Early detection of fungal infection in citrus using biospeckle imaging

Fungi are among the leading defects causing severe economic losses in the citrus market. Early fungal infection diagnosis is crucial to avoid their propagation throughout production. This paper presents a new non-destructive and accurate method based on biospeckle imaging for the early identification of green mold due to Penicillium digitatum. First, the time and frequency domain properties of biospeckle signals of citrus inoculated with fungal suspension and sterile water were investigated. Three biospeckle parameter images were acquired to analyze changes in biospeckle activity during citrus infection. Next, five numerical parameters were extracted from two regions (injected and infected) of citrus to characterize the pattern of activity change in citrus from healthy to decay. Then, parameters were combined with support vector machine (SVM) and artificial neural network (ANN) classification methods to build fungal infection prediction models. Parameter sensitivity analysis was performed on the best-performing model. The results show that the early decay properties of the infected area appeared in biospeckle parameter images on the second day of infection, earlier than in RGB images. The five indexes in two citrus regions showed consistent variations in the biological activity of citrus at different infection stages. The parameters in the infected region could precede the appearance of visible fungal infection by one to three days. The ANN-based one-day-in-advance prediction model, two-days-in-advance prediction model and three-days-in-advance prediction model achieved 93.9%, 89.3%, and 86.4% discriminant accuracy in the prediction set, respectively. And the SVM-based one-day-in-advance prediction model, two-days-in-advance prediction model and three-days-in-advance prediction model achieved 90.2%, 83.9%, and 81.4% discriminant accuracy in the prediction set, respectively. Therefore, our results show that early fungal infections in citrus can be identified with biospeckle technology and discriminant analysis. Consequently, our techniques can be used in agriculture research to classify fruit fungal infections efficiently and effectively, developing biospeckle imaging technology use in the related sector.