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Computer Vision for Food Defect Detection and Classification

This B.Tech Agricultural Engineering project is based on the recent AI/ML research direction ‘Computer Vision for Food Defect Detection and Classification’. The project connects agricultural engineering with artificial intelligence, machine learning, deep…

Food Processing Projects
Project Overview This B.Tech Agricultural Engineering project is based on the recent AI/ML research direction 'Computer Vision for Food Defect Detection and Classification'. The project connects agricultural engineering with artificial intelligence, machine learning, deep learning, IoT, computer vision, drone analytics, or RAG-style decision support. Students can use the linked 2023-onward paper/source as the academic base and convert it into an implementation-focused final-year project with sensors, datasets, dashboards, mobile/web interfaces, prediction models, or prototype automation.
Research Paper Title Computer Vision for Food Defect Detection and Classification
Research Paper / PDF Link Open Paper / PDF
Year 2024
Project Area Food Processing Projects
Project Type Food Defect Detection
Required Tools / Software Python, OpenCV, TensorFlow/PyTorch, CNN/YOLO/U-Net, image dataset, Streamlit
Main Features / Working Principle Detect visible defects in food products using CNN/YOLO models
Expected Output A defect detection prototype for food images
Possible Add-ons Add defect localization and severity score
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This B.Tech agricultural engineering project resource helps students connect a recent AI-based research direction with a practical implementation plan, tools, expected output, and possible extensions.

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