An innovative fusion method with micro-vision and spectrum of wheat for detecting asymptomatic Fusarium head blight

Fusarium head blight (FHB) poses a significant threat to global wheat health and seriously affects the quality of the wheat and its products. Therefore, detection of early FHB infection in wheat is crucial for preventing its rapid spread and ensuring food safety. This study proposed an innovative fusion method for detecting the severity of FHB invasion in wheat based on near-infrared spectroscopy and microscopic visual images. This method concatenated 512 features from near-infrared spectra and microscopic visual images of wheat and used neural architecture search (NAS) to build a model for fused features to achieve accurate classification of the degree of infection caused by pathogens in wheat, with accuracy of 90.60 % and F1-score of 90.95 %. This represented significant improvements of 20.80 % and 21.79 % over single spectral data modelling and 11.41 % and 12.67 % over single image data modelling, respectively. The study results showed that this method enables more accurate and non-destructive detection of FHB in wheat, providing a solution for the early identification of potential fungal diseases, which is valuable for improving the quality and yield of wheat.