Deep Learning Approaches for Crop Health Monitoring and Early Disease Detection: A Review

Crop diseases remain a threat to the world's food security with yield loss ranging from 10–40% annually. The last few years have witnessed spectacular evolution in artificial intelligence (AI), deep learning, Internet of Things (IoT), and unmanned aerial vehicles (UAVs), which transformed crop disease monitoring and early detection of diseases. Earlier image processing methods are now overpowered by convolutional neural networks (CNNs), object detectors such as the YOLO family, and CNN-transformer hybrids, which are significantly more accurate and robust. At the same time, IoT sensors and UAV-based multispectral imaging provide complementary environmental and spectral information for enabling active monitoring irrespective of visible indicators. But there are some limitations they have, which are poor model generalization when trained from human-annotated datasets, expensive computation in field deployment, lack of rich plentiful annotated data for low-frequency diseases, and challenging adoption in smallholder farming settings.Here, the three areas of crop health monitoring system development are critically evaluated as follows: (i) image-based systems, (ii) deep learning models, and (iii) multimodal integration of UAV and IoT. Critical comparative performance, strength, and weakness of current methods are analyzed, highlighting dataset heterogeneity, detection accuracy, scalability, and practicability of deployment. Additionally, the review reveals the key deficits in the research—i.e., necessity for robust multimodal fusion paradigms, conventional benchmarking, and affordable field solutions—and suggests likely future directions such as federated learning, predictive outbreak modeling, and robotics for targeted intervention. Synthesizing current success and pointing toward likely future research directions, this review seeks to inform researchers and practitioners toward sustainable, tech-enabled crop disease management.