Santasup Natchanon, Theanjumpol Parichat, Santasup Choochad, Kittiwachana Sila, Mawan Nipon, Khongdee Nuttapon
Department of Plant and Soil Science, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand.
Postharvest Technology Research Center, Faculty of Agriculture, Chiang Mai University, Chiang Mai, 50200, Thailand.
Data Brief. 2025 Jun 25;61:111840. doi: 10.1016/j.dib.2025.111840. eCollection 2025 Aug.
The paper presents the spectroscopic data obtained from a homemade NIR spectrometer developed for agricultural quality analysis, along with the calibration and validation of a model database for predicting agricultural soil properties. We collected NIR spectral data from 190 soil samples taken at a depth of 0-20 cm from agricultural areas in northern Thailand, including vegetable farms, orchards, and field crops. The acquisition process started by air-drying the soil and sieving it through 2.0 mm and 0.5 mm mesh. Six preprocessing techniques, including Savitzky-Golay smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative, second derivative, and mean centering, were used with partial least squares (PLS) regression to create the prediction model for soil organic matter and total carbon. Seventy percent of the sample was divided into calibration and the remaining thirty percent was validation. The most suitable model for assessing soil organic matter (SOM) and total carbon is Savitzky-Golay smoothing through the PLSR model, with a coefficient of determination (R) of 0.79 and 0.78, a root mean square error (RMSE) of 0.701% and 0.382% for validation samples, respectively. Thus, the NIR dataset spanning 900-1,700 nm proved to be an ideal wavelength range for developing a portable/handheld NIR spectrometer, with potential for further accuracy improvements through model refinement.
本文介绍了从一台为农业质量分析而开发的自制近红外光谱仪获得的光谱数据，以及用于预测农业土壤性质的模型数据库的校准和验证。我们从泰国北部农业区0-20厘米深度采集的190个土壤样本中收集了近红外光谱数据，这些区域包括蔬菜农场、果园和大田作物。采集过程首先是将土壤风干，然后通过2.0毫米和0.5毫米的筛网筛分。六种预处理技术，包括Savitzky-Golay平滑、多元散射校正（MSC）、标准正态变量（SNV）、一阶导数、二阶导数和均值中心化，与偏最小二乘法（PLS）回归一起用于创建土壤有机质和总碳的预测模型。70%的样本用于校准，其余30%用于验证。通过PLSR模型评估土壤有机质（SOM）和总碳的最合适模型是Savitzky-Golay平滑，验证样本的决定系数（R）分别为0.79和0.78，均方根误差（RMSE）分别为0.701%和0.382%。因此，900-1700纳米的近红外数据集被证明是开发便携式/手持式近红外光谱仪的理想波长范围，通过模型优化有进一步提高精度的潜力。
