Rice Grain Quality Detection
The analysis of grain type, grading, and their quality attributes is still done by skilled persons manually. This method leads to complexity because it depends on several factors like human factors, working conditions, and the rate of cleaning and recovery of salvage. This may be overcome by using image processing techniques. Testing quality is gaining importance in the food industry for classifying and grading grains. Since manual testing is time-consuming, costly, and inaccurate, machine vision-based quality analysis of rice grains is preferred. In deep learning-based testing, we take both physical (grain shape and size) and chemical characteristics (amylose content, gel consistency) for the evaluation and grading of rice grains. In this proposed algorithm, the quality and grading of rice grains were analyzed using the average values of the features extracted from the network.
1. Image thresholding
- Support vector machine
3. Edge Detection methods
1. Inaccurate results
2. Time-consuming process
3. Require manual analysis
The growth in technology is making people more demanding towards the things they use and consume, this is the reason why everything is becoming automated. The use of Image processing techniques for testing the quality of rice grains is inexpensive and is less time-consuming. In this method, the quality of grain is tested based on its size and shape features.
1. Less time consumption
2. By using deep learning we can get the accurate results
3. High performance
1. Agricultural purposes
2. Research purposes.
- Matlab 2018a and above
- Deep learning toolbox
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