One of the oldest and largest sectors of the global economy is agriculture. To determine the calibre and standard of a product, it is crucial to test the quality of agricultural commodities. One of the key agricultural products that is significant in many nations is rice. The standard of examining the quality standards of rice production, rice is a crucial component.
The rice’s quality is vital for people because it directly affects their health. Rice quality can be determined based on a number of physical characteristics, such as grain size and shape, moisture, whiteness, bulk density, and content. The traditional method of testing the rice quality determined by seeing the five senses of the rice physical characteristics causes subjective analysis results and resulting in mixed assessments.
A precise system that can distinguish between different types of rice and give rice quality analysis and accurate test result analysis is needed to assess the rice’s quality. When using traditional techniques, analysis leaves a lot of room for human mistake.
To assist the testing system that was previously used conventionally, a new technology is created as an alternative to determine the quality of rice based on digital image processing. Based on the criteria of the form and size of the rice grains, the results of the rice grain lengths identification will yield a classification of rice types, indicating whether it is intact, incomplete, or damaged.
This blog’s goal is to research rice quality detection technology utilising a digital image processing algorithm to assess both the quantity and quality of rice. It is an image that a computer has analysed to create information of the calibre of rice. To be more precise, the boundary region of each item is determined using edge detection algorithm. The creation of a rice quality detection system that uses digital image processing to evaluate rice quality is the specific goal to be attained.
What is Image Processing Technique in Rice Quality Analysis?
A digital image can be processed into information using an image processing approach. Digital image processing greatly benefits from knowing the size and shape of things. If the object’s edge can be located, both can be gotten. The edge detection methodology is the approach used in rice quality detection study to count the number of rice grains and categorise them according to the ratio of the rice average length to width.
Image Acquistion
JPEG, PNG, JPG, and other image input formats are used in digital image processing. In order to make it easier to identify individual rice grains, images of rice-related objects are taken against a blue background.
Image pre-processing
This step involves using a filter to get rid of noise for better image quality and digital image sharpening. To separate or segment rice grain objects from their backdrop, the threshold algorithm is applied.
Image of the Shrinkage Morphological Operation
This type of erosion is used to distinguish the rice grain’s tactile characteristics without sacrificing the integrity of any one feature. After the erosion process comes the widening process. The goal of widening is to restore the original shape of deteriorated features without recombining the individual components.
Edge Detection Image
Edge detection is done at this stage to assist in identifying the rice grain boundaries. This method can locate edges with a 1 pixel thickness.
Object Measurement Image
This step is to count the number of rice grains. The edge detection technique is then performed to the image after the number of rice grains has been counted, and the result is the end point value for each grain. Combining the end points allows for the measurement of each grain’s length and width using the calliper technique. In order to determine the rice grain ratio, the results of the length and width values are processed.
Image Classification of an Object
Rice grain parameter data that have been previously identified are needed for the rice grain quality classification stage. A digital picture object’s rice grains are measured and tallied.
Conclusion
Based on the shape of the rice grains in a digital image format, a digital image processing technique employing the clever edge detection algorithm can calculate and determine the quality of the rice grains. Using a blue background and randomly put rice samples, image analysis is applied using RiceX Rice Quality Software.
To calculate the size ratio of the total number of grains identified, the testing system collects data on the number of grains found in digital images and measures each grain’s length. In the case of rice quality detection, digital image processing demonstrates how precise information about the quality of rice grains may be gleaned from the input of a digital image.