Andrade Renata, Silva Sérgio Henrique Godinho, Benedet Lucas, Mancini Marcelo, Lima Geraldo Jânio, Nascimento Kauan, Amaral Francisco Hélcio Canuto, Silva Douglas Ramos Guelfi, Ottoni Marta Vasconcelos, Carneiro Marco Aurélio Carbone, Curi Nilton
Dept. of Soil Science, Federal University of Lavras, P.O. Box 3037, 37200-900, Lavras, Minas Gerais, Brazil.
Agriculture Promotion Company, CAMPO, Lindolfo García Adjuto, 1000, 38606-026, Paracatu, Minas Gerais, Brazil.
Environ Res. 2023 Nov 1;236(Pt 1):116753. doi: 10.1016/j.envres.2023.116753. Epub 2023 Jul 26.
Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint.
农场使用大量来自多种来源的肥料，这使得质量控制成为一项具有挑战性的任务，因为传统的湿化学分析成本高、耗时且不环保。作为一种替代方法，这项工作提出使用便携式X射线荧光（pXRF）光谱法和机器学习算法来快速、低成本地估算矿物肥料和有机肥料中的大量和微量营养元素含量。测试了四种机器学习算法。分析了完整（即制造商提供的原样）（CP）和研磨后（AQ）的样本（共429个），以测试肥料粒度对预测性能的影响。模型验证表明，对于有机肥料和矿物肥料中的大量元素（氮：R = 0.92；磷：0.97；钾：0.99；钙：0.94；镁：0.98；硫：0.96）和微量元素（硼：0.99；铜：0.99；铁：0.98；锰：0.91；锌：0.94），预测高度准确。AQ样本的RPD值范围为2.31至9.23，随机森林和规则集回归是性能最佳的算法。即使是对从制造商处收到的原样（即未研磨）进行分析的样本，也能提供准确的预测，这加快了对肥料中营养成分的确认。结果表明，pXRF数据与机器学习算法相结合，有潜力高精度地评估矿物肥料和有机肥料中的营养成分，实现清洁、快速且准确的质量控制。基于传感器的肥料质量评估可改善土壤和植物健康、作物管理效率以及粮食安全，同时减少对环境的影响。
