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I think that machine learning is used for vertical farming because it is easier to collect samples, since there is no need to travel great distances to sample a crop.

The flavour of a vegetable is much more complex than what a human can explain, so I would guess that it takes into account much more scientifically focussed information. This may be colour profiles, light exposure, gaseous byproducts, mineral/vitamin content and texture could possibly determined via a type of image recognition.

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Greens leaving the grow environment on route to harvesting and packaging in Fifth Season's vertical ... [+]
Tom O'connor and Fifth Season

With the global demand for food escalating, vertical farms are becoming a critical component of agriculture's future. They use robotics, machine learning and artificial intelligence (AI) to automate farming and perfect the growing of greens and vegetables. With steady growth, the vertical farming market was had an estimated value of $4.4 billion in 2019 and is expected to reach $15.7 billion by 2025.

Fifth Season is a vertical farm in Pittsburgh that uses super-stack software and robotics to run their fully automated farming systems. And, by combining big data and AI, they have created the optimal grow recipe that determines the best flavor for the plants they grow.

"The role of AI in determining flavor is to leverage big data and AI to ensure you achieve the target flavor - sweetness, spiciness, bitterness, total degree of flavor and texture," said Austin Webb, CEO of Fifth Season.

"Our plant's individualized grow recipe is the unique mix of the different LED lights," said Webb. "The plants go through the grow room with a QR code that communicates that plant's route and tells the automated system where each plant needs to be throughout the process."

Webb says their super stack system, which serves as 'the brain' of the vertical farm maps, maps out each plant's route through the grow room based on its grow recipe and then moves the plants where they need to go.

"We use AI and data to find improvements in all aspects of crop quality, even beyond what humans think they know about flavor profiles. We call this proactive, deterministic growing compared to traditional farming, including greenhouse growing, where you have to be reactive based on weather and sunlight conditions," said Webb. "We then leverage human/chef feedback on what tastes the best and what texture is best and [..] combine that qualitative data with the 26K quantitative data points for every tray of greens per lifecycle."

"From there, we tweak our grow recipes to build the best flavor. For some vegetables, like tomatoes, experts have leveraged Brix scores, but [..] we measure flavor quality based on these factors: sweetness, spiciness, bitterness, the total degree of flavor, texture and color," said Webb. "Humans don't need to guess what iron content or Brix score is best; the brain in our farms can do that. Humans tell the grain what tastes best, and the brain will compute and tweak the grow recipes from there."

Darryn Keiller, CEO and founder of WayBeyond, says that to impact flavor, you either have to change the genetics of the crop or alter the existing biochemical profile.

"For example, growers can impact flavor by adjusting light and nutrients, which can then enhance the texture (crunch, thickness) or flavor (increased sweetness or bitterness)," said Keiller. "Once you determine the key characteristics you want in a crop, you then use machine learning or AI to automate and optimize the production process for consistent growth and be responsive to changing consumer preferences."

"Currently, vertical farms utilize seed stock bred for outdoor farming. Using AI technology, they can create their breeding stock (or lines) better suited for indoor environments. It's about refining your research and development and creating genetics perfect for your environment and management practices while ensuring continuous improvement of commercial production. The potential is huge."

Webb adds that many indoor growers sought to build an outdoor farming system that can thrive indoors; however, Fifth Season chose to apply smart manufacturing principles to agriculture that would enable them to grow food in a new way.

"We grow more than 15K pounds a week of fresh food with 90% less water than what would be required to grow that amount of fresh food on a traditional outdoor farm - and that is done on a footprint of just 25,000 square feet," added Webb.

Webb believes that scaling viable vertical farming operations that can crack code on both the technology and the consumer experience to deliver consistently fresh, nutritious and clean produce changes the consumer shopping significantly.

"It changes our entire definition of what fresh can and should taste like; it changes the ease of access and availability and convenience to fresh food, which brings so much value to consumers," said Webb. "It's another avenue for shoppers to access the freshest, highest-quality food at retail with produce that has a much longer shelf life than we're accustomed."

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According to Fifth Season, the company has raised $70 million in funding to date.
... Read More

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AI Is Learning To Understand How Vegetables Taste

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With the global demand for food escalating, vertical farms are becoming a critical component of agriculture's future. They use robotics, machine learning and artificial intelligence (AI) to automate farming and perfect the growing of greens and vegetables. With steady growth, the vertical farming market was had an estimated value of $4.4 billion in 2019 and is expected to reach $15.7 billion by 2025.

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https://youtu.be/kMNSAhsyiDg

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This Technology fits into a wide variety of industry uses. 3D software is used by Engineers, Artists, Movie producers, Hobbyists, and many more and can be used for rapid prototyping, quick visualisation aswell as final product developtment.

This machine learning de-noiser allows the noise generated when rendering an image to be greatly reduced by learning from the millions of previous renders aswell as the cloud library it uses to learn de-noising. Machine learning was the correct option to use for a program such as this as due to its extremely large requirement and desired flexability writing rules for such a program (although done before) results in a slow, buggy and non-optimal program.

The inputs to the model is the entire rendered scene, however the de-noiser picks out the black specs that are seens as "noise" and then the surrounding pixels are analysed aswell as the overall light direction and the noise is removed via machine learning