Predicting NH4 Levels for Corn Crop in Wisc

Ammonium (NH4), an organic matter that accumulates in the top portion of soil, can pose a serious risk to biodiversity. Using machine learning to construct regression models, NH4 levels can be predicted and therefore mitigated. In this paper we used linear, ridge, and lasso regressions. Through the evaluation of crop farming factors that contribute to the NH4 levels, it was concluded that NO3 and N2O have the most direct correlation to NH4. These factors yielded the best accuracy for regression models with the best performing model being a multiple feature linear regression which resulted in 60% accuracy. While certain measures did improve the model’s performance, outliers continuously worsened the results.

Ammonium (NH4), an organic matter that accumulates in the top portion of soil, can pose a serious risk to biodiversity. Using machine learning to construct regression models, NH4 levels can be predicted and therefore mitigated. In this paper we used linear, ridge, and lasso regressions. Through the evaluation of crop farming factors that contribute to the NH4 levels, it was concluded that NO3 and N2O have the most direct correlation to NH4. These factors yielded the best accuracy for regression models with the best performing model being a multiple feature linear regression which resulted in 60% accuracy. While certain measures did improve the model’s performance, outliers continuously worsened the results.

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