Research Article
Analysis and Prediction of Soil Fertility Using Machine Learning Techniques
Tigist Mintesnot Tewabe*
Issue:
Volume 1, Issue 3, September 2026
Pages:
73-83
Received:
6 March 2026
Accepted:
28 July 2026
Published:
17 August 2026
DOI:
10.11648/j.ajris.20260103.11
Downloads:
Views:
Abstract: Soil fertility is a crucial aspect of agricultural productivity and sustainability, determines the soil's capacity to provide essential nutrients necessary for plant growth and development. This study focuses on the analysis and prediction of soil fertility using ensemble learning techniques in the South Gondar Zone. By examining various soil parameters, including macro and micronutrients, soil structure, pH, and organic matter content, the research aims to develop predictive models that accurately assess soil fertility levels. The objective of this study is to analyze and predict soil fertility using machine learning techniques, specifically targeting the south Gondar Zone, in the Amhara region of Ethiopia. The dataset for this study comprises 20,168 instances, including both fertile and non-fertile samples, with 17 selected attributes. Several machine learning models were evaluated on both the original and SMOTE-balanced datasets. The models included Random Forest, AdaBoost, and XGBoost classifiers. I applied Random Forest classifier consistently demonstrated the highest performance, with testing accuracies of 94.55% on the original dataset and 94.37% on the SMOTE-balanced dataset. AdaBoost also showed strong performance, achieving testing accuracies of 94.6% and 94.31% on the original and SMOTE-balanced datasets, respectively. XGBoost performed well but was slightly less accurate compared to the ensemble methods. However, XGBoost showed robust performance on both datasets, with testing accuracies of 94.04% and 94.27%. Feature importance analysis using the Random Forest classifier has shown that factors such as Clay, CEC, CaCO3, Sand, and Mn significantly impact soil fertility prediction. as a conclusion Random Forest classifier, an ensemble-based learning technique, was the most reliable and accurate model for predicting soil fertility. These results highlight the importance of specific soil properties in determining fertility and can guide targeted soil management practices to improve agricultural productivity.
Abstract: Soil fertility is a crucial aspect of agricultural productivity and sustainability, determines the soil's capacity to provide essential nutrients necessary for plant growth and development. This study focuses on the analysis and prediction of soil fertility using ensemble learning techniques in the South Gondar Zone. By examining various soil param...
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