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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Nainggolan, Rut Juniar
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Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Prediction of Soil Nutrients from Different Soil Textures using Portable Spectrometer and Machine Learning Himawan, Harki; Nainggolan, Rut Juniar; Rakhmadi, Handono; Djoyowasito, Gunomo; Ubaidillah; Nopriani, Lenny Sri; Al Riza, Dimas Firmanda
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2166

Abstract

Soil nutrients, such as nitrogen, phosphorus, and potassium, are critical for plant growth and agricultural productivity. Conventional laboratory methods for measuring these nutrients are accurate but often time-consuming, costly, and environmentally taxing. This study explores the potential of portable visible-near infrared (Vis-NIR) spectrometer combined with machine learning algorithms as a rapid, cost-effective, and eco-friendly alternative for soil nutrient analysis. Soil samples of clay, clay loam, and sandy clay were collected and analyzed using artificial neural network (ANN) approach to predict soil nutrients. A total of 81 reflectance spectra data from each soil type were acquired using an AS7265x sensor and processed to develop a predictive model for nutrient content. ANN models demonstrated high accuracy, with R² values exceeding 0.8 in each type of soil texture. This study emphasizes the potential of portable Vis-NIR spectrometer and machine learning integration to revolutionize soil nutrient analysis, offering significant improvements in agricultural efficiency and sustainability.
Co-Authors Dimas Firmanda Al Riza Gunomo Djoyowasito Himawan, Harki Rakhmadi, Handono Ubaidillah