A predictive analytics model for crop suitability and productivity with tree-based ensemble learning

Abstract

The increasing global population has heightened the importance of agriculture and the need for food security. This study focuses on developing and evaluating tree-based ensemble learning models for predicting crop suitability and productivity to achieve Agenda Zero Hunger by 2030. The study encompasses two primary objectives: (1) exploring and analyzing the relationships between environmental factors and crop suitability and productivity and (2) developing a predictive analytics tree-based ensemble learning model for crop suitability and productivity prediction. The proposed models are evaluated using a publicly available dataset from Kaggle. The experimental results demonstrate exceptional performance with an accuracy of 99.32%, precision of 99.34%, recall of 99.39%, and F1-score of 99.34%. Furthermore, our findings reveal that the choice of crop cultivation within a specific area heavily relies on factors like rainfall and potassium levels. Our findings contribute to the advancement of agricultural practices, enabling farmers to make informed decisions regarding crop selection, ultimately striving towards improved food security and sustainable agricultural practices aligned with the Zero Hunger Agenda 2030 goals.