IoT-Enabled Smart Farming for Real-Time Weather Forecasting and Crop Health Monitoring Using Hybrid GRU-CNN Models

Life depends on agriculture, which provides food, medicine, and shelter. Rural agriculture has moved to cities due to urbanisation and population migration. Food safety and healthy living are now improved by urban agriculture. Smart farming solutions for animal husbandry, fertilisation, and irrigation are needed as cities become smarter. To maximise agricultural productivity, Smart Farming, Weather Forecasting, and CHM are essential. The CHM technique includes data processing, feature extraction, and model training. Data is cleaned using Min-Max Normalisation during preprocessing to prepare for analysis. Feature extraction using PCA improves model efficiency. For CHM classification, the AtBiGRU architecture uses deep learning to improve prediction. The AtBiGRU architecture classifies CHMs with 95.76% accuracy, outperforming baseline methods. This shows its crop health monitoring superiority over other deep learning algorithms. The future of urban agriculture depends on smart agricultural technologies like Weather Forecasting and CHM. CHM classification is robust using the AtBiGRU architecture, beating deep learning models. As smart cities evolve, intelligent agricultural practices will help ensure sustainable and efficient food production.