Machine vision system for food grain quality evaluation: A review

•This work provides an updated review of various machine vision systems and techniques used for quality evaluation of food grains.•These systems have been effectively used to monitor grain quality during processing and for grading applications. They have also shown successful results for product and variety based classification of food grains.•Other major applications include: identification of foreign matter, insect infestation, microbial infection and discoloured grains.•This review also explains various system configurations used and the scope of using artificial intelligence for such applications.•The work concludes with a note on limitations and the scope for further research to develop robust and efficient machine vision systems for the grain industry.

BackgroundQuality of pre-processed food grains is a critical aspect and a major decider of market acceptability, storage stability, processing quality, and overall consumer acceptance. Among various indices of food grain quality evaluation, physical appearance (including external morphology) provides the foremost assessment on the condition of the grain. Conventional method of grain quality evaluation, visual inspection (a manual method) is challenging even for trained personnel in terms of rapidity, reliability and accuracy.Scope and approachMachine vision systems have the potential to replace manual (visual) methods of inspection and, have therefore gained wide acceptance in industries as a tool for quality evaluation of numerous agricultural products. This note provides an up-to-date review on the major applications of machine vision systems for grain quality evaluation applications in non-touching arrangement, highlighting system components, image processing and image analysis techniques, advantages and limitations of machine vision systems.Key findings and conclusionsMachine vision systems can provide rapid and accurate information about external quality aspects of food grains. However, it is a task to integrate such systems with those that can explain internal grain quality attributes. In the near future, with ever-growing application requirements and research developments, machine vision systems can provide effective solutions for various grain quality evaluation applications.