Deep Learning and IoT Fusion for Crop Health Monitoring: A High-Accuracy, Edge-Optimised Model for Smart Farming

Crop diseases and adverse field conditions threaten global food security, particularly in resource-limited regions. Current deep-learning models for disease detection suffer from insufficient accuracy, high prediction instability under field noise, and a lack of integration with environmental context. To address these limitations, we present a hybrid deep learning architecture combining EfficientNetV2, MobileNetV2, and Vision Transformers, augmented with attention mechanisms and multiscale feature fusion. Optimised for edge deployment via TensorFlow Lite and integrated with IoT sensors for real-time soil and field monitoring, the model achieved state-of-the-art performance with 99.2% accuracy, 0.993 precision, 0.993 recall, and a near-perfect AUC of 0.999998, outperforming benchmarks like DenseNet50 (88.4%) and ShuffleNet (95.8%). Training on 76 classes (22 diseases) demonstrated rapid convergence and robustness, with validation accuracy reaching 98.7% and minimal overfitting. Statistical validation confirmed superior stability, with 69% lower prediction variance (0.000010) than DenseNet50 (0.000035), ensuring reliable performance under real-world noise. Bayesian testing showed a 100% probability of superiority over DenseNet50 and 85.1% over ShuffleNet, while field trials on 249 real-world images achieved 97.97% accuracy, highlighting strong generalisation. IoT integration reduced false diagnoses by 92% through environmental correlation, and edge optimisation enabled real-time inference via a 30.4 MB mobile application (0.094-second latency). This work advances precision agriculture through a scalable, cloud-independent framework that unifies hybrid deep learning with edge-compatible IoT sensing. By addressing critical gaps in accuracy, stability, and contextual awareness, the system enhances crop health management in low-resource settings, offering a statistically validated tool for sustainable farming practices.