The AgroScout platform collects data to create powerful analytics for actionable insights in crop management. AgroScout monitors the crop from emergence stand count, through canopy coverage estimates, and plant biomass, throughout the season. AgroScout announced today that it has completed a Series A investment round of $7.5 million to expedite the development of its AI cloud platform for remote agronomy, and to increase its accessibility to the 500 million mostly unserved farms worldwide. The platform allows all growers - from the biggest to the smallest - to efficiently comply with the rising demand for sustainable crop protection and carbon accountability. The investment round was led by Kibbutz Yotvata.
To do that, the nonprofit is implementing new technology like better video review platforms, better cameras on boats, and increased artificial intelligence, which CEO Mark Hager said is the most exciting. New England Marine Monitoring, in partnership with the Gulf of Maine Research Institute and Vesper, is developing artificial intelligence for fishermen. The goal is to make commercial fishing both economically and ecologically better. Typically, there are human observers on a boat to be sure the fishermen are following federal guidelines, but this technology could change that. "The idea is to ultimately shift from having at-sea human observers," Blaine Grimes of the Gulf of Maine Research Institute said.
Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing.
Over 30 percent of that is already destroyed in the production process. In the Resource-efficient Intelligent Foodchain ("REIF") projects working to combat this food waste. In this undertaking, artificial intelligence (AI) can be a valuable asset. Cheese, bread, meat, and other food products can be efficiently produced using data-based algorithms. Germany has committed to the United Nations goal to reduce food waste by half by the year 2030.
Today, advances in agronomy combined with smart agriculture technology have improved crop yields and sustainability. Enhancements in animal husbandry technology, improved breeding, nutrition and disease management help ensure optimal growth and performance of livestock. In spite of these innovations, the agricultural industry still faces significant challenges in producing enough food and getting it safely to market. These include changing weather patterns, water shortages, urbanization, population growth, complex environmental regulations, and dwindling available agricultural land, among others. In addition, food waste is a significant drain on the global food supply.
Agriculture is a both major industry and the foundation of the economy. Artificial Intelligence (AI) techniques are widely used to solve a variety of problems and to optimize the production and operation processes in the fields of agriculture, food, and bio-system engineering. The use of artificial intelligence in the agriculture supply chain is becoming more and more important while involving Artificial Intelligence ML algorithms. The main four clusters are preproduction, production, processing, and distribution. In fact, in the preproduction, ML technologies are used, especially for the predictions of given features.
Artificial intelligence is not the scary, half-human half-robot movie character some might think it is, says Precision AI founder Daniel McCann. "AI is just a data processing system that points out patterns in huge volumes of data and that's it," he said during a presentation at the virtual Canada's Farm Show. For agriculture, it represents the future. McCann said he believes within the next 15 years common pieces of farm equipment, such as the broadcast sprayer, will become like a BlackBerry -- still around, not too common and not too efficient. But he said AI in agriculture is at an interim step along the way to that.
Machine Learning (ML) is about statistical patterns in the artificial data sets, while artificial intelligence (AI) is about causal patterns in the real world data sets. The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased data volumes, advanced algorithms, and improvements in computing power and storage. Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Artificial intelligence is important because it automates repetitive learning and discovery through data. Instead of automating manual tasks, AI performs frequent, high-volume, computerized tasks.
The new institute is one of 11 launched by the National Science Foundation and among two funded by the U.S. Department of Agriculture-National Institute of Food and Agriculture in 2021. It's called the AgAID Institute, which is short for USDA-NIFA Institute for Agricultural AI for Transforming Workforce and Decision Support. While traditional AI development involves scientists making tools and delivering them to end-users, the AgAID Institute will involve the people who will use the AI solutions--from farmers and workers to policy makers--in their development, said Ananth Kalyanaraman, a WSU computer science professor and the lead principal investigator for the Institute. "People are very much part of the agricultural ecosystem. Humans manipulate crops on a daily basis and make complex decisions, such as how to allocate water or mitigate the effects of an incoming storm," said Kalyanaraman, who also holds the Boeing Chair in WSU's School of Electrical Engineering and Computer Science.
Brooklyn, New York, July 30, 2021 (GLOBE NEWSWIRE) -- According to a new market research report published by Global Market Estimates, the Global Artificial Intelligence in Livestock Farming Market is projected to grow at a CAGR value of around 25.6% during the forecast period [2021 to 2026]. Rapidly rising population clubbed with increasing poultry and dairy product consumption, and rising concern associated with livestock health and disease spread will positively affect the growth of the market. Browse 151 Market Data Tables and 111 Figures spread through 181 Pages and in-depth TOC on "Global Artificial Intelligence in Livestock Farming Market - Forecast to 2026"