IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach
Computers and Electronics in Agriculture
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Although most of the process of canning mandarin segments is already automated, this has still not been achieved with the on-line inspection and sorting of the fruit because of the difficulty in the handling of the product and the complexity of the inspection software required to classify the segments following subjective criteria. A machine vision-based system has been developed to classify the objects that reach the line into four categories, detecting broken fruit attending, basically, to the shape of the fruit. A full working prototype has been developed for singulating, inspecting and sorting satsuma (Citrus unshiu) segments. The segments are transported over semi-transparent conveyor belts to allow illuminating the fruit from the bottom to enhance the shape of the segments against the background. The system acquires images of the segments using two cameras connected to a single computer and processes them in less than 50ms. By extracting morphological features from the objects, the system automatically identifies pieces of skin and other raw material, and separates whole segments from broken ones; it is also capable to grade between those with a slight or a large degree of breakage. Tests showed that the machine is able to correctly classify 93.2% of sound segments.