Enhanced Defect Management in Strawberry Processing Using Machine Vision: A Cost-Effective Edge Device Solution for Real-Time Detection and Quality Improvement
Source abstract
Abstract
Managing defects in agricultural fruit processing is crucial for maintaining quality and sustainability in the fruit market. This study explores the use of edge devices, web applications, and machine vision algorithms to improve defect reporting and classification in the strawberry processing sector. A software solution was developed to utilize edge devices for detecting and managing strawberry defects by integrating web applications and machine vision algorithms. The study shows that integrating built-in cameras and machine vision algorithms leads to improved fruit quality and processing efficiency, allowing for better identification and response to defects. Tested in small organic and conventional strawberry processing enterprises, this solution digitizes defect-reporting systems, enhances defect management practices, and offers a user-friendly, cost-effective technology suitable for wider industry adoption. Ultimately, implementing this software enhances the organization and efficiency of fruit production, resulting in better quality control practices and a more sustainable fruit processing industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Smart Agriculture and AI · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
References (37)
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