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Automated Object Sorting Using Image Processing and Machine Learning Techniques

  • Unique Paper ID: 179956
  • Volume: 11
  • Issue: 12
  • PageNo: 9066-9073
  • Abstract:
  • The integration of image processing and machine learning has transformed automated object sorting, enabling significant advancements across industries like recycling, man- ufacturing, agriculture, and logistics. In waste management, AI-driven models have enhanced the accuracy and efficiency of sorting recyclables, addressing challenges in manual pro- cesses. Manufacturing has benefited from machine vision systems that streamline quality control and defect detection, improving workflow efficiency. In agriculture, automated grading systems leverage visual features such as color and texture to evaluate and categorize produce. Cost-effective solutions are emerging, making advanced sorting systems accessible for small and medium- sized enterprises. Recent innovations also include multi-modal approaches, combining image analysis with depth-sensing for greater precision in dynamic environments. However, challenges such as limited training data, hardware integration, and energy consumption remain. This review highlights the transformative potential of these technologies and the ongoing need to address barriers for widespread adoption.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 9066-9073

Automated Object Sorting Using Image Processing and Machine Learning Techniques

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