An Ensemble Learning Methodology to a Decision Tree Algorithm for Soil Type Classification Using Machine Learning


  • Sumanth S HOD, Department of Computer Science, Smt. V. H. D. Central Institute of Home Science, Bengaluru



Soil Types, Crop Productivity, Decision Tree, Ensemble Learning, Machine Learning


In this study, we focus on the classification of soil types in a specific region, employing a stacking ensemble learning approach with the decision tree algorithm. A significant portion of the population in rural areas relies heavily on agriculture for their livelihood, making agriculture the backbone of our nation's economy. Understanding soil characteristics is critical for agricultural development as it profoundly influences crop productivity. Data mining techniques play a pivotal role in predicting soil types and assisting farmers in selecting the most suitable crops for cultivation. To address this agricultural challenge, we propose a novel method named "Stacking Ensemble Learning with the Decision Tree Model" for soil type classification. Our approach outperforms existing Decision Tree-based methods and exhibits unique advantages in solving complex soil classification problems. Our experimental results demonstrate the effectiveness of our approach in creating an optimal decision tree model for soil type classification. 


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