Estimating Macronutrient Content of Paddy Soil Based on Near-Infrared Spectroscopy Technology Using Multiple Linear Regression

Jonni Firdaus; Usman Ahmad; I Wayan Budiastra; I Dewa Made Subrata

doi:10.46604/aiti.2023.12683

Authors

Jonni Firdaus Agricultural Engineering Science Study Program, IPB University, Bogor, Indonesia/ National Research and Innovation Agency Republic of Indonesia, Jakarta, Indonesia
Usman Ahmad Department of Mechanical and Biosystem Engineering, IPB University, Bogor, Indonesia
I Wayan Budiastra Department of Mechanical and Biosystem Engineering, IPB University, Bogor, Indonesia
I Dewa Made Subrata Department of Mechanical and Biosystem Engineering, IPB University, Bogor, Indonesia

DOI:

https://doi.org/10.46604/aiti.2023.12683

Keywords:

fertility, near-infrared spectroscopy, nitrogen, phosphorus, potassium

Abstract

This study investigates the feasibility of employing near-infrared (NIR) spectroscopy with multiple linear regression (MLR) to estimate macronutrients in paddy soil compared with partial least squares (PLS) and principal component regression (PCR). Seventy-nine soil samples from West Java Province, Indonesia, are subject to conventional nutrient analysis and NIR spectroscopy (1000-2500 nm). The reflectance data undergoes various pretreatment techniques, and MLR models are calibrated using the forward method to achieve correlations exceeding 0.90. The best model calibrations are selected based on high correlation coefficients, determination coefficients, RPD, and low RMSE values. Meanwhile, the comparison of performance MLR is made with the PLS and PCR models. Results indicate that simple MLR models perform less than PLS for all nutrients, better than PCR for nitrogen, and below PCR for phosphorus and potassium. However, MLR reliably estimates soil nitrogen, phosphorus, and potassium content with ratio of performance to deviation (RPD) exceeding 2.0. This study demonstrates the potential of MLR for precise macronutrient estimation in paddy soil.

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