A Review of the Literature on Arecanut Sorting and Grading Using Computer Vision and Image Processing

Abstract

Background/Purpose: These days, the involvement of computer science in agriculture and food science is expanding. Classification and fault identification of diverse products employ a variety of Artificial Intelligence (AI), soft computing approaches, and methodologies, which contribute to higher-quality products for consumers. The position of Arecanuts in the international and Indian markets, as well as the application of computer vision and image processing to a system for categorizing and grading Arecanuts, are the main topics of this article.

Objective: The development of a system for the automated categorization of Arecanut using images is limited by difficulties. To assess the value of computer vision application for Arecanut, it is critical to taken as account the traditional and economic significance of Arecanut.

Design/Methodology/Approach: Several types of Arecanut are prone to great variation in color, texture, and form depending on the category and the area in which they are cultivated. Arecanuts are processed utilizing a variety of techniques, with an emphasis on the finished product's exterior. Here, the color, size, and texture of Arecanut are used to construct a classification or grading system.

Findings/Result: With reference to the cited significant work that has been done on other fruits as well as Arecanuts from the standpoint of computer vision. This article provided a thorough introduction to Arecanuts, computer vision, and the uses and benefits of vision-aided technologies in the grading of Arecanuts and categorization.

Result Limitations/Implications: This review is based on the detection and classification of the Arecanuts done using computer vision and AI techniques.

Originality Value: Several inline resources including review papers on Arecanut, research articles, technical books, and website resources.

Paper Type: Literature Review paper on smart auto Arecanut Sorting and Grading of Arecanut using Computer Vision and Image Processing