Analysing chickpea physical characteristics emphasising on count, shape and size using computer vision. (17th December 2021)

• Record Type: Journal Article
• Authors: Khatri, Ajay; Agrawal, Shweta
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 12:Number 4(2021)
• Page Start: 271
• Page End: 278
• Publication Date: 2021-12-17
• Subjects: computer vision -- chickpea -- image processing -- grain analysis -- accuracy; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Chickpeas are the food supplements which are very rich in protein, fibre and minerals. This grain affects a large percentage of Indian economy and India has the largest production and consumption of these grains. The most important quality attribute of chickpea's are size of seed, colour and taste. Based on these quality attributes chickpeas are graded into three main grades 7-8 mm, 8-9 mm, 9 mm and above. Determining seed size through sieve analysis in legumes is labour dependent, time consuming and inaccurate method. In general quality assessment of desi chickpea is done by visual inspection of small samples from the lot which is a slow and inaccurate process. The paper proposes a computer vision-based algorithm to assess the quality of chickpea on the basis of their shape, size and count. Experiment is performed for 20 sample images the results present that accuracy achieved through proposed algorithm for width calculation is 97.4%, for height calculation is 98.14%, for aspect ratio accuracy achieved is 97.3% and for chickpea count accuracy achieved is 98.6%. The proposed algorithm used concept of reference object to overcome the problem of dependency on distance of object and camera while capturing the image.