氮气
计算机科学
环境科学
点(几何)
数学
化学
几何学
有机化学
作者
Max Grell,Giandrin Barandun,Tarek Asfour,Michael Kasimatis,Alexander Silva Pinto Collins,Jieni Wang,Firat Güder
出处
期刊:Nature food
[Springer Nature]
日期:2021-12-13
卷期号:2 (12): 981-989
被引量:24
标识
DOI:10.1038/s43016-021-00416-4
摘要
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, with [Formula: see text] and [Formula: see text], 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.
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