Characterisation and Identification of Rice Grains through Digital Image Analysis

Hobson, D.M.; Carter, R.M.; Yan, Yong

doi:10.1109/imtc.2007.379116

Cited by 25 publications

(10 citation statements)

References 3 publications

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“…Picture of rice characteristics [5] In 2002, Y. N. Wan developed an automatic grain kernel handling system in which 1296 singularized kernel images per minute were taken for machine vision inspection. He developed a Windows-based software program for rice quality inspection.…”

Section: Fig 32mentioning

confidence: 99%

Unified Approach in Food Quality Evaluation Using Machine Vision

Parmar

Jain

Modi

2011

Advances in Computing and Communications

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Abstract. The paper presents a unified approach for quality evaluation of food using image processing and machine vision. In this paper basic tool is combination of computer and machine vision for image analysis and processing through which fast and accurate quality is achieved that too with the help of nondestructive method. Machine vision in food has broadened its range of applications from grains, cereals, fruits to vegetables including processed products as well as spices in which there is a high degree of quality achieved as compared to human vision inspection. In this paper we quantify the qualities of various food products and figure out features which are directly or inversely affect the quality of the food product. Based on these features a generalized formula of quality is proposed to be used for quality evaluation of any type of food product.

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Section: Fig 32mentioning

confidence: 99%

Unified Approach in Food Quality Evaluation Using Machine Vision

Parmar

Jain

Modi

2011

Advances in Computing and Communications

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“…Research on pre-harvest attributes has demonstrated the significant within-field variability of paddy grain quality (protein and moisture content) [1][2][3], while in the post-harvest milling process, quality assessment has focused on physical characteristics, such as immature grain, varietal purity, and discolored grain (identification of damaged grain kernels) [4], inspecting and identifying rice varieties [5], degree of milling [6], yield prediction, and percentage of whole kernels [7], etc. To date, though, no research has been done on quality assessment of the material attributes of the paddy during harvesting.…”

Section: Introductionmentioning

confidence: 99%

Double Lighting Machine Vision System to Monitor Harvested Paddy Grain Quality during Head-Feeding Combine Harvester Operation

et al. 2015

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A machine vision system to evaluate harvested paddy grain quality during harvesting using double lighting was developed. The prototype consisted of a low-cost web camera and two lighting systems: a ring white LED for front lighting, and a flat dome white LED light for backlighting. Both lighting systems were arranged in a coaxial axis, making the system simple, compact and easy to handle. The aim of the system is to analyse the captured images and determine the amount of unwanted materials (rachis branch, grass and leaves, and stems) and damaged grain (brown and crack rice) present in the paddy as it is being harvested. In this paper, we introduce the first step in the development of the system: the design and selection of components to optimize the performance of the system to monitor harvested paddy grain quality. The idea would be to mount the system on top of

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“…There are sticky rice seeds mixed in. There are many existing methods currently used for rice identification, such as certain physical characteristics in minimum average length and aspect ratio are specified explicitly in the standards [4], [5] or DNA testing [6], [7]. Though these reliable methods are expensive, tedious, laborious, and destructive of the sample material.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive Identification of unmilled rice using digital image analysis

Punthumast

Auttawaitkul

Chiracharit

et al. 2012

2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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In this paper, digital image analysis is applied for non-destructive classification of rice and sticky rice seeds that are mixed together. It is a difficult task because of the similar surface color of the seeds. This paper presents an automatic classification method based on RGB color features. Hardware of image capturing is designed using back light source in order to maximize the contrast between the rice seeds and their background. RGB histogram is then calculated. The rule of classification between rice seed and sticky rice seed are created. Almost 97% of rice seeds are identified correctly. The correct classification rates for two rice varieties are: rice seeds 'Jasmine' 96.34% and sticky rice seeds 100%.

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Characterisation and Identification of Rice Grains through Digital Image Analysis

Cited by 25 publications

References 3 publications

Unified Approach in Food Quality Evaluation Using Machine Vision

Unified Approach in Food Quality Evaluation Using Machine Vision

Double Lighting Machine Vision System to Monitor Harvested Paddy Grain Quality during Head-Feeding Combine Harvester Operation

Non-destructive Identification of unmilled rice using digital image analysis

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