Optimizing Date Sorting in Consumer-Packaged Goods (CPG) Assembly Lines Using Machine Learning and Image Recognition

Authors: Devender Yadav

DOI: https://doi.org/10.5281/zenodo.14838598

Short DOI: https://doi.org/g84g64

Country: USA

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Abstract: This paper examines the complex challenges associated with managing dates (the fruit) in the Consumer-Packaged Goods (CPG) industry, specifically focusing on the essential task of sorting them by quality, ripeness, and size during the assembly process. The existing methods, which frequently depend on manual labor, are insufficient regarding speed and accuracy. Inefficiencies result in inconsistencies in product quality, heightened waste from spoilage, and adversely affect overall profitability. We propose a solution that integrates machine learning algorithms with advanced image recognition technology. This system is intended to analyze the visual characteristics of dates, including color, texture, and size, with speed and accuracy, thereby facilitating an efficient and precise automated sorting process. Initial analysis and testing suggest that this system can significantly improve operational efficiency, reduce fruit waste, and ultimately enhance the financial performance of CPG companies engaged in date processing and packaging. This study analyzes the existing methods of date sorting, presents the suggested technological solution, and investigates its potential effects on the date-centric consumer packaged goods sector.

Keywords: Dates, Date Fruit, CPG, Assembly Line Optimization, Machine Learning, Image Recognition, Quality Sorting, Ripeness Assessment, Waste Reduction, Supply Chain Efficiency, Automation

Paper Id: 232106

Published On: 2023-11-04

Published In: Volume 11, Issue 6, November-December 2023

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