5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

Hepatitis A infections across multiple states have inspired retailers like Wal-Mart and Trader Joe’s to recall some brands of organic strawberries from their shelves and led the FDA to begin an investigation. As of Monday June 13th, 18 individuals have been affected across California, Minnesota, and North Dakota. More than a dozen of these shoppers have been hospitalized as a result of their infections.

Investments in computer vision technology can help agribusinesses and food manufacturers of all types spot signs of trouble early and stop costly, potentially deadly recalls before they happen. Deployed across the production and manufacturing cycles, these models can detect hazards ranging from contaminants and foreign objects to defective equipment and non-compliant behavior. Organizations capture hundreds of thousands of hours of visual data in the form of video footage and imagery every day, and computer vision allows these businesses to put this data to work for process transformation.

The Strawberry Recall: Just One of the Year’s Many

The strawberry recall isn’t the only major incident to affect agribusinesses, manufacturers, retailers, and shoppers this year—or even the most recent. This month alone, foods including cheese, baked goods, and crab meat have been recalled due to potential infection with contaminants like listeria. Even large, name-brand corporations are not safe from the myriad risk factors that lead to recalls: So far this year, ubiquitous products like Jif peanut butter and Skittles have been removed from stores. Though the FDA occasionally initiates recalls itself, this action is typically a voluntary decision on the part of the manufacturer. In many instances, companies are able to proactively initiate recalls before any consumers report issues. 

With round-the-clock monitoring from vision AI models, businesses can take a proactive approach to investigations and more easily, accurately identify hazards. Models for recognizing hazards, identifying their location, measuring their severity, and automating alerts empower businesses to conduct thorough investigations with AI analytics as a dependable source of ‘evidence.’

Traditional approaches for validating product quality and recognizing hazards are obviously not enabling businesses to maintain risk-averse processes, avoid recalls, and keep both their reputations and customers as safe as possible. The computer vision experts at Plainsight are prepared to help agribusinesses and manufacturers make a change by developing custom AI models and deploying them across the manufacturing cycle. 

5 Ways Computer Vision Helps Stop Recalls

  1. Recognize hazards including foreign objects, contaminants, and malfunctioning equipment.
  2. Introduce automated alerts to ensure quick remediation of any issues in the production process. 
  3. Monitor facilities with an unblinking computer eye to recognize hazards including dangerous behavior, missing personal protective equipment, and more. 
  4. Improve tracking and traceability within facilities.
  5. Establish an auditable record of visual data to facilitate model training, regulatory compliance, and allow for quick responses to disruptions.

These use cases don’t just apply to agribusinesses and food manufacturers. Contamination with pathogens, allergens, and foreign objects affect drug production as well, leading to additional FDA investigations. Over the last several months, the FDA has reported recalls for both over-the-counter and prescription pharmaceutical products including nasal sprays and allergy medications.

The images below showcase object detection models trained to recognize product and packaging defects in pharmaceuticals. Recognizing these concerns early can not only empower quicker remediation, but it allows businesses to proactively address the potential causes of contamination or damage to their products. This means stopping recalls before they occur and avoiding the financial and reputational repercussions.

More Computer Vision Use Cases for Agribusinesses

Agribusinesses sit at the nexus of numerous evolving issues. Many are experiencing outsize effects of climate change and they all serve a customer base that is increasingly discerning about the food they consume and the organizations they do business with. It all demands a transformative approach to managing and leveraging visual data. Plainsight’s models for agriculture include:

Precision Counting and Tracking: Even trained eyes are imperfect, leading to inaccurate counts and problems further down the line. Precision counting from a vision AI solution ensures a new standard of accuracy and paves the way for monitoring throughout the full production cycle.

Livestock Health Monitoring: Advanced models can recognize subtle signs of health concerns in livestock animals—even recognizing changes in facial expressions—to provide for a higher level of healthcare and a higher quality, more ethical food product. 

Ripeness Detection: Growers can deploy models for detecting and evaluating their crops to ensure timely harvests and even enhance genetic decision making to optimize the quality and shelf life of their fruits and vegetables.  

Read about how Plainsight enabled JBS, one of the world’s largest food processors, to attain near-total accuracy in its counts and automate additional manual tasks

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What could enhanced visibility and traceability mean for your organization and its operations? Find out by scheduling a demonstration with Plainsight’s computer vision experts to discuss how custom vision AI models can lead to process automation, enhanced operational analytics and more.

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