Apr 9, 2021

AppHarvest buys Root AI for $60m

AI
Technology
Robot
Farming
Tilly Kenyon
2 min
Indoor farming company AppHarvest announced this week that it had acquired Root AI, with the deal valued at around $60 million
Indoor farming company AppHarvest announced this week that it had acquired Root AI, with the deal valued at around $60 million...

Kentucky based agtech and farming company AppHarvest have purchased artificial intelligence firm Root AI and their robotic universal harvester Virgo for approximately $60 million. AppHarvest is investing about $10 million in cash and the remaining balance in AppHarvest common shares to acquire Root AI. 

According to AppHarvest, the ‘game-changing advantage’ of Root AI’s technology is in the data the robots can collect as they harvest. 

“Farming as we’ve known it is broken because of the increasing number of variables such as extreme weather, droughts, fire and contamination by animals that make our food system unreliable. Indoor farming solves many of those challenges, and the data gathered can exponentially deliver more insights that help us predict and control crop quality and yield,” said AppHarvest Founder and CEO Jonathan Webb.

How does Virgo work? 

Virgo is the world’s first universal harvester, which can be configured to identify and harvest multiple crops of varying sizes, including tomatoes, peppers, cucumbers and more delicate fruits such as strawberries, among others.

The robot uses a set of cameras, combined with an infrared laser, to create a 3D colour scan of an area to determine the work it can carry out. For example, once it maps the tomatoes, it assesses their orientation and determines if they are ripe enough to be picked. 

The scan enables the robot to find the least obstructive and fastest route to pick the crop ahead of the robotic arm and gripper’s arrival. The robot can identify hundreds of tomatoes in a fraction of a second without having to connect to the cloud. Virgo keeps score of its success rate like a video game. A built-in feedback mechanism constantly evaluates its efficiency, so it learns how to harvest any given configuration of fruit most effectively.

By gathering data using AI, growers can use real-time information to improve various sustainability efforts, such as detecting and eliminating pests naturally. This, in turn, helps indoor farms successfully grow chemical pesticide-free fruit and vegetables.

(Image: AppHarvest)

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Jun 23, 2021

Google launches Visual Inspection AI tool for manufacturers

AI
Google
Manufacturing
ML
3 min
Google has launched Visual Inspection AI, a new Google Cloud Platform solution designed to help reduce defects during the manufacturing process

Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped. 

Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale. 

How does it work? 

The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.

Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI. 

  • Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor. 
  • Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps. 
  • Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers. 
  • Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
  • Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.

Industry use cases

The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing. 

Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”

Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture. 

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