Automated sorting and quality grading of agricultural produce are crucial for
providing commodities with consistent quality to the consumers and markets. Machine
vision has been playing a key role in this quest by presenting technological solutions
that provide robust, consistent, and accurate decisions with minimal human
intervention. An end-to-end quality inspection system should recognize the type of
agricultural product and then perform quality grading. Accordingly, in this proof-o-
-concept study, a deep learning-based end-to-end solution for quality inspection of
agricultural produce is presented, where an initial system automatically sorts fruitsvegetables,
while a second system grades apples by skin quality. Experimental
evaluations show that the presented end-to-end solution achieves accurate and
promising results, and thus holds high-potential for offering high-impact, traceable and
generalizable answers for the industry.
Keywords: Computer vision, Deep learning, Grading, Fruit and vegetable,
Machine vision, Quality inspection.