4.7 Review

Soil Properties Prediction for Precision Agriculture Using Visible and Near-Infrared Spectroscopy: A Systematic Review and Meta-Analysis

Journal

AGRONOMY-BASEL
Volume 11, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/agronomy11030433

Keywords

soil characteristics; variability; organic matter; carbon; texture; moisture; nitrogen; salinity; machine learning; regression

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Reflectance spectroscopy for soil property prediction is a non-invasive, fast, and cost-effective alternative to traditional laboratory analysis. The performance of soil spectroscopy varies depending on soil composition and properties, as well as instrumentation and data analysis methods used. Studies have shown that this technology can provide accurate predictions for carbon and nitrogen content, but may have limitations for silt and clay content.
Reflectance spectroscopy for soil property prediction is a non-invasive, fast, and cost-effective alternative to the standard laboratory analytical procedures. Soil spectroscopy has been under study for decades now with limited application outside research. The recent advancement in precision agriculture and the need for the spatial assessment of soil properties have raised interest in this technique. The performance of soil spectroscopy differs from one site to another depending on the soil's physical composition and chemical properties but it also depends on the instrumentation, mode of use (in-situ/laboratory), spectral range, and data analysis methods used to correlate reflectance data to soil properties. This paper uses the systematic review procedure developed by the Centre for Evidence-Based Conservation (CEBC) for an evidence-based search of soil property prediction using Visible (V) and Near-InfraRed (NIR) reflectance spectroscopy. Constrained by inclusion criteria and defined methods for literature search and data extraction, a meta-analysis is conducted on 115 articles collated from 30 countries. In addition to the soil properties, findings are also categorized and reported by different aspects like date of publication, journals, countries, employed regression methods, laboratory or in-field conditions, spectra preprocessing methods, samples drying methods, spectroscopy devices, wavelengths, number of sites and samples, and data division into calibration and validation sets. The arithmetic means of the coefficient of determination (R-2) over all the reports for different properties ranged from 0.68 to 0.87, with better predictions for carbon and nitrogen content and lower performance for silt and clay. After over 30 years of research on using V-NIR spectroscopy to predict soil properties, this systematic review reveals solid evidence from a literature search that this technology can be relied on as a low-cost and fast alternative for standard methods of soil properties prediction with acceptable accuracy.

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