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A New Market Study, Titled Artificial Intelligence (AI) in AgricultureMarket Upcoming Trends, Growth Drivers and Challenges has been featured on fusionmarketresearch.

This report provides in-depth study ofArtificial Intelligence (AI) in Agriculture Market using SWOT analysis i.e. strength, weakness, opportunity and threat to Organization.Artificial Intelligence (AI) in Agriculture Marketreport also provides an in-depth survey of major market players which is based on the various objectives of an organization such as profiling, product outline, production quantity, raw material required, and production. The financial health of the organization.

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This report studies the Artificial Intelligence (AI) in Agriculture market, from angles of players, regions, product types and end industries, to analyze the status and the future.Artificial Intelligence (AI) in Agriculture Market size is being driven by the growing adoption of the robots in agriculture. Increasing consumption and rising requirement of better yield of crops are estimated to be one of the major factors that is fueling the demand of robots in agriculture. Increasing consumption motivates farmers to scale up farming operations and give rise to the requirement of automating farming operations. As the farmers are shifting more towards automation, drones and robots have become integral part of agriculture farms and are enhancing yield and improving the product quality. Since AI is the backbone of robotics, increasing adoption of robots in agriculture is estimated to drive the AI in agriculture market growth.

This report contains market size and forecasts of Artificial Intelligence (AI) in Agriculture in Global, including the following market information:Global Artificial Intelligence (AI) in Agriculture Market Revenue, 2017-2022, 2023-2028, ($ millions)Global top five companies in 2021 (%)The global Artificial Intelligence (AI) in Agriculture market was valued at 595.5 million in 2021 and is projected to reach US$ 1652.1 million by 2028, at a CAGR of 15.7% during the forecast period.

The U.S. Market is Estimated at $ Million in 2021, While China is Forecast to Reach $ Million by 2028.Machine Learning Segment to Reach $ Million by 2028, with a % CAGR in next six years.The global key manufacturers of Artificial Intelligence (AI) in Agriculture include IBM, Intel, Microsoft, SAP, Agribotix, The Climate Corporation, Mavrx, aWhere and Precision Hawk, etc. In 2021, the global top five players have a share approximately % in terms of revenue.

Fusion Market Research (FMR)has surveyed the ARTIFICIAL INTELLIGENCE (AI) IN AGRICULTURE manufacturers, suppliers, distributors and industry experts on this industry, involving the sales, revenue, demand, price change, product type, recent development and plan, industry trends, drivers, challenges, obstacles, and potential risks.

Competitor Analysis

The report also provides analysis of leading market participants including:Key companies Artificial Intelligence (AI) in Agriculture revenues in global market, 2017-2022 (estimated), ($ millions)Key companies Artificial Intelligence (AI) in Agriculture revenues share in global market, 2021 (%)

Further, the report presents profiles of competitors in the market, key players include:

IBMIntelMicrosoftSAPAgribotixThe Climate CorporationMavrxaWherePrecision HawkGranularProspera TechnologiesSpensa TechnologiesRessonVision RoboticsHarvest Croo RoboticsCropXJohn DeereGamayaCainthus

Total Market by Segment:

Global Artificial Intelligence (AI) in Agriculture Market, by Type, 2017-2022, 2023-2028 ($ millions)Global Artificial Intelligence (AI) in Agriculture Market Segment Percentages, by Type, 2021 (%)Machine LearningComputer VisionPredictive Analytics

Global Artificial Intelligence (AI) in Agriculture Market, by Application, 2017-2022, 2023-2028 ($ millions)Global Artificial Intelligence (AI) in Agriculture Market Segment Percentages, by Application, 2021 (%)Precision FarmingLivestock MonitoringDrone AnalyticsAgriculture RobotsOthers

Global Artificial Intelligence (AI) in Agriculture Market, By Region and Country, 2017-2022, 2023-2028 ($ Millions)Global Artificial Intelligence (AI) in Agriculture Market Segment Percentages, By Region and Country, 2021 (%)North AmericaUSCanadaMexicoEuropeGermanyFranceU.K.ItalyRussiaNordic CountriesBeneluxRest of EuropeAsiaChinaJapanSouth KoreaSoutheast AsiaIndiaRest of AsiaSouth AmericaBrazilArgentinaRest of South AmericaMiddle East & AfricaTurkeyIsraelSaudi ArabiaUAERest of Middle East & Africa

