Cutting Edge Food Automation with an Integrated AI Platform
Demographic studies project the world population to reach nearly 10 billion within 25 years, requiring a 70% increase in food production by 2050 compared to 2005 levels. All this while tackling resource scarcity, sustainability, climate change, and waste reduction.
To meet demand, it is not just about producing more, it is about finding smart ways to produce. Vision AI automation and intelligent robotics are already paving the way for a sustainable, efficient, and scalable approach to modern food production.
Unlike traditional machine vision, Robovision’s Vision AI platform “sees” the variability of natural, organic foods as humans do, addressing labor shortages and accelerating food production to meet growing demand.
What Robovision Can Do: Food Industry Applications
Integrating Robovision’s AI platform speeds up time to market by bringing AI expertise to machinery OEMs’ vast engineering and software capabilities. The platform is also fully scalable: it grows alongside expanding production needs.
Food Grading and Sorting
Adaptive AI models in machinery enable systems to analyze and interpret variations in size, shape, color, and surface quality, even on fast-moving production lines. The platform identifies irregular or damaged fruits, so only high-quality items reach the market.
Foreign Object Detection
The Robovision AI platform identifies foreign objects in real time. In a food production facility, it can detect materials like plastic fragments, glass shards, or metal pieces accidentally mixed with food products. This approach reduces the risk of recalls, improves consumer trust, and ensures compliance with safety regulations.
Intelligent Robotics and Automation
Robovision integrates with machinery and robotics to automate complex manual processes with precision and efficiency. In applications such as picking, sorting, handling, and packaging, it directs robotic movements with unmatched accuracy and speed, as seen with the peach sorting robot developed with partner QING.
Success Stories: Turning Vision into Proven Results
System integration partner QING used Robovision's AI platform to automate its customer's quality control.
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PackCheck integrates AI platform to improve and scale quality inspection in food and packaging
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In horticulture, fully automated machines recognize, pick, and pot plant cuttings.
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Robovision developed advanced agricultural robotics together with automation partners.
View success storyWhy Robovision AI in Food Production?
Streamlined Integration
Robovision is flexible and can be deployed on the cloud, on-premises, or a combination. This allows for easy implementation of highly scalable deployments and fast, real-time processing capabilities.
Built to Scale
Inherently scalable, Robovision’s platform evolves alongside operations, future-proofing the system for the end user—the platform already powers more than 1,000 robots in 40+ countries across multiple industries.
Adaptable
The AI platform is suitable for dozens of quality control applications across various industries. Build and streamline models across production lines with a single platform: defect and contamination detection, picking and sorting, blend accuracy, anomaly detection, many different grading applications, and more.
Stay State-of-the-Art
Robovision’s R&D roadmap means access to the latest and greatest AI technologies and the team delivers ongoing AI expertise. That’s dedicated support without the overhead, resulting in a better ROI for businesses.
Empower Operations and Data Science Teams
The platform is built so production teams can independently create, maintain, adjust, and re-train AI models without needing external technical teams. It relieves data science teams from the grunt work of managing in-house solutions.
Full Control, Full Ownership
Creators retain ownership of the AI models they build with Robovision, strengthening competitive positioning by leveraging intellectual property as a valuable asset.
How it Works
Data Annotation
Label and annotate visual data.
Model Training
Upload data to train new models.
Model Optimization
Evaluate the reliability of your data and identify the most impactful samples for re-labeling.