Optimizing the Object using Real-Time Computer Vision and Neural Network
Roberto Aguero1, Noah Olson2, Justus Selwyn3
1Roberto Aguero, Department of Computer Science, John Brown University, Siloam Springs, USA.
2Noah Olson, Department of Computer Science, John Brown University, Siloam Springs, USA.
3Justus Selwyn, Professor, Department of Computer Science, John Brown University, Siloam Springs, USA.
Manuscript received on 07 July 2025 | First Revised Manuscript received on 12 July 2025 | Second Revised Manuscript received on 20 September 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025 | PP: 35-42 | Volume-15 Issue-1, October 2025 | Retrieval Number: 100.1/ijeat.F468414060825 | DOI: 10.35940/ijeat.F4684.15011025
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Poultry and food processing manufacturing units have several automated and monitoring processes of the food that go into packaging. However, at the packaging section, manual human intervention is needed to ensure that the correct amount of food, in terms of count and weight, is placed into every package. This is not without error. Hence, an accurate real-time measurement of sample object counts and weight is critical for optimizing processing efficiency and automating workflows in the production chain. The system identifies the food object, counts it, and weighs it before packaging the batch. In this work, we present a novel approach that integrates an Ultralytics You Only Look Once (YOLO) v10 model, a convolutional neural network (CNN)-based object detection framework, with an automated weighing system and dashboard to optimize quality control.
Keywords: Machine Learning, Object Detection, Computer Vision, Neural Networks, Ignition SCADA.
Scope of the Article: Artificial Intelligence and Methods