Early detection of citrus anthracnose caused by Colletotrichum gloeosporioides using hyperspectral imaging

Citrus fruit are susceptible to Colletotrichum gloeosporioides infestation during postharvest and shelf storage. Early and accurate detection of citrus anthracnose is conducive for carrying out targeted pesticide control and mitigating the potential spread of the disease. An early citrus anthracnose detection method using hyperspectral imaging and machine learning techniques is proposed. The hyperspectral data of sound citrus fruits were first collected and served as healthy samples, which were then inoculated with C. gloeosporioides and were further divided into asymptomatic and symptomatic samples. To characterize the global and local grayscale differences of the susceptible samples in different band images, the mean spectrum of the region of interest (ROI) of each band image was extracted as the global spectral features; moreover, the ROI in each band was segmented into disjointed local regions using the contrast limited adaptive histogram equalization and the Otsu thresholding algorithm, where the mean spectrum of the local regions were extracted as the local spectral features, respectively. The global and local spectral features were then concatenated into fused spectral features. Finally, the performance of the fused features was evaluated using support vector machine (SVM), k-NN and random forest (RF). The results showed that, compared with the conventional spectral feature-based methods, the proposed fused spectral features combined with SVM obtained the optimal results, where the average detection accuracy reached 91.97%. Furthermore, after applying feature selection using the successive projections algorithm (SPA), the resultant dimensionally-reduced fused spectral features also obtained acceptable results, with an average detection accuracy of 91.04%.

Automated summary

This study proposes an early citrus anthracnose detection method using hyperspectral imaging and machine learning techniques. By extracting global and local spectral features and fusing them, the method achieves satisfactory results using classifiers such as SVM.