Intelligent soil fertility forecasting using enhanced STGNN and hybrid swarm-based optimization
C. Selvi, M. Manimaraboopathy, M. S. Jeyalakshmi, G. Narmadha, Selva Sheela K · Results in Engineering · 2025
Soil fertility plays a key role in sustainable agricultural productivity and environmental health. Existing soil assessment techniques are labour-intensive and time-consuming. Also, they are limited in their spatial and temporal coverage. To address these challenges, in this paper, a deep learning-based Enhanced Spatiotemporal Graph Neural Network (E-STGNN) is proposed. The model captures spatial dependencies through graph convolutional layers and temporal dynamics using recurrent neural structures. In addition, the E-STGNN model is optimized using a hybrid Particle Swarm Optimization-guided Red Kite Optimization (PSO-RKO) algorithm to improve model performance. Experimental results on Kaggle dataset shows that the proposed model achieves an exceptional accuracy of 98.9%, with corresponding improvements in precision (98.54%), recall (98.68%), and F1-score (98.60%).