Real-Time Deep Learning based Tomato Fruit Quality Control in Conveyor Belt
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DOI:
https://doi.org/10.59287/ijanser.352Keywords:
Deep Learning, Quality Control, Tomato, Real Time, Object DetectionAbstract
Quality control is crucial in ensuring that the finished goods adhere to the specified requirements and standards in a manufacturing or production context. One key area where quality control is essential for sorting and separating goods on a conveyor belt. Automating this process can significantly improve the efficiency and accuracy of quality control. Manual quality control is a labor-intensive and time-consuming process that can lead to human error and inconsistencies. On the other hand, automation allows for the use of technology, such as machine vision and artificial intelligence, to quickly and accurately sort and separate goods based on predefined criteria. Automation also enables real-time monitoring and data collection, which can provide valuable insights into the manufacturing process and help identify areas for improvement.
Additionally, automation can improve the speed and efficiency of the sorting process, allowing for greater throughput of goods and increasing productivity. Furthermore, automation can reduce workplace injuries, as manual sorting and separation can be physically demanding and lead to repetitive strain injuries. In summary, automating quality control in the sorting and separating of goods on a conveyor belt is essential for improving the accuracy and efficiency of the process, reducing the risk of human error and workplace injuries, and providing valuable insights into the manufacturing process.
This paper used real-time deep learning-based tomato fruit quality control in conveyor belts with IP cameras. User interface and object detection models were used. For an automated separation procedure, tomato form, quality, size, and other variables were assessed in real-time. YoloV4 tiny, SSDMobileNet, and Faster RCNN models were utilized, and real-time accuracy of 88, 87, and 92 percent was attained. We carried out hyperparameter optimization, fine-tuning, and data augmentation.
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