Authors : Dr. Chandran Maasi; Iswarya; Akash S.; Saran M.; Sugan R.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/379h2k2j

Scribd : https://tinyurl.com/yc3pw4sc

DOI : https://doi.org/10.38124/ijisrt/26May318

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Health monitoring and dietary management applications are widely popular nowadays. Knowledge about nutrition information is mandatory to maintain a healthy lifestyle. However, finding the nutritional value of every fruit and vegetable is often challenging when relying solely on manual searches. Therefore, it is essential to have an easy and efficient way to access nutritional information for fruits and vegetables. Artificial Intelligence (AI) has the capability to detect nutrition information effectively and efficiently. The proposed system presents an AI-based approach for detecting nutrition information using deep learning-based object detection. The nutrition detection process utilizes the YOLO (You Only Look Once) model to classify fruits and vegetables from live camera footage. By training on a diverse dataset containing various fruit and vegetable types, it ensures precise and reliable recognition across a wide range of fruits and vegetables.

Keywords : Artificial Intelligence (AI), Deep Learning, YOLO, Object Detection, Nutrition Analysis.

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