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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: | Areca Nuts Classification based on size and color using CNN model |
| Authors Name: | Yash Nagda , Madhukar , Lohit B Nijaguni , Sulabh Anand , Kavitha |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217427
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| Published Paper Id: | IJNRD2404198 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Areca nut, also known as betel nut, is a tropical crop. India holds the position of the world's second-largest producer and consumer of areca nuts, which undergo various challenges throughout their life cycle. Farmers traditionally rely on their visual senses to identify diseases in the nuts. This paper explores multiple image processing techniques aimed at categorizing areca nuts based on properties like color and texture. In real-world applications, computer detection models have gained popularity for their quick, efficient, accurate, and transparent testing capabilities. Unlike manual separation methods employed thus far, this study proposes the use of a sophisticated color sorting mechanism that considers external nut properties such as color, texture, shape, and size. The captured image is analyzed using various approaches to extract relevant information, including the classification of areca nuts based on their size and color. The efficient planning of areca nuts requires consideration of all these traits. The classification models developed in this research serve as valuable tools for stakeholders in the areca nut industry, including farmers, traders, and processors, enabling them to optimize sorting processes and tailor their products to meet specific market demands. When given training data, it categorizes the data into healthy and unhealthy areca nuts based on colour and quality. Areca nut is separated using CNN classifiers. |
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| Cite Article: | "Areca Nuts Classification based on size and color using CNN model", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b815-b820, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404198.pdf |
| Downloads: | 00034 |
| 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:IJNRD2404198 Registration ID: 217427 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: b815-b820 Country: BENGALURU, KARNATAKA, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404198 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404198 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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