International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Fruit Quality Detection using Deep Learning for Rotten and Fresh Classification |
|---|---|
| ABSTRACT | Fruit quality assessment is essential for ensuring consumer safety and supporting the food industry in reducing waste. Traditional manual inspection is time-consuming and prone to human errors. This study proposes a deep learning–based image classification model for distinguishing rotten and fresh fruits. A Convolutional Neural Network (CNN) is trained on a labeled dataset containing images of various fruits, enabling automatic and accurate detection of spoilage characteristics such as discoloration, fungal spots, and texture differences. The model achieves high accuracy, demonstrating its potential use in automated sorting systems and quality control processes. |
| AUTHOR | MONISHA A R, HEMANTHA KUMAR B N |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311082 |
| pdf/82_Fruit Quality Detection using Deep Learning for Rotten and Fresh Classification.pdf | |
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