AI-based remote sensing utilizes drones or satellites equipped with RGB, multispectral, or hyperspectral cameras to capture high-resolution images of sorghum and millet fields. These sensors detect early signs of blast (Magnaporthe oryzae), like leaf lesions, or smut (Sporisorium spp.), like dark spore masses, by identifying changes in color, texture, or spectral reflectance. Deep learning models, such as Convolutional Neural Networks (CNNs) or Vision Transformers, trained on labeled datasets (e.g., PlantVillage), classify these patterns to detect disease before visible spread. Spectral indices like NDVI highlight stress-related changes. IoT devices and edge AI enable real-time processing on farms, reducing latency. Federated learning can improve models by aggregating data across farms while preserving privacy. This approach minimizes labor-intensive manual inspections, enabling precise, early interventions like targeted fungicide application. By integrating continuous monitoring with AI, farmers can protect yields and reduce crop losses effectively. For sorghum and millets, where early symptoms are subtle, hyperspectral imaging paired with robust models offers high sensitivity, making it ideal for farm-level disease management, particularly in resource-constrained settings where timely action is critical. for more information email: [email protected]

AI-based remote sensing can detect early blast and smut in sorghum and millets through multispectral and hyperspectral imaging that captures subtle changes in plant reflectance patterns before visible symptoms appear. Machine learning algorithms, particularly convolutional neural networks (CNNs), analyze spectral signatures in the near-infrared and red-edge bands to identify stress indicators like altered chlorophyll content, leaf structure changes, and water stress associated with fungal infections. Drone-mounted cameras and satellite imagery provide high-resolution temporal monitoring, while computer vision models trained on infected vs. healthy plant datasets can automatically flag affected areas with 85-95% accuracy. Key spectral indices like NDVI, SAVI, and disease-specific vegetation indices help distinguish between blast lesions and smut symptoms by detecting changes in photosynthetic activity, leaf moisture content, and cellular structure disruption that occur 7-14 days before visual disease manifestation, enabling timely intervention and targeted treatment applications.