Authors - M. V. Rama Sundari, Bhuvan Unhelkar, Pravin Kshirsagar, Supriya Nandikolla Abstract - The management of nutrient content in soil is vital for improving productivity of crops, as well as sustainable agriculture. Conventional processes of establishing the best ratios of macronutrients tend to overlook intrinsic relation-ships that exist among soil properties, crop, weather. Main purpose is to come up with a smart prediction system that will help forecast Macronutrient needs by infusing particular domain-specific expertise in agriculture into machine learning models to improve interpretability and predictive accuracy. The suggested methodology utilises a Graph Convolutional Network (GCN) to simulate spatial and relational relationships amongst soil, crop and environmental parameters. To provide the model with a better semantic understanding, a Knowledge Graph (KG) is created to encode the relationships between domains. Embedding algorithms, such as TransE and DistMult, are then added to the GCN to form a KG-embedded GCN model that can learn feature-based as well as semantic relationships to predict nutrients. The experimental analyses on the ICFA Crop Recommendation dataset reveal that the baseline GCN got the R² scores of 0.806, 0.729, and 0.768, and the TransE GCN and DistMult GCN models have been advanced to the R² scores of 0.808-0.821, 0.746-0.762, and 0.800-0.814 for Nitrogen, Phosphorus and Potassium respectively. These findings indicate that the predictive strength is greatly advanced by the incorporation of domain knowledge. The model can, however, perform differently on unknown crop varieties and soils, which suggests that more work needs to be done in the future on larger and region-specific datasets.
Open Zoom