Determining and Predicting Soil Chemistry with a Point-of-Use Sensor Toolkit and Machine Learning Model

Abstract

Overfertilization with nitrogen fertilizers has damaged the environment and health of soil; yields are declining, while the population continues to rise. Soil is a complex, living organism which is constantly evolving, physically, chemically and biologically. Standard laboratory testing of soil to determine the levels of nitrogen (mainly NH4+ and NO3-) is infrequent as it is expensive and slow and levels of nitrogen vary on short timescales. Current testing practices, therefore, are not useful to guide fertilization. We demonstrate that Point-of-Use (PoU) measurements of NH4+, when combined with soil conductivity, pH, easily accessible weather (in this study, we simulated weather in the laboratory) and timing data (i.e. days passed since fertilization), allow instantaneous prediction of levels of NO3- in soil with of R2=0.70 using a machine learning (ML) model (the use of higher-precision laboratory measurements instead of PoU measurements increase R2 to 0.87 for the same model). We also show that a long short-term memory recurrent neural network model can be used to predict levels of NH4+ and NO3- up to 12 days into the future from a single measurement at day one, with R2NH4+ = 0.64 and R2NO3- = 0.70, for unseen weather conditions. To measure NH4+ in soil at the PoU easily and inexpensively, we also developed a new sensor that uses chemically functionalized near zero-cost paper-based electrical gas sensors. This new technology can detect the concentration of NH4+ in soil down to 3{+/-}1ppm (R2=0.85). Gas-phase sensing provides a robust method of sensing NH4+ due to the reduced complexity of the gas-phase sample. Our machine learning-based approach eliminates the need of using dedicated, expensive sensing instruments to determine the levels of NO3- in soil which is difficult to measure reliably with inexpensive technologies; furthermore, crucial nitrogenous soil nutrients can be determined and predicted with enough accuracy to forecast the impact of climate on fertilization planning, and tune timing for crop requirements, reducing overfertilization while improving crop yields.