Rice Grading and Quality Analysis

To make sure that the rice grains fulfill the required quality requirements, rice grain grading and quality analysis are crucial tasks in the food sector. For this objective, image processing techniques have been widely adopted since they provide an effective and non-destructive means of analyzing the physical properties of the rice grains. In light of this, this research offers a methodology for analyzing the quality of rice grains based solely on the length and width measurements derived from digital photographs. Steps for picture acquisition, preprocessing, feature extraction, and classification are all included in the suggested methodology. To get the images ready for additional analysis, the preprocessing procedure entails image cleaning, enhancement, segmentation, scaling, and normalization. Relevant properties including length, width, aspect ratio, and perimeter are extracted from the rice grain photos during the feature extraction process. A machine learning technique, more precisely a support vector machine, is used in the classification step to divide the rice grains into several quality grades according to their length and width measurements. After testing, the suggested methodology was determined to have adequate accuracy and to be suitable for further research. Using only the length and width measurements derived from digital images, the results illustrate the efficacy and precision of the suggested methodology in rice grain quality analysis.