标题： 预测土壤大养分水平：基于 pH 值、电导率和酸碱溶液平均功率的机器学习方法 显示英文标题

标题： Predicting Soil Macronutrient Levels: A Machine Learning Approach Models Trained on pH, Conductivity, and Average Power of Acid-Base Solutions

摘要： 土壤大量营养元素，特别是钾离子（K$^+$），对于植物健康至关重要，支撑着多种生理和生物过程，并有助于管理生物和非生物胁迫。 缺乏大量营养元素会导致生长迟缓、成熟延迟以及对环境胁迫的敏感性增加，从而强调了精确监测土壤养分的重要性。 尽管传统技术如化学分析、原子吸收光谱法、电感耦合等离子体发射光谱法和电化学方法较为先进，但它们过于昂贵且耗时，因此不适合实时监测大量营养元素。 本研究提出了一种创新的土壤测试方案，利用从合成溶液中获得的数据集来模拟土壤行为。 该数据集涵盖了包括导电性和pH值在内的物理特性，并重点关注三种关键大量营养元素：氮（N）、磷（P）和钾（K）。 四种机器学习算法应用于数据集，随机森林回归器和神经网络被选用于预测土壤养分浓度。 与实验室土壤测试结果的比较显示，使用随机森林模型时磷的预测误差为23.6%，钾为16%；而使用神经网络模型时，磷的预测误差为26.3%，钾为21.8%。 此方法展示了成本效益高且有效的实时土壤养分监测策略，在传统技术的基础上实现了显著进步，增强了维持有利于作物健壮生长的最优养分水平的能力。 显示英文摘要

摘要： 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.