Food and beverage manufacturers have innovated and invested a great deal of time and resources into optimizing production and packaging processes. Although fast-moving lines mean greater production volume, they also create a challenge. High-speed food and beverage production requires employees to supervise processes, actively monitoring lines to identify defects, damaged bottles or packaging, and material handling issues – and stop production when necessary.
However, inspectors have a limited field of vision at any given time, which means a subpar product or issue that could damage equipment could escape their attention.
To keep production running and ensure higher quality, food and beverage manufacturers are turning to artificial intelligence (AI) to power machine vision inspection systems. AI’s ability to learn and categorize patterns enables machine vision technology to detect broken bottles, torn packaging, and other material handling issues that can stop production and create waste.
The benefits that AI-powered machine vision systems provide to the industry are driving growth. Grand View Research predicts the machine vision market will grow at a CAGR of 6.9 percent through 2028, with the food and beverage sector representing the highest rate of machine vision system adoption.
Although all manufacturers can face challenges with visual inspection, food and beverage manufacturers face five that are unique to their industry:
- Detecting faulty or abnormal products, including identifying partially filled packages or bottles, damaged packaging, or food products that aren’t up to standard — and stopping production in time to resolve issues.
- The need to conduct visual quality inspection by manual methods to confirm, for example, texture, color, or freshness, as well as counting or tracking products.
- Ensuring workplace safety by detecting raw materials that fall onto equipment and jam the line or malformations that could cause a subsequent process to fail and create a hazard.
- Costs of visual inspection, including full-time employees to be vigilant over their entire shift – and costs of downtime, food waste, and maintenance when they don’t stop the line immediately
AI-based visual inspection can enable food and beverage manufacturers to overcome all of those challenges. However, the solution must be designed specifically for this industry.
Because their inspection challenges are different from other manufacturers, a general visual inspection solution won’t address the needs of food and beverage. For instance, traditional machine vision applications often rely on rule-based inspection, but an AI system for a food or beverage manufacturer must be trained to identify non-standard patterns, which are more difficult for machines to detect.
In addition, the environment of a food or beverage plant is often more hot and humid than other types of manufacturing and regulations for cleanliness and hygiene are more strict. The hardware used for the machine vision inspection system must be able to meet standards and withstand use in a harsh environment.
It’s also vital that the visual inspection system can perform detection and issue alerts in real-time as products pass by at high speed. Furthermore, the data that the system collects and generates must also integrate with control, data management, and logging systems or other processes.
The best strategy for adding an AI-based visual inspection system is to deploy it with existing systems versus investing in replacing equipment. This plan not only saves the costs of new hardware and machinery, but it also saves the costs of retraining employees on a new technology and user interface.
Food and beverage manufacturers also need to consider the costs of training the algorithm for the visual inspection system, which can vary with the complexity and potential diversity of the products it will inspect, and not cut corners on this vital component of the system.
When food or beverage manufacturers achieve the right balance and deploy AI-powered machine vision systems for their operations, they can expect benefits and ROI, including:
- Inspection and detection at the speed of the line, exceeding human inspectors’ capabilities
- Automated visual inspection and decreased labor costs
- Decreased human error and waste
- Faster decision making and greater accuracy
- Continuous improvement and implementation with no downtime
To make it easier for your food or manufacturing operations to have the advantages of AI-based visual inspection, software provider ANSCENTER partnered with ADLINK to create a visual quality inspection solution for the food and beverage industry.
ANSCENTER and ADLINK’s end-to-end computer vision solution facilitates setting up a machine vision inspection system on demand while meeting speed, accuracy, safety, and security requirements.
The system, which uses the ADLINK NEON AI smart camera and ANSCENTER ANSVIS video analytics, brings together expertise in software development, hardware, and AI technology with a focus on video management. The system enables you to:
- Design deep learning models with ANSCENTER’s graphical machine learning and deep learning platforms, allowing you to achieve even complex computer vision tasks
- Manage, deploy, update or switch AI models in NEON smart cameras installed throughout production by using ANS Video Intelligent System (ANSVIS)
- Receive alerts or send control signals to video management systems (VMS) and automation control systems via various trigger types when the system detects an abnormal event
- Manage and scale the system easily and limit the need for onsite visits.
To learn more about deploying a solution that will help you enhance product quality, productivity, and safety, download the use case.