Unveilling Soil Fertility Classification with Explainable AI

Population growth necessitates the urgent enhancement of sustainable food production systems. However, the overuse of fertilizers significantly undermines soil fertility, posing a dual threat to agricultural productivity and environmental integrity. This paper introduces an innovative machine learning (ML) methodology integrated with interpretable artificial intelligence (IAI) aimed at promoting sustainable soil management practices. We conduct a thorough investigation into interpretable ML models specifically designed for the classification of soil fertility. Our approach meticulously analyzes model outcomes while pinpointing critical features that influence predictions regarding soil fertility. The results of this method exhibit remarkable promise, achieving high accuracy in predicting soil fertility.