Potential Applications of Computer Vision in Quality Inspection of Rice: A Review

55Citations
Citations of this article
123Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Among the cereals, rice is the major foodstuff for a large part of the world’s population. Due to its tremendous importance in the global market, its qualitative economic aspects during processing have always been attended by producers. As the most delicate of the cereals, rice needs the utmost care during post-harvest handling and processing, because in most cases, it is consumed as whole kernel. The growing demand for production of rice with high-quality and safety standards has increased the need for its accurate, fast and objective quality monitoring. Computer vision techniques, as novel technologies, can provide an automated, nondestructive and cost-effective way to achieve these requirements. In recent years, various studies have been conducted to evaluate rice qualitative features based on computer vision techniques. This paper presents the theoretical and technical principles of computer vision for nondestructive quality assessment of rice combined with a review of the recent achievements and applications for quality inspection and monitoring of the product.

References Powered by Scopus

Improving quality inspection of food products by computer vision - A review

848Citations
N/AReaders
Get full text

Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson

435Citations
N/AReaders
Get full text

Machine vision system: A tool for quality inspection of food and agricultural products

244Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

715Citations
N/AReaders
Get full text

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

536Citations
N/AReaders
Get full text

Classification of rice varieties with deep learning methods

132Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Zareiforoush, H., Minaei, S., Alizadeh, M. R., & Banakar, A. (2015, September 28). Potential Applications of Computer Vision in Quality Inspection of Rice: A Review. Food Engineering Reviews. Springer New York LLC. https://doi.org/10.1007/s12393-014-9101-z

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 38

63%

Researcher 8

13%

Professor / Associate Prof. 7

12%

Lecturer / Post doc 7

12%

Readers' Discipline

Tooltip

Engineering 27

42%

Agricultural and Biological Sciences 20

31%

Computer Science 15

23%

Economics, Econometrics and Finance 2

3%

Save time finding and organizing research with Mendeley

Sign up for free