Automated Fusarium Head Blight Detection Using a ResNet18 Model on High-Resolution Hyperspectral UAV Images

Fusarium head blight (FHB) is a crop disease that significantly threatens grain production and the global agricultural economy. Recent advancements in remote sensing and image-based methods for plant disease diagnosis, emphasizing the superior spectral-spatial information provided by hyperspectral imaging (HSI), aim to address this issue. Accurate and automated FHB detection is crucial for disease management and crop production. This paper explores the potential of HSI for automated crop disease detection, focusing on FHB in wheat, and provides two deep learning-based approaches to address the challenge of FHB detection. The results show that the modified Resnet18 model achieved 100% evaluation accuracy while the DarkNet19 only managed to achieve 73% evaluation accuracy. The t-distributed stochastic neighbor embedding (t-SNE) visualizations used to visualize the latent space for both models further validate these results and illustrate distinctive separation between classes in their feature space. These findings demonstrate the potential of HSI for rapid, non-destructive, and accurate crop disease diagnosis, contributing to the development of efficient, large-scale monitoring systems for improved agricultural management and food security.