EBAR: A Novel Machine Learning Model for Quantifying Chemical Concentrations using NIR Spectroscopy

Abstract

The examination of Near Infrared Reflectance Spectroscopy (NIR) in cattle and poultry fertilizers provides a viable solution for determining optimal fertilizer composition for crop growth while mitigating adverse impacts on soil and groundwater quality. In recent studies, conventional machine learning models combined with spectral analysis have been used to ascertain cattle and poultry fertilizer concentrations. However, these traditional machine learning models encounter challenges in achieving data generalization, resulting in suboptimal prediction accuracy. To address this issue, this study proposes a synthesized machine learning model named EBAR (Error Based Accumulation Regression), which exhibits a commendable coefficient of determination, with an average R2 = 0.865 across 7 chemical substances, surpassing the performance of existing traditional machine learning models. Additionally, a Backward Elimination technique is designed to identify crucial wavelength ranges for monitoring component concentrations. The research outcome is promising and acts as a novel benchmark for later models in determining component concentrations through NIR spectroscopy. Future research gears toward expanding datasets and increasing samples of fertilizers, extending examined wavelength, and improving the model’s efficiency to apply to various types of foods, including seafood, vegetables, and fruits.