Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models
| Autor: | Dayesh Murgaokar, Rabi Narayan Sahoo, Rahul Mukund Kulkarni, Ashwini Desai, Bappa Das, Katja Berger, Ittai Herrmann, Kiran Puna Patel, Shaiesh Morajkar, G. R. Mahajan |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Elastic net regularization
Multivariate statistics multivariate modeling 010504 meteorology & atmospheric sciences hyperspectral remote sensing Science 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Chemometrics Nutrient 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics business.industry Nutrient management Hyperspectral imaging chemometrics Regression precision nutrient management VNIR spectroscopy General Earth and Planetary Sciences Principal component regression Artificial intelligence business computer |
| Zdroj: | Remote Sensing, Vol 13, Iss 641, p 641 (2021) Remote Sensing; Volume 13; Issue 4; Pages: 641 |
| ISSN: | 2072-4292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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