Citrus Classification and Grading Using Machine Learning Algorithms 
Sugumar D1, Harshavarthan V2, Kavisri S3, Aezhisai Vallavi M S4, Vanathi P T5
1Sugumar D, Department of Engineering and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore-641114.
2Harshavarthan V, Department of Engineering and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore-641114.
3Kavisri S, Department of Engineering and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore-641114.
4Aezhisai Vallavi M.S, Department of Mech, Government College of Technology-Coimbatore-641013.
5Vanathi P.T Department of Engineering and Communication Engineering, PSG College of Technology, Coimbatore-641004.
Manuscript received on 15 July 2019 | Revised Manuscript received on 20 July 2019 | Manuscript published on 30 August 2019 | PP: 2616-2621 | Volume-8 Issue-10, August 2019 | Retrieval Number: J93490881019/19©BEIESP | DOI: 10.35940/ijitee.J9349.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sorting of fruit into different grade is essential to fetch high price in the market. The fruits are graded based on height, size, area and weight. Each and every fruit changes the skin’s color in their life span. Hence, it is appropriate to grade them by processing color images of them and then applying estimation or recognition techniques on those images. Citrus (plant) grows even in temperature lands and it does not penetrate its root too deep. It is a precious commodity and used for various day to day activities. In this paper, Machine vision technique is used to sort citrus based on variety and quality. Primarily, the image is captured by a camera, placed at a particular distance. Then captured citrus image is classified into different categories, based on their color, size and quality. During the processing, the attributes are determined based on their defects in the surface of the citrus. Finally, the quality and breed are determined based on the three-color planes of color image and gray scale image respectively.
Keywords: Machine Vision, Machine Learning, Citrus, Unsupervised Algorithm, Classification
Scope of the Article: Classification