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Abstract – Rice stands as a favored and extensively consumed cereal grain in Asian countries, while also enjoying global accessibility. Within the rice market, the overarching determinant of milled rice lies in its quality, an attribute that assumes heightened significance in the context of import and export trade. Rice samples often harbor assorted extraneous elements such as paddy, chaff, damaged grains, weed seeds,
and stones. The principal objective of the proposed approach is to introduce an alternative avenue for quality control and analysis, characterized by reduced expenditure in terms of effort, cost, and time. Image processing emerges as a pivotal and technologically advanced sphere marked by significant
advancements. Image processing maneuvers images to execute targeted operations, thereby refining and enhancing the desired outcome. Moreover, this technique enables the extraction of valuable insights from input images. This study strives to develop image processing algorithms with a specific focus on segmenting and identifying rice grains. By harnessing image processing algorithms, it becomes possible to efficiently analyze the quality of grains based on their size. This paper furnishes a solution for the classification and assessment of rice grains, predicated on their dimensions and morphology, through the application of image processing techniques. While prior research has focused on the morphological attributes of grains, encompassing parameters such as area and shape, these endeavors often struggle to yield a generalized formula capable of classifying diverse rice varieties due to the considerable variance in shapes and sizes. In a distinctive departure, this paper augments the analysis by incorporating Fourier features extracted from grain images, thus augmenting the accuracy of classification outcomes.
Key Words: —agriculture, image processing, morphological operations, edge detection, quality analysis, object classification , deep learning, food quality detection
INTRODUCTION
The agricultural industry, spanning across centuries, remains expansive and steeped in tradition. The challenge of assessing grain quality has persisted throughout history. This project introduces a pioneering solution for the evaluation and grading of rice grains by harnessing image processing techniques. Traditionally, the commercial grading of rice hinges on grain size classification, categorizing grains
as full, half, or broken. The assessment of food grain quality has conventionally relied on human inspectors employing visual scrutiny. However, the decision-making abilities of human inspectors are susceptible to external influences such as fatigue, subjectivity, and personal biases. The integration of image processing techniques offers a transformative approach, eliminating the aforementioned challenges while remaining non-destructive and costeffective. This methodology transcends human limitations,enhancing objectivity and accuracy. The subsequent discussion outlines the procedure deployed to ascertain the percentage quality of rice grains. Rice quality, in essence, is a composite of both physical and chemical attributes. Physical characteristics encompass grain size, shape, chalkiness, whiteness, milling degree, bulk density, and moisture content. On the other hand, chemical attributes involve gelatinization temperature and gel consistency, contributing to the comprehensive assessment of rice quality. This study centers on the development of image processing algorithms aimed at effectively segmenting and identifying rice grains. The utilization of image processing algorithms proves to be a highly efficient approach for gauging grain quality based on its size. The paper introduces a comprehensive solution for grading and assessing rice grains, focusing on grain size and shape through the application of image processing techniques. Particularly, an edge detection algorithm is employed to discern the boundaries of each grain, employing a technique that identifies the endpoints of individual grains. Subsequently, a caliper is utilized to ascertain the length and breadth of rice grains. This methodology stands out for its minimal time requirement and cost-effectiveness. In contrast, conventional methods employed for measuring grain shape and size, such as the grain shape tester, dial
micrometer, and graphical method, tend to be protracted International Research Journal of Engineering and Technology and cumbersome. These methods typically allow for the measurement of the dimensions of one grain at a time, yielding results that are not only time-consuming but also susceptible to human errors. Consequently, there is a pressing need for greater precision to fulfill customer expectations and overcome the limitations posed by manual procedures. Numerous studies have previously delved into the analysis of morphological characteristics of grains, encompassing factors like area and shape. However, the vast diversity in shapes and sizes across different rice varieties precludes the generalization of a uniform formula for classifying all rice types. Addressing this challenge, this paper introduces an
additional dimension by extracting Fourier features from grain images, complementing the spatial features and culminating in an elevated level of accuracy for classification purposes. This paper aims to employ image processing algorithms to analyze grain quality based on size has become a prevalent
and effective methodology. This approach facilitates the assessment and classification of rice grain quality by leveraging advanced image processing techniques. By focusing on the dimensions of rice grains, these algorithms contribute to a comprehensive understanding of their quality attributes. This technique holds the potential to revolutionize the conventional methods of evaluating grain quality, providing a more accurate and efficient means of classification. The remaining part of the paper is organized as follows.
Section 2 contains the Literature Survey. The Proposed Model is discussed in Section 3. Section 4 contains the Experiments and Results. Lastly, the Conclusion and Future Directions is presented in Section 5.
