Synergistic intelligence: a novel hybrid model for precision agriculture using k-means, naive Bayes, and knowledge graphs

This study presents a novel hybrid knowledge discovery model integrating K-Means clustering, Naive Bayes classification, and Knowledge Graph technology to address interpretability and data heterogeneity challenges in precision agriculture. The proposed framework first applies K-Means to segment agro-ecological zones using multi-source data (soil, climate, satellite imagery), then employs Naive Bayes to classify crop productivity tiers, achieving 89% accuracy—surpassing standalone benchmarks (Naive Bayes: 86%, Random Forest: 87.5%). A Neo4j-based Knowledge Graph contextualizes these outputs, demonstrating 95% schema completeness and efficient querying (0.1559s latency), while enabling dynamic analysis of soil-climate-crop relationships. Pilot trials confirmed actionable impacts, including 22% reduced water use and 18% less fertilizer waste in targeted farms. By unifying unsupervised/supervised learning with semantic reasoning, this work advances scalable, interpretable decision support systems for sustainable agriculture, offering a replicable template for global food security initiatives.