• Aditya
• Manuj.P
• Rabiyabasari Kagadagar
• Dr. Anand Jatti
• Dr. K.B.Ramesh

Food adulteration on a holistic approach is jeopardizing consumer food safety and health. Leveraging the vision of low power AI with computer vision and deep machine learning techniques such as CNN and image processing, this paper gives a comprehensive review on all these aspects related to the efficient and precise identification of the foreign particles and the adulterated food materials [1]. This review delves more into the percentage of food adulteration which includes contamination and the amount of adulterant mixed with food products emphazing the threats or the risks involved. This paper also dwells more on image processing, image segmentation, feature extraction using CNN algorithms which is executed by Jupyter Python Notebook. It explains more on high performing CNN models for developing a food adulteration detection system.

Image Processing, CNN models, adulteration, image segmentation, GPU

"Food Adulteration Detector using Image Processing and CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.i367-i372, July-2023, Available :http://www.jetir.org/papers/JETIR2307849.pdf

"Food Adulteration Detector using Image Processing and CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppi367-i372, July-2023, Available at : http://www.jetir.org/papers/JETIR2307849.pdf