B G, Channabasavanna (2025) SmartSnacks: AI-Driven Fruit Freshness & Nutrition Detection System. International Journal of Innovative Science and Research Technology, 10 (5): 25may1153. pp. 2161-2165. ISSN 2456-2165

[thumbnail of IJISRT25MAY1153.pdf] Text
IJISRT25MAY1153.pdf - Published Version

Download (745kB)

Abstract

In the wake of increasing health awareness and food safety demands, accurately determining the freshness and nutritional value of fruits remains a persistent challenge. Traditional inspection methods are either subjective or require costly instruments, making them inaccessible to everyday consumers. This paper presents SmartSnacks, an innovative, AI- driven system that leverages computer vision and deep learning to assess fruit freshness and estimate nutritional quality using just an image captured via a smartphone. The system employs a Convolutional Neural Network (CNN) to classify fruits into various freshness categories and detect spoilage indicators. It then cross-references scientific nutritional databases to estimate nutrient degradation based on freshness levels. SmartSnacks offers real-time, user-friendly insights that support healthier eating habits, reduce food waste, and promote transparency in food quality. Designed for accessibility and scalability, this system holds significant potential for consumers, retailers, dietitians, and agriculture stakeholders alike.

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Editor IJISRT Publication
Date Deposited: 14 Jun 2025 06:55
Last Modified: 14 Jun 2025 06:55
URI: https://eprint.ijisrt.org/id/eprint/1171

Actions (login required)

View Item
View Item