论文标题
使用Vis-Nirs和机器学习方法诊断甘蔗土壤化学特性
Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
论文作者
论文摘要
了解化学土壤特性在作物管理和总产量产生中可能是决定因素。传统的土壤特性估计方法是耗时的，需要复杂的实验室设置，避免农民迅速采取措施朝着农作物中的最佳实践采取措施。土壤特性从其光谱信号（Vis-Nirs）估算为低成本，无创和无损的替代方案。当前的方法使用数学和统计技术，避免机器学习框架。该提案使用甘蔗土壤和机器学习技术（例如三种回归和六种分类方法）中使用的。该范围是评估通过最常见的指标评估的预测和推断公共土壤特性类别（pH，土壤有机物OM，CA，Na，K和Mg）的性能。我们使用回归来估计特性和分类来评估土壤财产状况。 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.
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.
