The Rotten Fruit Detection System is an advanced image analysis platform designed to automatically identify and classify rotten fruits. Leveraging state-of-the-art machine learning techniques, the system analyzes images of fruits to accurately determine their freshness status. By using computer vision algorithms, the platform can detect subtle visual cues and patterns associated with decay, discoloration, and other signs of spoilage in fruits.
The system utilizes a pre-trained deep learning model specifically trained on a large dataset of both fresh and rotten fruit images. When a new fruit image is uploaded, the system processes the image through the model, extracting relevant features and comparing them against learned patterns. Based on the analysis, the system provides real-time feedback on whether the fruit is fresh or rotten.
With its accurate and efficient detection capabilities, the Rotten Fruit Detection System enhances food safety, quality assurance, and customer satisfaction in the fruit industry. By incorporating cutting-edge image analysis techniques, the platform contributes to reducing economic losses and supporting sustainable food practices.
| Name | Matric |
|---|---|
| ADAM WAFII BIN AZUAR | A20EC0003 |
| AHMAD MUHAIMIN BIN AHMAD HAMBALI | A20EC0006 |
| FARAH IRDINA BINTI AHMAD BAHARUDIN | A20EC0035 |
| MUHAMMAD DINIE HAZIM BIN AZALI | A20EC0084 |
| MIKHEL ADAM BIN MUHAMMAD EZRIN | A20EC0237 |