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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
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Total Authors : 21252
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| Paper Title: | Deep Neural Network-Based Grain Adulteration Detection |
| Authors Name: | Ranjana B K , Palguna M S , Dr. Nandini G |
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IJRTI_189783
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| Published Paper Id: | IJRTI2405020 |
| Published In: | Volume 9 Issue 5, May-2024 |
| DOI: | |
| Abstract: | Grains, including dal, play a crucial role in maintaining overall health and well-being. However, dal, a staple protein source in Indian cuisine, is susceptible to contamination due to the reliance on manual sorting within the industry. This paper presents a novel method for automating the detection of dal adulteration using deep learning techniques. It proposes a fusion of machine vision with deep neural networks, utilizing ResNet and SqueezeNet, to categorize various dal varieties based on distinctive attributes such as shape, size, and color. This innovative approach overcomes the limitations of conventional human inspection methods and the impracticality of lab-based techniques. The current practices for detecting food adulteration, including the presence of formalin in dal, involve intricate sample preparation and advanced technologies, rendering the process time-consuming and challenging. In response, our method employs a Convolutional Neural Network (CNN)-based YOLO (You Only Look Once) architecture to precisely forecast the concentration of formalin. The primary objective is to streamline the manual inspection process, thereby accelerating the procedure, enhancing accuracy, and improving efficiency. The system captures images of dal samples, extracts essential features such as grain color and size, and identifies adulterated dal based on pixel-level analysis. The classification is accompanied by a confidence score up to 94% the image corresponds to toor dal. Subsequent sorting is conducted based on these identified color and size characteristics. |
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| Cite Article: | "Deep Neural Network-Based Grain Adulteration Detection", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.132 - 138, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405020.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 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: IJRTI2405020
Registration ID:189783
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 132 - 138 Country: banglore, karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2405020 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2405020 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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