Automation on Fruit and Vegetable Grading System and Traceability

Abstract

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.

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