Crop Recommendation Predictive Analysis using Ensembling Techniques

Abstract

Crop recommendation systems play a crucial role in modern agriculture by aiding farmers in making well-informed choices to optimize crop yield and resource utilization. Ensemble learning approaches can significantly improve the effectiveness of crop recommendation systems. To achieve this, multiple forecasts are combined from various models. In this paper, a complete Machine Learning Pipeline is used to evaluate the performance of ensemble learning models in crop recommendation tasks. A diverse dataset is used to select and train four ensemble learning methods, Bagging, Voting, Stacking, and One-Vs-Rest (OVR), as separate classifiers. The dataset includes various agricultural factors such as soil characteristics, meteorological conditions, and past crop productivity. Various metrics, including accuracy, precision, recall, F1-score, and support, are utilized for each model. Bagging is considered the most effective ensemble learning technique, demonstrating excellent levels of accuracy and overall performance. The bagging algorithm achieves a high level of accuracy, reaching 99.32%. It also achieves perfect precision, recall, and F1-score metrics, with values of 0.99, 1.00, and 1.00 respectively. The support value, which represents the number of instances used for evaluation, is 141. This study provides valuable perspectives on the choice of appropriate ensemble learning models for crop recommendation tasks. Consequently, it enables farmers and other individuals involved in agriculture to make well-informed choices using data, resulting in enhanced agricultural output and sustainability.