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A combined data mining approach for on-line prediction of key soil quality indicators by Vis-NIR spectroscopy
Soil and Tillage Research ( IF 6.1 ) Pub Date : 2020-09-24 , DOI: 10.1016/j.still.2020.104808
Muhammad Abdul Munnaf , Angela Guerrero , Said Nawar , Geert Haesaert , Marc Van Meirvenne , Abdul Mounem Mouazen

Successful modelling of visible and near-infrared (vis-NIR) spectra for on-line prediction of key soil quality indicators is crucial for accurate variable rate applications of farm input resources. The aim of this paper is to optimize modelling of on-line collected spectra for the prediction of soil pH, organic carbon (OC), extractable phosphorous (P) and potassium (K) by means of spiking, combined with clustering and/or extra-weighting. A mobile fiber-type vis-NIR spectrophotometer (CompactSpec from Tec5 Technology, Germany), with spectral range of 305−1700 nm was calibrated using 100 samples collected from five different fields, which were merged with 28 samples collected from a target field. The resulting dataset was subjected to spectral pretreatments followed by k-means clustering and 95 % confidence ellipsoid, resulting in three optimal datasets. Partial least squares regression (PLSR) analyses were carried out on the calibration set (75 % of samples) for four calibration strategies: (i) non-clustered and non-weighted (NCNW), (ii) clustered and non-weighted (CNW), (iii) non-clustered but extra-weighted (NCW), and (iv) clustered and extra-weighted (CW). Results showed that the quality of on-line predictions was the best after clustering combined with extra-weighting. Modelling based with CW significantly improved model prediction accuracy to be very good for pH (ratio of prediction deviation (RPD) = 2.32) and P (RPD = 2.05), and good for OC (RPD = 1.90) and K (RPD = 1.80), whereas results of NCNW (standard calibration approach) were the poorest to be fair for P (RPD = 1.74) and OC (RPD = 1.50), and poor for K (RPD = 1.1) and pH (RPD = 1.39). It can be concluded that optimal sample selection with k-mean clustering when combined with extra-weighting will result in accurate PLSR calibration models for on-line prediction of soil pH, P, OC and K using a multi-field diverse dataset.



中文翻译:

结合数据挖掘方法,通过Vis-NIR光谱在线预测关键土壤质量指标

对于主要土壤质量指标的在线预测,成功建立可见和近红外(vis-NIR)光谱模型对于准确输入农场输入资源的可变速率至关重要。本文的目的是通过加标,结合聚类和/或额外的方法来优化在线收集光谱的模型,以预测土壤的pH,有机碳(OC),可提取的磷(P)和钾(K)。 -加权。使用从五个不同场采集的100个样品校准了光谱范围为305-1700 nm的移动纤维型vis-NIR分光光度计(德国Tec5 Technology的CompactSpec),将其与从目标场采集的28个样品合并。对所得数据集进行光谱预处理,然后进行k均值聚类和95%置信度椭球,得出三个最佳数据集。在四种校准策略的校准集上进行了偏最小二乘回归(PLSR)分析:(i)非聚类和非加权(NCNW),(ii)聚类和非加权(CNW) ),(iii)非群集但超重(NCW),以及(iv)群集和超重(CW)。结果表明,在结合了额外加权之后,在线预测的质量是最好的。基于CW的建模显着提高了模型的预测精度,对于pH值(预测偏差比(RPD)= 2.32)和P(RPD = 2.05)非常好,对于OC(RPD = 1.90)和K(RPD = 1.80)很好。 ,而对于P(RPD = 1.74)和OC(RPD = 1.50)而言,NCNW(标准校准方法)的结果最差,不能令人满意,而K(RPD = 1.1)和pH(RPD = 1.39)较差。可以得出结论:k-均值聚类与额外权重结合使用时,将得出准确的PLSR校准模型,以便使用多字段多样的数据集在线预测土壤的pH,P,OC和K。

更新日期:2020-09-24
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