Detection of Fusarium Head Blight in Individual Wheat Spikes Using Monocular Depth Estimation with Depth Anything V2

Fusarium head blight (FHB) poses a significant threat to global wheat yields and food security, underscoring the importance of timely detection and severity assessment. Although existing approaches based on semantic segmentation and stereo vision have shown promise, their scalability is constrained by limited training datasets and the high maintenance cost and complexity of visual sensor systems. In this study, AR glasses were employed for image acquisition, and wheat spike segmentation was performed using Depth Anything V2, a monocular depth estimation model. Through geometric localization methods—such as identifying abrupt changes in stem width—redundant elements (e.g., awns and stems) were effectively excluded, yielding high-precision spike masks (Precision: 0.945; IoU: 0.878) that outperformed leading semantic segmentation models including Mask R-CNN and DeepLabv3+. The study further conducted a comprehensive analysis of differences between diseased and healthy spikelets across RGB, HSV, and Lab color spaces, as well as three color indices: Excess Green–Excess Red (ExGR), Normalized Difference Index (NDI), and Visible Atmospherically Resistant Index (VARI). A dynamic fusion weighting strategy was developed by combining the Lab-a* component with the ExGR index, thereby enhancing visual contrast between symptomatic and asymptomatic regions. This fused index enabled quantitative assessment of FHB severity, achieving an R2 of 0.815 and an RMSE of 8.91%, indicating strong predictive accuracy. The proposed framework offers an intelligent, cost-effective solution for FHB detection, and its core methodologies—depth-guided segmentation, geometric refinement, and multi-feature fusion—present a transferable model for similar tasks in other crop segmentation applications.