutrient management
VNIR spectroscopy
 
Description Not Available
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.
SERB DST Government of India
 
Date 2021-07-28T05:25:19Z
2021-07-28T05:25:19Z
2021-02-10
 
Type Journal
 
Identifier Mahajan GR, Das, B, Murgaokar D, Herrmann I, Berger K, Sahoo RN, Patel KP, Desai A, Morajkar S, Kulkarni RM (2021) Monitoring the foliar nutrients status of mango using spectroscopy-based novel spectral indices and PLSR-combined machine learning models. Remote Sensing, 13(4): 641. https://doi.org/10.3390/rs13040641.
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/50432
 
Language English
 
Relation Not Available;
 
Publisher Not Available