Published Paper Details:

Soil fertility constitutes a key factor in influencing the precise prediction of soil fertility classes, as well as agricultural productivity is important to optimizing agriculture practices. Classical soil fertility assessment methods are often labor intensive and costly. Machine learning based approaches are therefore an attractive alternative for improving efficiency. In this research, a novel method is introduced as a stacking ensemble method for soil fertility classification, which combines models. Each individual model is trained separately and their forecasts are combined through a meta-model in order to improve categorization ability.Unlike traditional single-model approaches the ensemble method effectiveness compared to individual models is assessed using classification metrics like F-1 measure, accuracy, sensitivity.The ensemble outperforms the the individual models demonstrating improved robustness and predictive reliability over existing techniques highlighting its potential for providing more robust and reliable soil fertility predictions.compared to previous approach this method provides more stable performance across fluctuations in nutrient levels and PH.This approach shows promising applications in precision agriculture, offering improved decision-making for soil management. Future studies aimed at improving performance could integrate data input such as sensor data.