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Table Of Content:

1 Introduction to Research & Analysis Reports

2 Global Artificial Intelligence (AI) in Agriculture Overall Market Size

3 Company Landscape

4 Sights by Product

5 Sights By Application

6 Sights by Region

7 Manufacturers & Brands Profiles

8 Global Artificial Intelligence (AI) in Agriculture Production Capacity, Analysis

9 Key Market Trends, Opportunity, Drivers and Restraints

10 Artificial Intelligence (AI) in Agriculture Supply Chain Analysis

11 Conclusion

12 Appendix

Tags: Artificial Intelligence (AI) in Agriculture Market Growth, Artificial Intelligence (AI) in Agriculture Market, Artificial Intelligence (AI) in Agriculture Market Size, Artificial Intelligence (AI) in Agriculture, Artificial Intelligence (AI) in Agriculture INDUSTRY, Artificial Intelligence (AI) in Agriculture Market Trends, Artificial Intelligence (AI) in Agriculture Market Share, artificial intelligence (ai) in agriculture market forecast, artificial intelligence (ai) in agriculture market outlook, artificial intelligence (ai) in agriculture market analysis, Artificial Intelligence (AI) in Agriculture Market Segmentation, Artificial Intelligence (AI) in Agriculture Market Prospectus

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Artificial Intelligence (AI) in Agriculture Market Expected to Grow at CAGR 5.51% and Forecast to 2028 - Inter Press Service

With the intrusion of artificial intelligence and blockchain technology, the world has reached new heights recently

Many things have changed since the beginning of the 21st century. At the core of all these transformations, a simple concept called technology prevails. Yes, for the past two decades, technology has affected the way we live, work, learn, study, communicate, transport, and even think. As a result of modern trends intrusion, computers are becoming faster, more portable, and higher-powered than ever before. Although the tech evolution has both positive and negative impacts, the good side of the transformation is heavily admired by people.

It all started in 2000 when the dotcom bubble burst and gave birth to disruptive trends like the internet and the smartphone. Even though the stocks of many companies tumbled for a while, it paved the way for tech giants like Amazon to get a stronghold on the market. Many more people are online today than they were at the start of the millennium. The broadband expansion has also introduced artificial intelligence into mainstream adoption. One could argue that technology has continued to improve our lives, keeping us more connected to big data and with each other. But the amazing transformation has also paved the way for increasing complexities.

Today, everything starting from transport vehicles to medical devices, financial transactions, and electricity systems are relying on computer software. While digitization has made things easier for humankind, it has also made technology harder to control. When human-to-human contact is minimized with disruptive trends, it provides a space for machines to entertain bias and dominance.

Although the term artificial intelligence came into existence in the 1950s, it entered mainstream acceptance only in the 2000s. The core motto of developing artificial intelligence technology is to make machines imitate human behavior like thinking and taking decisions on their own. However, that kind of intelligence is yet to be achieved. Meanwhile, humans have come a long way from where they started the 21st on accords with technology. With amazing branches like data science, machine learning, robotics, and business intelligence rocking the digital sphere, modern artificial intelligence can understand data and make real-time decisions.

Initially, AI was intended to defeat more manageable issues like language recognition, playing a game, and picture recovery. With the innovative headways, artificial intelligence is getting progressively sophisticated at doing what people do, yet more effectively, quickly, and at a lower cost in tackling complex issues. Further, the outbreak of the Covid-19 pandemic took artificial intelligence to the next level. Even industries that were extremely slow in adopting technology embraced artificial intelligence at a quicker pace. Today, AI is playing an essential role in supporting and aiding decision-making in every walk of life.

If we step out of the AI ecosystem and enter the blockchain bubble, far more emerging trends are flying around like never before. From being a Bitcoin platform as conceived by Satoshi Nakamoto in 2009, blockchain has come a long way to emerge as a futuristic aspect in the digital sphere. It has reached far beyond the originally planned cryptocurrency realm. Furthermore, blockchain is expanding its wings through new features like decentralized applications, smart contracts, metaverse, and NFTs.

