ArticleSensors (Basel, Switzerland)2026
Low-Cost Portable Near-Infrared Spectroscopy for Predicting Soil Properties in Paddy Fields of Southeastern China.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R
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