Digital grading and sorting of fruits. (2021)

• Record Type: Journal Article
• Authors: Kishan Das Menon, H.; Achal Raj Jain, M.; Janardhan, V.; Deepa, D.
• Is Part Of: Materials today. Volume 43:Part 6(2021)
• Page Start: 3749
• Page End: 3758
• Publication Date: 2021
• Subjects: Image processing -- Deep Learning -- Residual neural networks -- Fruit Maturity -- Automation; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.10.989
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: This paper delves into the application of Image Processing and Artificial Intelligence in the field of Agriculture, of which quality analysis and grading is a major concern. Sorting fruits as per the right grade is cumbersome and a labor heavy task, which makes it challenging for farmers with limited resources. In order to develop a system that can address these challenges, automation comes to our rescue. Automation being implemented on fruit raises certain concerns such as retention of quality of fruits and their safety while they are being processed by such techniques. Hence, in this paper a system has been proposed that is nondestructive and also causes no harm to the fruit. The proposed methodology is designed using Image processing and Deep Learning techniques which are apt and are the best possible solutions for the above-mentioned concerns. This paper discusses several strategies like Edge Detection, Image Contours, Residual Neural Networks (ResNet) to implement the system.