Hyperspectral Imaging Study of Wheat Grains Infected with Several Fusarium Fungal Species and Their Identification with PCA-Based Approach

Wheat is an important agricultural crop grown under various conditions on five continents. The ability to promptly detect and defeat fungal diseases has a significant impact on the volume of the obtained harvest. One of the most significant threats to human and domestic animal health is metabolites produced by <i>Fusarium</i> genus fungi. In this regard, this work is devoted to the possibility of the rapid differentiation between healthy grains and grains simultaneously infected with several species of <i>Fusarium</i> genus fungi (<i>Fusarium graminearum</i> Schwabe FG-30, <i>Fusarium poae</i> Kr-20-14, <i>Fusarium roseum</i> (<i>sambucinum</i>) St-20-3) for practical reasons. An approach based on obtaining hyperspectral data with their subsequent processing using the principal component analysis (PCA) method and determining statistically important spectral regions sensitive for grain infection at different stages (5 and 40 days) was proposed. The effects of the grain orientation and data dimensionality on the classification result were studied. For further practical application in devices for the rapid identification of wheat grains infected with <i>Fusarium</i>, a method based on the use of reflection at wavelengths of 400, 451, 708, 783, 801, and 863 nm, together with normalization [0, 1] and the subsequent projection of spectral data onto the first three principal components (PCs), was proposed, regardless of the grain orientation.