1. Food Security : Grading The Quality Of Variety Of Rice Grains Through Digital Image Processing Rajlakshmi Ghatkamble Research Scholar at School of Research and Innovation Department of Computer Science , C. M. R University, Bangalore Karnataka, India
Abstract or Introduction
Conclusion
Results
  Rice is the staple food of India. Adulteration of rice is a major issue and has distinct effect on its yield. Proper inspection and grading of rice is very important. In present grain-handling system, grain type and quality, are rapidly assessed by visual inspection which is, however, tedious and time consuming. The decision-making capabilities of grain inspector can be seriously affected by his/her physical condition, mental state caused by bias and work pressure. Working conditions such as improper lighting, climate, etc can also affect the results of visual inspection. To prevent fraudulent mislabelling of rice grains, an automated imaging system that can extract the visual features of rice grains and classify those using imaging techniques will simplify this task.
In the present work a digital imaging approach has been devised in order to investigate different types of characteristics to identify the rice varieties. Two different common rice varieties were used in tests for defining. These include existing standards for rice length, area and aspect ratio features of rice. It successfully shows the effectiveness of compactness as its features. When the data base of this work can recognize the rice’s, which has been trained the data in number of time; and hence it has been identified. With proper selection of software tools, we can design a low cost tool for quality analysis of rice grains which provides all relevant parameters about rice grains by image analysis. Hence we aim for the accurate classification of micro calcification of different types of rice varieties for using in the testing and producing the accurate result.
Objective
In the present work a digital imaging approach has been devised in order to investigate different types of characteristics to identify the rice varieties. Two different common rice varieties were used in tests for defining.
Existing and Proposed System
Testing Table for Performance Analysis
Testing
Rice Type
File Name
Rice Count
Actual Type
Classified Type
Accuracy
Total Rice Accuracy 3
Sona Masuri
Sona Masuri_1 44
90.90%
91.16% 4
Sona Masuri_2 35
91.42% 5
Big
Rice
Big Rice_1
Big Rice
95.34%
90.72% 6
Big Rice_2
86.11% 7
Indraini
Rice_1
50%
40% 8
Rice_2
30% 9
Basmati
100% 10
Total Project Accuracy
84.376%
References
In the present grain-handling system, grain type and quality are rapidly assessed by visual inspection.
This evaluation process is however tedious and time consuming.
The decision-making capabilities of human-inspectors are subjected to external influences such as fatigue, vengeance, bias etc.
[1]. Rajlakshmi Ghatkamble, “Matching And Grading Of Different Varieites Of Rice Grains Through Digital Image Processing”, International Journal of Computer Science Trends and Technology (IJCST) – Volume X Issue X, Year 2016, C.M.R University, Bangalore, Karnataka, India
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Image
Acquisition
Preprocessing
Thresholding
Feature Selection and Extraction
Counting the number of grains
Image Segmentation
Neural Network
Decision and Classification