AgriSemNet: A Semantic-Aware Hybrid Deep Learning and Knowledge Graph Architecture for Precision Crop Recommendation

Most crop recommendation systems either act as uninterpretable black boxes or struggle to process raw sensor data. In this paper, we built AgriSemNet to bridge this gap by combining a tabular MLP with a Graph Attention Network (GAT) and an external 42-node Agronomic Knowledge Graph. Instead of only predicting a single static crop label, our system generates a practical 4-tier recommendation: the primary crop, a low-risk backup crop, a sustainable crop rotation sequence, and clear biological justifications for the farmer. We also built in two-stage soil safety guardrails to flag extreme or toxic soil conditions before planting. On a benchmark 22-crop dataset, our model achieves 98.79% accuracy and stays remarkably stable against IoT sensor noise, dropping only 1.52% under 20% Gaussian noise, where standard models like Random Forest and XGBoost degrade significantly.