Edge AI and IoT for Real-Time Crop Disease Detection: A Survey of Trends, Architectures, and Challenges

The intersection of Edge Artificial Intelligence (Edge AI) and the Internet of Things (IoT) has transformed precision agriculture, especially in the application of real-time crop disease detection. This survey investigates the most important technologies, architectures, and AI models that facilitate the on-field detection of crop anomalies through edge devices and smart sensors. To continue, click here. We categorize and summarize recent work into four main categories: edge-enabled sensing and processing frameworks, lean deep learning models for resource-limited devices, data-driven methods such as augmentation and incremental learning, and communication protocols appropriate for agricultural settings. The use of CNN architectures such as MobileNet and EfficientNet, and methods such as pruning and quantization, have made effective disease detection possible on hardware such as Raspberry Pi and Jetson Nano. Nonetheless, significant challenges continue to exist in scalability, energy efficiency, data sparsity, model generalization, and secure communication. We end with directions for future research involving multi-modal sensor fusion, federated learning, and adaptive AI systems for improvements in robustness and scalability in real-world agricultural settings.