Crop diseases, particularly Fusarium Head Blight (FHB), pose a significant threat to agricultural productivity, causing severe economic losses and contributing to food insecurity. Traditional diagnostic methods, which are based heavily on expert visual inspections, are often time-consuming, error-prone, and insufficient for early intervention. Hyperspectral imaging (HSI) provides a powerful alternative by capturing spectral information across multiple wavelengths, revealing subtle physiological changes caused by diseases imperceptible to the naked eye. This study combines HSI with deep learning, using a hybrid Inception-ResNet architecture for the binary classification of the severity of FHB. The model efficiently extracts localized spectral features from hyperspectral datasets by implementing a sliding-window approach and custom preprocessing techniques. The proposed framework achieved a validation accuracy of \(\sim 98.92\%\) and a training accuracy of \(\sim 99.4\%\) , demonstrating its effectiveness in distinguishing between mild and severe FHB. The fusion of HSI and CNNs offers a scalable, noninvasive solution for precision agriculture, enabling timely disease detection and reducing reliance on labour-intensive diagnostic methods. This framework holds promise for broader agricultural applications, supporting sustainable farming practices and contributing to global food security. Future research will explore the extension of this approach to detect other plant diseases and the deployment of the model in real-time monitoring systems for more efficient crop health management.