Fruit Quality Detection using Image Processing

Registration ID: IJNRD_221231

Published ID: IJNRD2405362

DOI: Click Here to Get

Rishikesh Hatekar , Lalit Thakare , Dipesh Dhamecha , Priyanka Uike , Dr. S.R. Gupta

Keywords—Image processing, Machine Learning, Deep Learning, CNN, Decision Tree Classifier

The country's primary source of economic development on a global scale is the agriculture industry. The fruit's appearance plays a crucial role in describing its size, shape, and colour as well as its quality whether it is rotten or fresh. We can determine the fruit's shelf life that is, how many days it will last by utilising its quality. Farmers can use this information to determine when to harvest fruit to prevent it from becoming overripe. Additionally, this will support planning aimed at lowering crop losses and raising farmer incomes. This study presents the widely used methods of image processing, machine learning, and deep learning technologies for fruit quality recognition and maturity categorisation. In this paper we have used CNN as a primary model to identify fruits quality and compared it with Decision Tree Classifier.

Published Paper   E-Certificate

Rishikesh Hatekar, Lalit Thakare, Dipesh Dhamecha, Priyanka Uike, & Dr. S.R. Gupta (May-2024). Fruit Quality Detection using Image Processing. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), d573-d577. https://ijnrd.org/papers/IJNRD2405362.pdf

Volume 9 Issue 5, May-2024

Pages : d573-d577

Paper Reg. ID: IJNRD_221231

Published Paper Id: IJNRD2405362

Downloads: 000121984

Research Area: Computer Engineering 

Country: Amravati, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405362.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405362

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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