Across midwestern farms, if Girish Chowdhary has his way, farmers will someday release beagle-sized robots into their fields like a pack of hounds flushing pheasant. The robots, he says, will scurry in the cool shade beneath a wide diversity of plants, pulling weeds, planting cover crops, diagnosing plant infections, and gathering data to help farmers optimize their farms. Chowdhary, a researcher at the University of Illinois, works surrounded by corn, one of the most productive monocultures in the world. In the United States, the corn industry was valued at $82.6 billion in 2021, but it -- like almost every other segment of the agricultural economy -- faces daunting problems, including changing weather patterns, environmental degradation, severe labor shortages, and the rising cost of key supplies, or inputs: herbicides, pesticides, and seed. Agribusiness as a whole is betting that the world has reached the tipping point where desperate need caused by a growing population, the economic realities of conventional farming, and advancing technology converge to require something called precision agriculture, which aims to minimize inputs and the costs and environmental problems that go with them. No segment of agriculture is without its passionate advocates of robotics and artificial intelligence as solutions to, basically, all the problems facing farmers today.
First of all, when growing agricultural products, which is the first step of food production, in the future, consumers are expected to grow and use plants directly for cooking without using pesticides at home. There are already many companies that have introduced growers of plants that make this possible. Samsung Electronics and LG Electronics are examples. Vegetables are automatically grown by placing the seeds in the inner shelf of the planter, which is similar in size to a household refrigerator. Temperature, humidity and nutrients are automatically controlled by AI (artificial intelligence). Heliponics, a start-up from Purdue University in the United States, has also introduced the'Gropot' indoor plant grower. Artificial intelligence automatically adjusts the temperature and humidity of agricultural products... The entire process of distribution and transportation is tracked seamlessly using blockchain technology.
IoT-enabled Agricultural (IoTAg) monitoring is smart, connected agriculture's fastest-growing ... [ ] technology segment projected to reach $4.5 billion by 2025, according to PwC. AI, machine learning (ML) and the IoT sensors that provide real-time data for algorithms increase agricultural efficiencies, improve crop yields and reduce food production costs. According to the United Nations' prediction data on population and hunger, the world's population will increase by 2 billion people by 2050, requiring a 60% increase in food productivity to feed them. In the U.S. alone, growing, processing and distributing food is a $1.7 trillion business, according to the U.S. Department of Agriculture's Economic Research Service. AI and ML are already showing the potential to help close the gap in anticipated food needs for an additional 2 billion people worldwide by 2050.
It's a collision course: We'll need to feed another 2 billion people by mid-century, even as climate change threatens our ability to produce food. Georgia, Florida and other Southeastern states must play a central role if we're to feed the world and simultaneously protect the planet. If we fail to rise to this challenge, we risk a multitude of problems driven by hungry people. And a new report released from the UN Intergovernmental Panel on Climate Change only heightens the concern. Our best chance to get off this collision course is through innovation.
John Deere has been in business for nearly 200 years. For those in the agriculture industry, the company that makes green tractors is as well-known as Santa Claus, McDonald's, or John Wayne. Heck, even city folk who've never seen a tractor that wasn't on a television screen know John Deere. The company's so popular even celebrities such as Ashton Kutcher and George Clooney have been known to rock a Deere hat. What most outsiders don't know is that John Deere's not so much a farming vehicle manufacturer these days as it is an agricultural technology company.
Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT). To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness.
Artificial intelligence (AI) is helping prevent overfishing in a bid to protect the world's rapidly dwindling supply of edible marine species. A new project uses AI to improve the identification and measurement of fish species in Africa's Nile Basin. The software can help scientists understand fish population density more quickly than human observers. It's part of a larger effort to harness AI to improve sustainability across a wide range of industries. "The promising thing about AI is that it now allows us to do tasks that would be time-consuming or impossibly complex using traditional methods, with considerably more speed and efficiency," Andrew Dunckelman, head of impact and insights at Google.org, the search giant's charitable arm, told Lifewire in an email interview.
Artificial intelligence (AI) is on the cusp of driving an agricultural revolution, and helping confront the challenge of feeding our growing global population in a sustainable way. But researchers warn that using new AI technologies at scale holds huge risks that are not being considered. Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities' worth of people. Imagine that all authority for tilling, planting, fertilising, monitoring and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up.
Utilizing technology is not a new practice in the agriculture and food and beverage sectors. We've already seen intelligent robots that can weed and pick apples, biometrics that can offer real-time animal information, and sophisticated equipment that can pump milk from cows with minimal human intervention. However, as technology improves, more clever technologies have appeared to help farmers increase their yield and speed up crop production. AI in the farming industry encourages farmers to increase their output while minimizing negative environmental consequences. The agriculture industry completely and publicly incorporated AI in its operations to change the overall result. Technology is transforming the way we grow our food, resulting in a 20% reduction in agricultural field emissions.