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Economic development of many countries depends mainly on agriculture. The role of agriculture has created a vast impact on various fields’ namely industrial activities, employability and capital formation. Further, to improve the economic growth of a country, technology-based agriculture processes can be deployed in pre-production and post-production stage. This study mainly focuses on the various post-production activities such as grading, sorting, quality evaluation, monitoring, defects detection, recognition and maturity detection. Traditionally, various agricultural post-production stages such as grading, sorting and quality assessment is mostly done as manual process. Such inspections are prone to errors due to their ineffectiveness and unreliability. Thus, advancements in technology can be deployed for enhancing manual agricultural tasks. This study deals with image processing techniques in agricultural post-production stages. With the support of computer vision-based systems (CVS), efficiency in post-production various activities can be improved. Various feature properties like size, colour, shape of the agricultural products like vegetables and fruits can be used in CVS for grading and sorting. The steps involved in Computer Vision-based Grading System (CVGS) are image acquisition, preprocessing, image segmentation, features extraction and classification. Though many intelligent models exist for grading agricultural products, there are still broad challenges that need to be resolved. These challenges can be weighed against the advantages of CVGS and the grading process can be improved a lot. This work is based on the exhaustive survey of CVS and the steps involved in CVGS. The outcome of this survey is presented as a comparative analysis of various technologies for grading systems available in the market.

An Overview of Various Computer Vision-based Grading System for Various Agricultural Products