用机器学习预测土壤氮磷钾含量，误差低于27%，适合实时监测。
Predicting Soil Macronutrient Levels: A Machine Learning Approach Models Trained on pH, Conductivity, and Average Power of Acid-Base Solutions
- 基于pH、电导率和酸碱溶液功率数据，训练随机森林与神经网络模型。
- 对磷的预测误差23.6%，钾为16%，优于传统方法。
- 成本低、速度快，适合农业实时营养管理。
土壤宏量营养元素，尤其是钾离子（K⁺），对植物健康至关重要，支撑多种生理与生物过程，并有助于应对生物与非生物胁迫。缺乏宏量营养会导致生长迟缓、成熟延迟及环境胁迫敏感性增加，因此精准监测土壤养分极为重要。传统化学分析、原子吸收光谱、电感耦合等离子体发射光谱及电化学方法虽先进，但成本高、耗时长，难以实现实时检测。本研究提出一种创新土壤测试方法，利用合成溶液数据集模拟土壤行为，涵盖电导率、pH等物理特性，重点建模氮（N）、磷（P）和钾（K）三种关键宏量元素。采用四种机器学习算法，最终选用随机森林回归与神经网络进行浓度预测。与实验室实测结果对比显示，随机森林模型对磷的预测误差为23.6%，钾为16%；神经网络模型分别为26.3%和21.8%。该方法为实时土壤养分监测提供了低成本、高效解决方案，显著优于传统技术，有助于维持作物健康生长所需的最佳营养水平。
原文摘要 · Abstract (English)
Soil macronutrients, particularly potassium ions (K$^+$), are indispensable for plant health, underpinning various physiological and biological processes, and facilitating the management of both biotic and abiotic stresses. Deficient macronutrient content results in stunted growth, delayed maturation, and increased vulnerability to environmental stressors, thereby accentuating the imperative for precise soil nutrient monitoring. Traditional techniques such as chemical assays, atomic absorption spectroscopy, inductively coupled plasma optical emission spectroscopy, and electrochemical methods, albeit advanced, are prohibitively expensive and time-intensive, thus unsuitable for real-time macronutrient assessment. In this study, we propose an innovative soil testing protocol utilizing a dataset derived from synthetic solutions to model soil behaviour. The dataset encompasses physical properties including conductivity and pH, with a concentration on three key macronutrients: nitrogen (N), phosphorus (P), and potassium (K). Four machine learning algorithms were applied to the dataset, with random forest regressors and neural networks being selected for the prediction of soil nutrient concentrations. Comparative analysis with laboratory soil testing results revealed prediction errors of 23.6% for phosphorus and 16% for potassium using the random forest model, and 26.3% for phosphorus and 21.8% for potassium using the neural network model. This methodology illustrates a cost-effective and efficacious strategy for real-time soil nutrient monitoring, offering substantial advancements over conventional techniques and enhancing the capability to sustain optimal nutrient levels conducive to robust crop growth.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。