Automatic Sorting and Grading of Fruits Based on Maturity and Size Using Machine Vision and Artificial Intelligence
Rupa Lalam *
Department of Processing and Food Engineering, Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India and Dr. NTR College of Agricultural Engineering, Bapatla, Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India.
K. Lavanya
Department of Processing and Food Engineering, Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India and Dr. NTR College of Agricultural Engineering, Bapatla, Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India.
Vinoda Nadella
Department of Food Process Engineering, Dr. NTR College of Food Science and Technology, Bapatla, Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India.
B. Raj Kiran
Dr. NTR College of Agricultural Engineering, Bapatla, Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India and Department of Farm Machinery and Power Engineering, Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
This paper introduces a computer vision-based system designed for the automated grading and sorting of agricultural products based on their size and maturity. The proposed machine vision system aims to replace traditional manual methods commonly used for sorting and grading fruits. Manual inspection often struggles to ensure consistency in grading and uniformity in sorting. To address these challenges and enhance the quality of fruit grading, image processing and machine learning algorithms can be employed. Key attributes such as the fruit’s shape, color, and size can be analyzed to enable a non-destructive approach to classification and grading. Automation of these processes becomes feasible when standardized criteria for grading are established. Such systems offer faster operations, save time, and reduce manual labor, making them highly valuable to meet the increasing demand for premium-quality agricultural produce.
Keywords: Artificial intelligence, grading, machine vision technology, maturity level, sorting