Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen.
Point-of-use sensors and machine learning enable low-cost determination of soil nitrogen.
复制标题
使用点传感器和机器学习可以低成本测定土壤氮。
DOI：
10.1038/s43016-021-00416-4
复制
发表时间：
2021
期刊：
影响因子：
23.2
通讯作者：
Grell M
中科院分区：
文献类型：
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作者：
Grell M
过度施用氮肥破坏了土壤的环境和健康，但没有定期进行土壤的标准实验室测试以确定氮（主要是NH4+和NO3−）的水平。在这里，我们证明了NH4+的使用点测量，结合土壤电导率，pH值，易于获得的天气和定时数据，可以使用机器学习模型即时预测土壤中的NO3−水平（R2= 0.70）。长短期记忆递归神经网络模型也可用于预测NH4+和NO3−的水平，直到未来12天，从第一天的单次测量中，对于不可见的天气条件。我们基于机器学习的方法消除了使用专用仪器来确定土壤中NO3−水平的需要。可以足够准确地确定和预测含氮土壤养分，以预测气候对施肥计划的影响，并调整作物需求的时间，减少过度施肥，同时提高作物产量。
Overfertilization with nitrogen fertilizers has damaged the environment and health of soil, but standard laboratory testing of soil to determine the levels of nitrogen (mainly NH4+and NO3−) is not performed regularly. Here we demonstrate that point-of-use measurements of NH4+, combined with soil conductivity, pH, easily accessible weather and timing data, allow instantaneous prediction of levels of NO3−in soil (R2= 0.70) using a machine learning model. A long short-term memory recurrent neural network model can also be used to predict levels of NH4+and NO3−up to 12 days into the future from a single measurement at day one, withand, for unseen weather conditions. Our machine-learning-based approach eliminates the need for dedicated instruments to determine the levels of NO3−in soil. Nitrogenous soil nutrients can be determined and predicted with enough accuracy to forecast the impact of climate on fertilization planning and to tune timing for crop requirements, reducing overfertilization while improving crop yields.