In a nutshell, it looks like artificial intelligence and blockchain technology are here to stay. If you want to be a part of this trailblazing revolution, know more about the important trends in this tech space.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Artificial Intelligence and Blockchain at the Heart of Modern Tech Evolution - Analytics Insight

AI is just about everywhere these days. With good reason. It simplifies and expedites processes that would otherwise be done manually. Though once an exotic term of science fiction, its now what greets you the moment you interact with the customer service page of any major retailer.

It should be no surprise that AI has entered the cannabis sphere. Artificial intelligence has the capacity to boost production, improve efficiency, and even make the entire process more environmentally friendly.

In this article, we take an in-depth look at how it can be used to improve cannabis production.

It should be said at the forefront that AI and agriculture have roots that extend beyond cannabis. In fact, AI technology has been used recently to generate hyper-specific simulated conditions for indoor farming operations.

This has made it possible for urban farmers in say, New York City, to produce tomatoes that, at a molecular level, have all the appearances of fruit grown and cultivated in Rome. Not only does this reduce the need for costly and environmentally unfriendly produce importing, but it also allows farmers with otherwise limited resources the opportunity to supply their communities with fresh, high-quality food all year round.

AI In Cannabis

In cannabis production, AI performs many of the same roles as described above. In fact, there are many benefits AI can have production, protection, and distribution of legal cannabis. Naturally, and as with any emerging technology, there are also issues to look out for. Below, we take a look at both.

Production

On a purely productive level, AI can be used to collect information on the best conditions for growing high-quality cannabis plants. It may take data on the ideal temperature, watering, nutrients, and light exposure levels for growing a particular strain.

It can then automatically replicate those conditions to ensure that plants receive exactly what they need at all times. Not only does this produce an incredibly consistent crop, but it also minimizes loss by ensuring conditions that are not conducive to disease.

AI can also fight back against crop sickness by automatically observing conditions likely to produce disease, and addressing issues as they arise. By catching sick plans early, cannabis farmers are able to easily reduce the spread of disease.

By guaranteeing high, healthy yields, farms become both more consistent and more profitable.

These factors may also be more conducive to growing cannabis plants that are designed to invoke very specific effects. For example, a farmer might discover that conditions X and Y produce cannabis with an extremely euphoric effect. Condition Z, on the other hand, aids in the production of cannabis that is tailored towards stress management.

Waste Reduction

Unfortunately, the rise of legal cannabis in the United States has come at a significant environmental toll. Unsuspecting cannabis manufacturers have been responsible for enormous energy consumption, as well as the production of waste materials that pose harm to their communities.

Granted, a larg part of the problem stems from the fact that 80% of cannabis is currently grown indoors. While indoor grow operations allow incredible control over how the crop is grown and nurtured, they also use enormous amounts of energy.

While the world of cannabis has, broadly speaking, responded positively to this informationwith many manufacturers making commitments towards waste-reducing policies and practices, the issue continues to persist.

AI may be able to significantly reduce cannabis waste production on a number of fronts. During growth and cultivation, AI makes very precise soil, nutrients, light, and water recommendations. This means that manufacturers can immediately cut out any unnecessary resource consumption, saving themselves money, and the planet, waste.

While the environmental conundrum of indoor farms will persist, AI can at least be there to make sure they are as sustainable as possible along the way.

Security

AI also has its role to play in making cannabis growth and cultivation sites safer and less susceptible to outside parties. Naturally, all outdoor agricultural operations have to contend with animal intrusions. While no technology can completely eliminate animal infiltration, AI can significantly improve your security efforts with accurate automated reporting.

This same technology can be used in the prevention of human intrusions. Many security technologies are prone to false alarmsespecially in outdoor locations where the wind or an otherwise non-threatening animal may be liable to set off the alarm.

These inaccurate reports are not only very tedious and annoying, but over time, they can effectively render an entire security system useless. Why bother responding to a security call if it will probably just be another squirrel?

AI makes security smart by analyzing threats and only notifying businesses about actual intrusions.

Distribution

AI continues to lend a hand a the level of distribution. For example, by taking in weather reports and other key data points, AI programs can recommend new routes, accurately predict delays, and otherwise help cannabis companies understand the best way to get their product out to the world.

