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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Smart Food Quality detection using ML |
| Authors Name: | Amruta Sadhu , Ayush Patel , Riddhi Mehta , Bhasha Anjaria |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_215957
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| Published Paper Id: | IJNRD2403421 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | One of the most critical aspects of quality assurance is inspecting products for defects before they are sold or shipped. A good product is more vital than having more of the same item for a customer’s enjoyment. the client has a significant role in determining the quality of a product. Another way to think about quality is as the total of all the characteristics that contribute to the creation of items that the client enjoys. Recently, the application of machine vision and image processing technology to improve the surface quality of foods has increased significantly. this is primarily because these technologies make significant advancements in areas where the human eye falls short. this means that, by utilizing computer vision and image processing techniques, time-consuming and subjective industrial quality control processes can be eliminated. Its excess food we waste has a large impact on various environmental factors. This research paper discusses how to check and assess food using picture segmentation and machine learning. then, we propose the development of a user-friendly mobile application that allows customers to conveniently capture and analyse images of their ordered food items in real-time. This application provides instant feedback on the freshness status of the food, enabling customers to take proactive measures if any concerns arise. Segmentation of the image is carried out using the K-means clustering technique. These algorithms determine if a food is fress or not. Finally, freshness detection is carried out using the Machine learning algorithm. An application is proposed to display the results of checking food items by the device. |
| Keywords: | quality assurance, product defects, machine vision, image processing, food surface quality, computer vision, segmentation, machine learning, freshness detection, mobile application |
| Cite Article: | "Smart Food Quality detection using ML", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.e151-e154, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403421.pdf |
| Downloads: | 00037 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2403421 Registration ID: 215957 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: e151-e154 Country: vadodara, Gujarat, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403421 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403421 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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