Artificial intelligence (AI) has entered its Golden Age. Machine learning requires more data to provide compelling insights on how to optimize human activity. Landsat 9 will fill the gap and feed invaluable information into the most powerful AI recommender, predictive, and classifications systems ever. Artificial intelligence (AI) has entered its Golden Age. Machine learning requires more data to provide compelling insights on how to optimize human activity.
Farming sustainably and efficiently has gone from a big tractor problem to a big data problem over the last few decades, and startup EarthOptics believes the next frontier of precision agriculture lies deep in the soil. Using high-tech imaging techniques, the company claims to map the physical and chemical composition of fields faster, better, and more cheaply than traditional techniques, and has raised $10M to scale its solution. "Most of the ways we monitor soil haven't changed in 50 years," EarthOptics founder and CEO Lars Dyrud told TechCrunch. "There's been a tremendous amount of progress around precision data and using modern data methods in agriculture – but a lot of that has focused on the plants and in-season activity -- there's been comparatively little investment in soil." While you might think it's obvious to look deeper into the stuff the plants are growing from, the simple fact is it's difficult to do.
As robots become more prevalent, demand for mechanical and computer engineers who work on autonomous systems is growing. In fact, it's projected that the number of jobs in the field will grow 9% between 2016 and 2026, leading to a shortage of qualified engineers. That's a real opportunity for new talent entering the workforce or for professionals looking to make a mid-career shift. Part of the allure is the broad applicability of automation, machine learning, and artificial intelligence technologies to a variety of sectors that heretofore haven't had much automation adoption. The chance to work on the leading edge of automation technologies and problem solve how they might be adapted to new use cases can be thrilling, particularly coming from IT and engineering fields that have now become more routinized after the profound shifts of previous decades.
When starting out to deploy artificial intelligence (AI) and machine learning (ML), executives of legacy companies often view the challenges mainly as technical problems -- particularly finding sources of internal data to analyze and choosing the right tools. What they may not appreciate is just how data-rich their legacy companies already are. From utilities and mining, transportation and shipping, to financial services and more, legacy company operations and customer interactions generate a wealth of data. Such data can be harnessed to tackle a very wide range of issues: optimizing supply chains, predicting maintenance, reducing accidents, increasing production output, improving operational efficiency, raising revenue productivity, and growing customer value. To realize these opportunities using AI, however, legacy companies worldwide typically soon discover that their biggest problem is not technology -- it's talent.
By the year 2050, Earth's population is expected to reach nearly ten billion people. With this growth comes a staggering demand for food resources, particularly drought, heat, pest and disease resistant crop varieties that give high yields in the face of climate change. Enter X, Alphabet Inc.'s so-called "moonshot factory," where innovators face the world's biggest challenges head-on and develop ground-breaking technology at a startup pace. Project Mineral, one of X's current efforts, is focused on finding an effective way to address the global food security crisis through "computational agriculture," a term coined by X to describe new technologies that will further increase understanding about the plant world. "The agriculture industry has digitized," says Project Mineral lead Elliott Grant.
Technological advances are bringing change to a great number of industries, and the agriculture industry is no exception. Farms are slowly starting to see increased adoption of practices based on technologies such as artificial intelligence, cloud computing, the Internet of Things (IoT), and robotics. The adoption of such technologies into the traditional farming practices as we know them is referred to as smart farming or farm automation. Let's have a look at what farm automation is exactly and how it can help farmers tackle a number of challenges in today's agricultural sector. Farm automation specifically focuses on applying data and information technologies for the optimization of production processes of complex farming systems as well as the quality of the food.
Agriculture and farming are some of the oldest and most important professions in the world. It plays an important role in the economic sector; especially in India, where agriculture has been the primary occupation for ages. The global population is expected to reach more than nine billion by 2050 which will require an increase in agricultural production by 70% to fulfill the demand. In this article, guest author Melanie writes about the applications of AI and Machine Learning in agriculture. The success of a business or industry is dependent on several factors, one of which is effective decisions.
Robots in agriculture are becoming increasingly used by the industry today. An example would be the multiple analytics and machine learning tools used in smart farming to help with predicting harvests. One of these tools, agriculture robots, are normally used collaboratively (known as cobots). These robots possess mechanical arms and make harvesting much easier for farmers. Compared to traditional industrial robots and machinery, cobots are designed to work alongside human employees, giving manufacturers the benefits of both robots and humans combined.
There was a time when good crop production was monitored solely by farmers' qualitative assessments of plant parts. With the growing need for increased production, more efficient methods need to be embraced. The Global Wheat Dataset was developed to help detect and eventually quantify the density and maturity of wheat crops. The dataset is composed of images compiled by different institutions worldwide. It includes different wheat phenotypes and different growing environments.
The average American consumes over 58 pounds of beef and 141 pounds of milk per year, but cattle farming is a deeply resource-intensive process with significant impacts on land use and carbon emissions – so any gains in efficiency are highly prized. Now, researchers at the University of Florida (UF) have used the university's powerful new HiPerGator AI supercomputer to help ranchers identify the highest-yield livestock. This June, UF made waves when HiPerGator AI, which delivers 17.2 Linpack petaflops, debuted on the Top500 list as the world's third most powerful publicly ranked supercomputer at an educational institution and debuted on the Green500 list as the world's second most efficient publicly ranked supercomputer. So when researchers from UF's Institute of Food and Agricultural Sciences (IFAS) set out to improve cattle yields in a smarter way, they turned to supercomputer-powered AI. "AI has rapidly emerged as a powerful approach in animal genomics and holds great promise to integrate big data from multiple biological layers, leading to accurate prediction of future traits – for example, meat yield," said Raluca Mateescu, a professor of animal science at UF. "My research group is investigating the use of AI methods to develop approaches to accurately predict the value of certain genes. Ultimately, we plan to provide more effective strategies to improve animal productivity."