To map every gene in the human body, scientists around the world collaborated for more than a decade, from 1990 to 2003. Thanks to their work, entire vistas of medicine have opened up, from new diagnoses to drug regimens tailored to an individual's genetic makeup. What if, posits Dorn Cox, a produce farmer in New Hampshire, the same could be done for the world's soil? With detailed knowledge of the nutrients in their soil, farmers could better tend their dirt and significantly reduce negative environmental impacts. For example, they could better learn what to plant and when, or how to maximize soil nutrients and track carbon content (more carbon in the soil means less carbon in the atmosphere).
The agriculture and allied sectors are considered the bedrock of India's economy. With farming employing almost half of India's workforce, Agri Gross Domestic Product (GDP) can be considered the engine of growth for the economy. The global need to produce 50% more food by 2050 cannot be accomplished if only 4% of the land is under cultivation.The vulnerabilities arising from climate change, coupled with the risk of increased dependency on unsustainable agriculture practices, can lead to agricultural distress. Artificial Intelligence (AI), along with other digital technologies, will play a key role in modernizing agricultural activities and realising the goal of doubling the farmer's income by 2022. The global'AI in agriculture' market size is expected to be worth USD 2.6 billion by 2025.
"We're trying to change how people run agronomic research. Instead of establishing a small field plot, running statistics, and publishing the means, what we're trying to do involves the farmer far more directly. We are running experiments with farmers' machinery in their own fields. We can detect site-specific responses to different inputs. And we can see whether there's a response in different parts of the field," says Nicolas Martin, assistant professor in the Department of Crop Sciences at Illinois and co-author of the study.
Pictured above is a general purpose dual RBG camera system, designed by Carnegie Mellon University researcher George Kantor and his R&D team, to collect high quality images in agricultural environments. Collected images can feed crop-specific artificial intelligence methods that extract measurements such as crop yield, maturity, or disease incidence. Generally speaking, artificial intelligence (AI) enabled technologies are infiltrating every aspect of our daily lives, from the smartphones everyone is carrying around everywhere to places where maybe AI is best left on the sidelines (have you heard about Alexa's newest integration into a connected shower head device?). As you all know, the greenhouse has not been spared from the "AI Revolution" – not in the slightest – and one area we're hearing the technology is making believers out of skeptics is in the legal cannabis space, where high profit margins and a youthful, tech-focused grower demographic creates the perfect storm for early-stage ag tech adoption. If you disagree with that statement, I invite you to spend a day next year at the massive MJBizCon show in Las Vegas, which at this point is basically a smaller, more focused CES show for cannabis producers, and then let me know if you still don't think cannabis growers are all that innovative or on the cutting edge of technology adoption.
If you go down to the farm today, you'll likely find it packed with sensors, drones and remote management systems run by iPhones, iPads and other mobile devices. In fact, we're only one or two Siri Shortcuts away from voice-controlled farms equipped with remotely controlled irrigation, livestock and crop management solutions and blockchain-based crop lifecycle analysis tools. Most of this technology exists, but cost constrains deployment. Leading the digital transformation of agriculture are apps, such as: Agrellus, an online marketplace for agriculture, xarvio Scouting App for better crop management, FieldNET Mobile to control water pivots remotely, Yara ImageIT, which turns your iPhone into a crop nutrient testing system, AgSense, and GrainTruckPlus. There are many more apps for agriculture available at the App Store – including Tudder, the "Tinder for farm animals."
For the first time, artificial-intelligence experts have created a place to collaborate, Climate Change AI. Some are mining the massive data of remotely-sensed emissions streams. Some are accelerating materials discovery for solar fuels by combining machine learning and physics to figure out a proposed material's crystal structure. Some are using system optimization to consolidate freight and route it more efficiently. Others are deploying agricultural robots armed with spectral cameras in hope of reducing fertilizer use in farming.
Last week we took a look at computer vision; what it is, how it works, and some of the applications for computer vision in agtech. In case you missed last week's article, computer vision or machine vision typically refers to the use of machine learning or deep learning algorithms in image processing to allow a machine to "see" and identify objects around it. Different computer vision technologies may use a variety of camera types to act as the machine's "eyes" depending on the imaging requirements. In the case of fully autonomous vehicles, an accurate computer vision system is essential. In typical vehicles, hazard detection, navigation, and object avoidance all depend on a human operator.
The aging adage, "there's an app for that," is evolving into, "there's a robot for that." More and more automation is finding its way to the market for household chores like cleaning floors, and now that innovation is in farmer's fields with Odd.Bot, an automatic weeding robot. Odd.Bot made an appearance at the Consumer Electronics Show (CES) in Las Vegas last month with an informational booth and the weed-plucking device on display. Martijn Lukaart, Founder and CEO, explains that Odd.Bot is currently intended for use in organic farming fields to make the weed-pulling process easier for large farms who currently do all the work by hand. Many large-scale farmers have already invested in a platform that allows workers to lay face down on a bed as they are propelled through the rows of crops.
It's a cloudy day in early October and I'm circling my rented Jeep Wrangler around a maze of industrial buildings in Hamilton, Ohio. Hamilton is a small city 30 miles north of Cincinnati with a population of just over 62,000 people. Like much of Ohio, farming is important here. I'm on my way to a farm called 80 Acres, but it isn't the sprawling midwestern wheat field you're picturing in your mind. This tech-centric farm is indoors, housed entirely in a nondescript 10,000-square-foot warehouse.
Amid the cacophony of concern over artificial intelligence (AI) taking over jobs (and the world) and cheers for what it can do to increase productivity and profits, the potential for AI to do good can be overlooked. Technology leaders such as Microsoft, IBM, Huawei and Google have entire sections of their business focused on the topic and dedicate resources to build AI solutions for good and to support developers who do. In the fight to solve extraordinarily difficult challenges, humans can use all the help we can get. Here are 8 powerful examples of artificial intelligence for good as it is applied to some of the toughest challenges facing society today. There are more than 1 billion people living with a disability around the world.