Database research record2025·Global
Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization
Eman Aldhahri, Abdulwahab Ali Almazroi, Monagi H. Alkinani, Nasir Ayub, Elham Alghamdi, Nourah Fahad Janbi
IEEE Access
Urban Farm DB summary
This study proposes ResXceNet-HBA, a novel classification model for crop health prediction and stress assessment in urban agriculture. The model integrates ResNet blocks, Xception modules, and HBA-optimized parameters, along with data handling techniques like WICL, LFS, and AFD. ResXceNet-HBA achieved 98.5% accuracy, 98.2% precision, 98.7% recall, and 98.4% F1-Score, outperforming ResNet, CNN, and Inception V2, and executed faster in 50.9 seconds.