Automatic detection of pomegranate fruit affected by blackheart disease using X-ray imaging

Blackheart is one of the primary diseases affecting pomegranate fruit globally, caused by the fungus Alternaria. The damages are not visually detectable, as it is an internal disease that requires non-invasive technologies to provide information from inside the fruit to be detected. This study explored the ability of X-ray imaging to detect this infection in 'Wonderful' pomegranate fruit. X-ray images of healthy and infected fruit at different levels were acquired and analysed. Texture features based on first-order statistics, the grey-level co-occurrence matrix (GLCM), and grey-level histograms with several resolutions were extracted from X-ray images and used to classify the fruit as healthy or infected through the random forest algorithm. The presence of the infection in three levels of severity was later assessed by destructive visual analysis by opening the samples in half. The highest accuracy models were obtained using all texture features and histograms with 256 bins. Compared to manual inspection, X-rays showed a clear advantage in detecting incipient infections (infected fruit at level 1), correctly identifying 93.3 % of infected fruits. In contrast, the manual inspection identified only 66.7 % of fruit, highlighting the limitations of early-stage detection.