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.
Highlights
IntroductionOver the last two decades, advancements in remote sensing technologies such as the use of reflectance spectroscopy, airborne and satellite technology, and statistical analysis approaches thereof have made it easy to understand several key processes and components of plants such as plant population [1,2,3], grain yield and biomass [4,5,6,7,8], pigment or chlorophyll [9,10,11], water stress response [12,13,14,15], nutritional status [16,17,18,19,20,21] or pest and disease identification [22,23,24,25]
The coefficient of variation (CV) for the nutrients analyzed for the calibration and validation dataset varied from 10.30–93.30% and
The results revealed that the difference between calibration and validation dataset for mean, variance, and CV was insignificant
Summary
Over the last two decades, advancements in remote sensing technologies such as the use of reflectance spectroscopy, airborne and satellite technology, and statistical analysis approaches thereof have made it easy to understand several key processes and components of plants such as plant population [1,2,3], grain yield and biomass [4,5,6,7,8], pigment or chlorophyll [9,10,11], water stress response [12,13,14,15], nutritional status [16,17,18,19,20,21] or pest and disease identification [22,23,24,25]. In [41], the oil palm nutrient content was retrieved using
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