Image processing applications in food industry

Traditional quality control methods in the food industry are mostly manual, leading to significant challenges such as variability of results, human error, and limited scalability. As the industry shifts towards automation and digitization, the potential of image processing as a solution for enhancing quality control processes becomes increasingly evident. This study is dedicated to applying deterministic image processing techniques, specifically those not involving machine learning algorithms, to streamline quality inspections in food production. The appeal of this approach lies in its simplicity, lower computational requirements, and ease of integration into existing production systems. Deterministic image processing, which employs defined algorithms for specific tasks, offers predictable and repeatable results. This thesis delves into techniques such as thresholding (including global and adaptive methods), edge detection (using operators like Sobel and Canny), and morphological processing to identify defects, contaminants, and structural inconsistencies in food products. Applying such methods ensures consistent quality checks and significantly reduces the reliance on human inspectors, thereby minimizing oversight and human error. For instance, thresholding allows for the segmentation of objects from the background, making it an essential tool for detecting surface anomalies and contamination. Edge detection methods facilitate the identification of product shapes and possible irregularities, contributing to more accurate quality assessments. Accordingly, no custom algorithm was written. Instead, reports were generated using Azure's Custom Vision API based on the captured images. The research methodology involves using Epson BT-350 smart glasses integrated with a custom-built Unity application for real-time image capture and analysis. Operators inspected pre-determined high-risk zones, collecting images processed using rule-based algorithms. These deterministic methods were chosen for their effectiveness in real-time applications, where processing speed and reliability are critical. The results from initial trials indicated that this system could accurately detect surface-level defects and contamination, with error rates reduced by up to 30% compared to manual inspections. This significant improvement not only highlights the potential for substantial cost savings but also underscores the enhanced operational efficiency that can be achieved through the use of deterministic image processing. One of the key findings of this research is the feasibility of implementing deterministic image processing methods in routine quality control without the need for complex machine learning models. While machine learning can offer adaptive and highly accurate solutions, it often requires extensive training data, higher computational resources, and specialized model development and maintenance expertise. In contrast, deterministic methods are more accessible and easier to implement and maintain, making them suitable for mid-sized and smaller food production facilities. This study also underscores the importance of understanding the specific challenges associated with image processing in a food production environment. Factors such as lighting variability, product movement, and diverse product types can affect the accuracy of automated inspections. By addressing these challenges and implementing adaptive techniques, such as local thresholding, which adjusts to changes in lighting conditions, the research ensures more robust performance, providing a comprehensive understanding of the complexities in the field. In conclusion, this thesis contributes to the field by demonstrating that non-machine learning-based image processing techniques can substantially benefit quality control in the food industry. These benefits include improved detection of defects and contamination, reduced human error, and enhanced operational efficiency. The system's adaptability and low computational overhead make it a promising and, importantly, practical and feasible solution for food manufacturers looking to modernize their quality control processes without significant infrastructure changes.