Improving the detection of cocoa bean fermentation-related changes using image fusion

Complex chemical processes occur in during cocoa bean fermentation. To select well-fermented beans, experts take a sample of beans, cut them in half and visually check its color. Often farmers mix high and low quality beans therefore, chocolate properties are difficult to control. In this paper, we explore how close-range hyper spectral (HS) data can be used to characterize the fermentation process of two types of cocoa beans (CCN51 and National). Our aim is to find spectral differences to allow bean classification. The main issue is to extract reliable spectral data as openings resulting from the loss of water during fermentation, can cover up to 40% of the bean surface. We exploit HS pan-sharpening techniques to increase the spatial resolution of HS images and filter out uneven surface regions. In particular, the guided filter PCA approach which has proved suitable to use high-resolution RGB data as guide image. Our preliminary results show that this pre-processing step improves the separability of classes corresponding to each fermentation stage compared to using the average spectrum of the bean surface.