A smart ontology based model to optimize crop decision support

Abstract

Effective crop recommendation systems are crucial for modern agriculture, yet existing models often struggle to adapt to dynamic environmental conditions and incorporate expert knowledge. This paper proposed a novel model that fuses decision tree (DT) algorithms with ontologies, combining robust data analysis with semantic knowledge representation. DT provide transparent, adaptable decision rules that respond to changing environmental factors, while ontologies structure domain expertise to enable deeper reasoning and improve accuracy. This integrated approach achieved a remarkable 99.77% accuracy on an Indian crop recommendation dataset, significantly outperforming previous methods. By merging the strengths of DT and ontologies, this model offers a powerful, adaptable tool for informed decision-making, supporting farmers in today's complex agricultural landscape.