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Now that Root AI’s robot has mastered the art of picking oblong vegetables as well as oblate shaped berries, could this technology help enhance global food security?

Image: Root AI

In recent months, the coronavirus pandemic has highlighted frangibility in the global supply networks; particularly those involved in food security. Hallmarks of digital transformation, automation, and artificial intelligence, are being tapped to create a decentralized 21st century food chain.

On Thursday, the agricultural robotics and artificial intelligence company Root AI announced new capabilities to its AI-enhanced robotic harvester as well as investments totaling more than $7 million. Now that the AI-enhanced robotic harvester has demonstrated enhanced dexterity to tackle crops of various shapes and sizes, the technology could help shore up these vulnerabilities.

Ripe for the automated picking

In the past, Root AI has provided glimpses of its robo-harvester, known as Virgo, picking ripe tomatoes off the vine. In the latest video titled “Going Cross-Crop,” Virgo is shown picking cucumbers and strawberries in the field. In the accompanying press release, the company asserted that Virgo was the “world’s first robot ever to replicate a person’s ability to harvest multiple crops.”

In an indoor agriculture environment, Virgo can be situated on a track in between rows of various crops. As it navigates a greenhouse, the robot leverages a host of sensors as well as artificial intelligence to analyze crop positions and ripeness and then uses a specialized gripper to pick produce once it’s ready.


Image: Root AI

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The machine sees its environment in 3D using intelligent motion sensing. This data allows Virgo to determine an optimal route, through vines, leaves, other unripe crops, to pluck its target. As Root AI CEO Josh Lessing explained, Virgo uses more than computer vision to see its environment and plan accordingly.

“We need to go beyond computer vision that finds fruit in three-dimensional space. We do that, but on top, we have a layer of computer perception that then plans how to go about grasping that fruit. How do I navigate through the environment and then land my fingers on that target to effectively pick it? To move with authority, the same way people look at an object that they want to pick, the mind needs to create a plan,” Lessing said.

The company is building solutions to enable its fleet of systems to learn on the job, so to speak, and then share these insights with other robotic harvesters in the field.

“We’re building artificial intelligence algorithms that understand how to do a task, but as it works, learns how to do it better, and then shares those learnings across a fleet of systems,” Lessing said.

Whether it’s an apple in a tree or a strawberry in a bush, these are both similar challenges from an identification, planning, and picking perspective, explained Lessing. The robotic gripper and software can be swapped for different crops, however, the underlying principles surrounding planning and picking will enable application across crops.

“It’s the same gripping concept, but for your human hand, when you grab a cucumber or, which is a cylinder, or you grab a tomato, which is an orb, you pose your fingers in different arrangements to grab them. Virgo is doing the same. So to go between different crops, you would swap the hand, which is a quick operation, and then use a different software package that is trained to go after the vegetable you’re interested in,” Lessing said.


Image: Root AI

Labor shortages and supply chain disruptions

In recent years, farmers have faced struggles with labor shortages throughout the agriculture industry. In California, more than half (56%) of participating farmers were “unable to hire all the employees they needed for production of their main crop at some point during the past five years,” according to a California Farm Bureau Federation report.

As a result, 56% of the farmers surveyed reported using labor-reducing technology. About half the farmers who incorporated mechanization solutions adopted these practices due to labor shortages.

In the US, the landscape of farming has changed dramatically over the decades shifting from a model made up primarily of numerous small farms to a realm dominated by large-scale farms. This increasingly rare agrarian skill set also presents a set of challenges for supply chains.

“It’s also increasingly challenging to find master growers. Fewer and fewer people are training to become these virtuosos of crop care and yield optimization. This is the moment in human history to start transferring those learnings to artificial intelligence, so we have a sustainable infrastructure that guarantees we have enough to eat,” Lessing said.


Image: Root AI

In recent months, the coronavirus has shone a light on the vulnerabilities inherent within the global food supply chain. Due to market disruptions, farmers were forced to dump millions of gallons of milk, bury crops, and plow edible produce into fields. To assist, a number of organizations are looking to create regional supply chains.

As we reported in July, AppHarvest is currently building North America’s largest greenhouse in Appalachia to address supply-side concerns that enhance sustainability over time. Root AI is similarly focused on addressing these logistical and sustainability challenges.

“What we need is the capacity to grow any of a number of staple crops that are in our diets, close to the cities where they’re consumed and to be able to do that using sustainable agriculture practices. So that agriculture is no longer one of the largest polluters on the planet,” Lessing said.

SEE: Chatbot trends: How organizations are leveraging AI chatbots (free PDF) (TechRepublic)

The cost of many of the technologies integral to indoor agriculture has dropped substantially in recent years; especially the price of LED light arrays. This has set the stage for market competition in the industry. Similarly, Lessing explained that robotics has undergone a technological revolution of sorts over the last five years with technologies ranging from cloud-enabled model training to high-resolution depth sensors.

These advancements have reached a tipping point where organizations can start leveraging these solutions to create sustainable, decentralized food networks, despite human labor shortages. In tandem, these technologies could buttress the availability and security of human civilization’s central needs.

“The vision and the goal of the business is for food production to become automatic. People need healthcare, they need housing, they need food. At Root, we are focused on solving one of those problems: food. And if we can build the automated food infrastructure that the world needs, we will have forever solved one of humanity’s biggest challenges,” Lessing said.

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A Human-Computer Duet System for Music Performance


Despite the popularity of virtual musicians, most of them cannot play together with human musicians following their tempo or create their own behaviors without the aid of human characters. The authors of a recent paper created a virtual violinist having these characteristics.

A Human Computer Duet System for Music Performance

Image credit: 刘睿忱 via Wikimedia (CC BY-SA 4.0)

It can track music and adapt to the human pianist’s tempo varying with time and with performance, making the two voices harmonized. The virtual musician’s body movements are generated directly from the music. The motion generator is trained on a music video dataset of violin performance and a pose sequence synchronized with live performance is generated.

These features mean that the human musician can practice, rehearse, and perform music with the virtual musician like with a real human, by following the music content. The proposed system has successfully performed in a ticket-selling concert, where a movement from Beethoven’s Spring Sonata was played. 

Virtual musicians have become a remarkable phenomenon in the contemporary multimedia arts. However, most of the virtual musicians nowadays have not been endowed with abilities to create their own behaviors, or to perform music with human musicians. In this paper, we firstly create a virtual violinist, who can collaborate with a human pianist to perform chamber music automatically without any intervention. The system incorporates the techniques from various fields, including real-time music tracking, pose estimation, and body movement generation. In our system, the virtual musician’s behavior is generated based on the given music audio alone, and such a system results in a low-cost, efficient and scalable way to produce human and virtual musicians’ co-performance. The proposed system has been validated in public concerts. Objective quality assessment approaches and possible ways to systematically improve the system are also discussed.