Agriculture


Ways Fourth Industrial Revolution can Help the Planet

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Smart grids, connected to each other via the cloud, and utilising the IoT, big data analytics and machine learning, can significantly increase the energy efficiency of the existing grid. The result is advanced production optimised for resource consumption and cost including energy, raw materials and water, whilst also enabling connection with customer devices to optimise lifespan performance. Wider 4IR technologies incorporated by the IIoT platform include Virtual Reality product simulators to optimise smart product design, sensor-driven computing, industrial big data analytics, energy efficient robotics, and intelligent machine applications. IoT, sensors, AI and cloud-enabled'precision agriculture' can use on-farm sensors and connected machinery to access real-time data for farmer smart devices that can optimise how much water, energy, fertiliser and feed to use, increasing productivity whilst reducing energy use and product waste.


Driverless tractors and drones grow crops in Shropshire

Daily Mail

The project, called hands Free Hectare, began with autonomous tractors drilling channels to precise depths for the barley seeds to be planted. The project, called hands Free Hectare, began with autonomous tractors drilling channels to precise depths for the barley seeds to be planted. Pictured is the team's partly-autonomous tractor spraying chemicals on the crops An automated combine harvester then harvested the field of spring barley. Pictured is the team's partly-autonomous tractor drilling into the soil before planting The team are using small-scale machinery and adapting it in the university's engineering labs to make it work autonomously - seen here using drone footage from above


Can artificial intelligence and IoT feed the planet's growing population?

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The innovation behind these transformations is machine learning, a kind of algorithm that ingests and analyzes tons of data to find common patterns, and turn those patterns into predictions and actions. The practice, known as "precision farming," uses real-time and historical data along with machine learning algorithms to take specific actions for smaller areas and time increments instead of performing the same thing for a very large area in a routine-based manner. Deep learning and computer vision algorithms analyze the collected data to learn and report when something important is happening. These tasks can be as easy as controlling irrigation on different parts of the field based on humidity data obtained from sensors, or notifying distribution partners based on the amount and time of yield expected.


The Insight Economy Trajectory Magazine

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In 2015, Chinese technology giant Baidu identified 24 potential ghost cities by tracking the GPS location of cellphones; cities that ingested a large number of cellphone users during the week and expelled them on weekends, it determined, were likely ghost cities. The workflow is simple and takes place entirely in the cloud: Users tell AaaS providers which geospatial questions their business needs to answer, at which point providers create new algorithms or leverage existing ones to automatically process imagery acquired from their own or partners' satellites. Using decades of historic data from NASA, the National Oceanic and Atmospheric Administration, and the U.S. Department of Agriculture (USDA), TellusLabs developed machine learning algorithms that process terabytes of data every day in order to predict agricultural yields. Then there are the farmers themselves: Two of TellusLabs' competitors, Astro Digital and Vinsight, provide field-specific insights to growers, who can synthesize data about crop production, health, and conditions to make better business decisions.


Can artificial intelligence and IoT feed the planet's growing population?

#artificialintelligence

The innovation behind these transformations is machine learning, a kind of algorithm that ingests and analyzes tons of data to find common patterns, and turn those patterns into predictions and actions. The practice, known as "precision farming," uses real-time and historical data along with machine learning algorithms to take specific actions for smaller areas and time increments instead of performing the same thing for a very large area in a routine-based manner. Deep learning and computer vision algorithms analyze the collected data to learn and report when something important is happening. These tasks can be as easy as controlling irrigation on different parts of the field based on humidity data obtained from sensors, or notifying distribution partners based on the amount and time of yield expected.


Latest John Deere Acquisition Could Lead to Machine Learning in Agriculture Equipment

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John Deere has officially acquired Blue River Technology, the machine learning technology company that puts a large focus on agriculture. Blue River develops equipment that can be used by agricultural specialists and producers to optimize their daily work. "As a leader in precision agriculture, John Deere recognizes the importance of technology to our customers. "Blue River is advancing precision agriculture by moving farm management decisions from the field level to the plant level," said Jorge Heraud, co-founder and CEO of Blue River Technology.


Global Bigdata Conference

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Deere & Company (NYSE: DE) has signed a definitive agreement to acquire Blue River Technology, which is based in Sunnyvale, California and is a leader in applying machine learning to agriculture. As an innovation leader, Blue River Technology has successfully applied machine learning to agricultural spraying equipment and Deere is confident that similar technology can be used in the future on a wider range of products, May said. Blue River has designed and integrated computer vision and machine learning technology that will enable growers to reduce the use of herbicides by spraying only where weeds are present, optimising the use of inputs in farming – a key objective of precision agriculture. "Blue River is advancing precision agriculture by moving farm management decisions from the field level to the plant level," said Jorge Heraud, co-founder and CEO of Blue River Technology.


How John Deere's New AI Lab Is Designing Farm Equipment For A More Sustainable Future

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The company spent $305 million to acquire Blue River Technology, a startup with computer vision and machine learning technology that can identify weeds–making it possible to spray herbicides only where they're needed. "What Blue River Technology allows us to do is move to the plant level, and start managing at that plant level," says Alex Purdy, director of John Deere Labs. Now, using computer vision tech to identify and spray only weeds, farmers can switch to other herbicides–including, potentially, organic herbicides that the weeds haven't evolved to resist (and that might otherwise kill the cotton, if they were sprayed everywhere). Computer vision and machine learning technology can also be used in every other step of farming: tilling soil, planting seeds in the optimal locations, spraying fertilizer or nutrients, and harvesting.


John Deere advancing machine learning in agriculture sector

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Blue River Technology The new technology will reduce the need for herbicides by almost 95 percent because computer vision and artificial intelligence will allow the machines to identify, and make management decisions about every single plant in the field, only applying an herbicide to those plants that need treating. "We are using computer vision, robotics, and machine learning to help smart machines detect, identify, and make management decisions about every single plant in the field." Using computer vision and artificial intelligence, smart machines can detect, identify, and make management decisions about every single plant in the field. Using computer vision and artificial intelligence, smart machines can detect, identify, and make management decisions about every single plant in the field.


9 AgTech Startups Using AI to Grow Smarter - Nanalyze

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One of the big reasons we're rooting for the future is that the world's biggest tech fund, the SoftBank Vision Fund, planted $200 million in the biggest agtech funding round ever for San Francisco-based Plenty. Founded in 2015, Germany's PEAT has developed a free app called Plantix that uses machine learning and computer vision, technologies within the broader AI umbrella, to identify the problem with a plant from just a picture. We've seen the benefits of AI and computer vision on agriculture with another company called Blue River Technology, which has developed a system that can actually "see" weeds so that farmers can dramatically reduce the use of pesticides. This year's $200 million mega-round to Plenty and $305 million exit by Blue River show that the sector is drawing serious attention.