Can artificial intelligence (AI) and machine learning help save the world's bees? That's the hope of scientists who are scrambling to reverse the dramatic declines in bee populations. Bees are in trouble, but we're not quite sure why. It could be the overuse of insecticides; air pollution; warming temperatures; the varroa destructor mite; or even interference from electromagnetic radiation. Or it could be a combination of all these factors.
Science hasn't been giving us a tremendous amount of good news these days. We've screwed up the environment so badly, it's hard to even call it an environment anymore. And that's coming back to bite (or sting) us: Bee populations, which we rely on to pollinate our crops, are plummeting. But science is also coming to the rescue, by gluing QR codes to bumblebees' backs and tracking their movements with a robotic camera. Researchers have created a system that tracks individual bees as well as the dynamics of whole colonies exposed to imidacloprid, a neurotoxin that belongs to the infamous neonicotinoid group of pesticides.
Why data could be the deciding factor in Africa's agricultural transformation. The world has a palm oil problem. It's a global, billion-dollar industry and its end result is irreversible environmental damage, ranging from deforestation and fires, to the loss of species such as tigers, pygmy elephants and orangutans. Palm oil is used in 50% of the products we buy (think bread, shampoo, soaps and even chocolate) due to the fact that it is the highest-yielding vegetable oil crop. Yet, in a country like Uganda, where 80% of the population is involved in agriculture as a way of life, many Ugandans farm oil palm on small plots, barely making a living.
In a quiet corner of rural Hampshire, a robot called Rachel is pootling around an overgrown field. With bright orange casing and a smartphone clipped to her back end, she looks like a cross between an expensive toy and the kind of rover used on space missions. Up close, she has four USB ports, a disc-like GPS receiver, and the nuts and bolts of a system called Lidar, which enables her to orient herself using laser beams. She cost around £2,000 to make. Every three seconds, Rachel takes a closeup photograph of the plants and soil around her, which will build into a forensic map of the field and the wider farm beyond. After 20 minutes or so of this, she is momentarily disturbed by two of the farm's dogs, unsure what to make of her.
PlantVillage, a research and development project, based at Penn State University, is beginning to bring artificial intelligence to these smaller farms. Scientists at PlantVillage, in collaboration with international organizations, local farm extension programs and engineers at Google, is working to tailor A.I. technology for farmers in Tanzania who have inexpensive smartphones. The initial focus is on cassava, a hearty crop that can survive droughts and barren soil. But plant disease and pests can reduce crop yields by 40 percent or more. PlantVillage and International Institute of Tropical Agriculture have developed a simple A.I. assistant, called Nuru ("light" in Swahili).
KANSAS CITY, Kan. – Companies around the globe are leveraging innovative technologies and artificial intelligence to make more informed decisions and better run their businesses. This week, Dairy Farmers of America, a national cooperative owned by dairy farm families across the U.S., announced an investment in SomaDetect, a dairy technology startup that will help farmers utilize artificial intelligence to more closely monitor the health of their herd and improve milk quality. "This is a potentially game-changing technology for our farmers and the industry as it allows dairy farmers to know the health of each cow and quality of milk in real time," said David Darr, president, farm services at DFA. "With access to better data, our farmers can make more knowledgeable decisions, which is a huge value." With SomaDetect's technology, farmers can easily evaluate components of interest in raw milk, including fat, protein, somatic cells, progesterone and trace antibiotics. While the technology continues to be refined for commercialization, the platform provides cost-effective, instant analysis, which enables farmers to make rapid and proactive decisions related to the overall health and management of their cows.
Imagine one hundred years ago if farmers had access to huge volumes of information about the soil profile of their land, the varieties of crops they were growing, and even the fluctuations of their local climate. This kind of information could have prevented an environmental crisis like the Dust Bowl of the 1920s in the American Midwest. But even ten years ago, the idea that farmers could have access to this kind of information was unrealistic. For the team behind the CGIAR Platform for Big Data in Agriculture, farming is the next frontier for using artificial intelligence (AI) to efficiently solve complex problems. The team--which includes biologists, agronomists, nutritionists, and policy analysts working with data scientists--is using Big Data tools to create AI systems that can predict the potential outcomes of future scenarios for farmers.
Dutch scientists have developed robot bees which could help pollinate plants without the use of insects. Researchers at Delft University of Technology in the Netherlands believe they may have solved the problem of climate change or pesticides killing off the creatures. The DelFly Nimble's wings beat at 17 times per second to power the robot at speeds over 15 miles per hour (25kph). However, they share an uncanny resemblance to robot bees that are hacked and turned into killing machines in the popular science fiction series Black Mirror. It uses off-the-shelf components, making it cheap to build, and scientists say it could be used in a host of real-world applications.
Olsen, Alex, Konovalov, Dmitry A., Philippa, Bronson, Ridd, Peter, Wood, Jake C., Johns, Jamie, Banks, Wesley, Girgenti, Benjamin, Kenny, Owen, Whinney, James, Calvert, Brendan, Azghadi, Mostafa Rahimi, White, Ronald D.
Robotic weed control has seen increased research in the past decade with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for arable croplands, ignoring the significant weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust detection of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the highly complex Australian rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust detection methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper also presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification performance of 87.9% and 90.5%, respectively. This strong result bodes well for future field implementation of robotic weed control methods in the Australian rangelands.