vis-NIRS与机器学习相结合诊断甘蔗土壤化学性质
Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
摘要：
了解土壤的化学性质可能是作物管理和总产量生产的决定性因素。传统的土壤特性估计方法耗时，需要复杂的实验室设置，这使得农民无法迅速采取措施，实现作物的最佳实践。近红外光谱技术是一种低成本、非侵入性和非破坏性的土壤性质估计方法。目前的方法使用数学和统计技术，避免了机器学习框架。该方案采用可见光近红外光谱技术和机器学习技术，如三种回归和六种分类方法。其范围是评估在预测和推断常见土壤性质（pH值，土壤有机质OM，Ca，Na，K和Mg）的类别方面的性能，并通过最常见的指标进行评估。我们使用回归估计属性和分类来评估土壤属性状态。在这两种情况下，我们在文献中报道的类似设置上实现了可比较的性能，用于pH的属性估计（$R^2$ =0.8，$ρ$ =0.89），OM（$R^2$ =0.37，$ρ$ =0.63），Ca（$R^2$ =0.54，$ρ$ =0.74），镁（$R^2$ =0.44，$ρ$ =0.66）。
英文摘要：
Knowing chemical soil properties might be determinant in crop management and total yield production. Traditional soil properties estimation approaches are time-consuming and require complex lab setups, refraining farmers from promptly taking steps towards optimal practices in their crops. Soil properties estimation from its spectral signals, vis-NIRS, emerged as a low-cost, non-invasive, and non-destructive alternative. Current approaches use mathematical and statistical techniques, avoiding machine learning frameworks. This proposal uses vis-NIRS in sugarcane soils and machine learning techniques such as three regression and six classification methods. The scope is to assess performance in predicting and inferring categories of common soil properties (pH, soil organic matter OM, Ca, Na, K, and Mg), evaluated by the most common metrics. We use regression to estimate properties and classification to assess soil property status. In both cases, we achieved comparable performance on similar setups reported in the literature for property estimation for pH($R^2$=0.8, $ρ$=0.89), OM($R^2$=0.37, $ρ$=0.63), Ca($R^2$=0.54, $ρ$=0.74), Mg($R^2$=0.44, $ρ$=0.66) in the validation set.
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