This paper presents an innovative approach for early detection of wheat diseases, particularly Bacterial Leaf Streak (BLS) and Scab, using a combination of hyperspectral, infrared, and RGB imaging along with Deep Convolutional Neural Networks (DCNNs). The method leverages both spatial and spectral information from wheat seed images, achieving remarkable disease classification accuracy. Advanced image preprocessing, segmentation, and feature extraction techniques are applied, and attention mechanisms enhance model robustness. The study's results outperform existing techniques, demonstrating the potential of multimodal data integration and deep learning in precision agriculture for effective wheat disease management, ultimately leading to increased global agricultural yields and reduced losses.
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