Title

Ripe fruit detection and classification using machine learning

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Article

Source Title

International Journal of Emerging Trends in Engineering Research

Volume

8

Issue

5

First Page

1845

Last Page

1849

Publication Date

1-1-2020

Abstract

One of the most important sectors in any country is the agricultural sector. However, in some countries, farmers and fishermen have limited technology compared to other developed countries. One of the effects of limited technology is the low quality of crops, fruits, and vegetables. This is because the quality of the products is only assessed depending on external factors like appearance, shape, color, and texture, which can be prone to human error. Determining the quality and ripeness level of fruit requires consistency, which can be hard and tedious for humans when it becomes repetitive work. This paper aims to present various methods and approaches on how ripe fruit detection and classification can be made easier and more convenient using machine learning and machine vision algorithms. Furthermore, this study presents systems that can be utilized in pre and post-harvest analysis. This paper aims to provide solutions using computer applications to help farmers have lesser manual labor yet more accurate data and results in the evaluation of crops. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

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Digitial Object Identifier (DOI)

10.30534/ijeter/2020/60852020

Disciplines

Electrical and Computer Engineering

Keywords

Machine learning; Fruit—Harvesting—Machinery; Computer vision

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