This real-time distributive analysis has already been assisting the transportation and shipping industry for years. As the technology is introduced to cannabis, it will not only boost efficiency and reduce costs but also vastly eliminate the potential for supply chain issues.

A Potential Downside?

Cannabis manufacturing currently serves as a lucrative, steady income for many people all across the country. In AIs quest for maximum efficiency, some of these employees may find their jobs are no longer needed.

By managing conditions automatically, AI can effectively reduce the number of employees required to manage the crop. Alas, employment reductions are often the outcome of emerging technologies.

However, it should also be noted that some jobs are created in the bargain. While less low-skilled positions may be required as AI continues to permeate, there is the potential need for more engineers, AI specialists, and technicians.

Still A Ways Away

Of course, none of this is to say that AI will find its way into every cannabis farm in the country in the near future. Artificial technology and its corresponding hardware are expensive. For the time being, that will mean it winds up primarily in the hands of large, cash-fluid businesses.

Like any technology, some results will be good. Others might be costly to some. However, few would argue that the prospects of continued AI infiltration into the world of cannabis production dont carry with them exciting potential.

Demand for high-quality cannabis continues to surge all across the country. As more states go legal, as medicinal applications continue to be explored, it will certainly help to have a highly intuitive technology making the process a little more efficient.

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Artificial Intelligence In The Cannabis Industry: From Production To Security And Distribution. - Benzing - Benzinga

BOSTON, MA / ACCESSWIRE / February 2, 2022 / The U.S. market for robotics and artificial intelligence career openings is exploding based on early 2022 trends from job postings on Robots.Jobs, the marketplace specifically for robotics and AI companies looking for talent and for jobseekers looking for the latest industry opportunities. In the last 90 days, open positions on Robots.Jobs have increased by more than 500 percent. Newly featured job-posters include autonomous drone hardware and sensors company GreenSight and Intrinsic AI, making industrial robotics accessible and usable for businesses.

"Robotics, IoT and AI careers are in high demand across almost all industries, including industrial, healthcare, biotech, logistics, consumer and more," said Ann P. Walsh, CEO & cofounder, Robots.Jobs. "In this competitive job market, talent recruitment requires skill, targeting and focus to attract the most qualified employees. For robotics and artificial intelligence, we are just at the beginning of demand for talent."

Geographies for Job Growth

Boston, Massachusetts maintains its stronghold on the largest volume of robotics and AI job searches with 25 percent of open positions posted on the Robots.Jobs job board. This growth is in part due to the number of biotech companies actively using robotics and artificial intelligence technologies within their organizations. Austin, Texas and Denver, Colorado are also seeing fast growth, with many new innovation centers increasing recruiting efforts for engineering talent. The industry is growing in these states due to lifestyle advantages, a lower cost of doing business, and tax incentives to build a younger, more diverse workforce.

Jobs, Companies in Demand

Engineering and software developer jobs are growing as many businesses are focused on recruiting senior robotics engineers, full-stack engineers and AI developers. Of the nearly 600 jobs currently posted on Robots.Jobs, 50 percent are software engineering roles, while mechanical engineering jobs continue to pace up. Large companies like Amazon Robotics which is empowering customer delivery through robotic automation, designer and builder of consumer robots iRobot, and autonomous mobile robot company Vecna Robotics are continuing recruiting efforts, along with growing startups like automation company Flexxbotics, retail automation business HyperVend, Autonodyne which delivers software solutions that make vehicles more autonomous, and Tutor Intelligence which is focused on applying state-of-the-art robotics to business challenges.

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Visit Robots.Jobs for more information, additional resources and job postings around career opportunities in robotics and artificial intelligence.

About Robots.JobsRobots.Jobs is a resource and talent marketplace specifically for robotics and AI companies looking for talent - and for job seekers looking for the latest opportunities in robotics and AI. Visit Robots.Jobs for career opportunities and resources for both job-posters and jobseekers.

CONTACT:

Alison Boghosianaboghosian@mower.com860.922.3887

SOURCE: Robots.Jobs

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2022 Trends from Robots.Jobs Shows Dramatic Growth in Robotics and Artificial Intelligence Career Opportunities - Yahoo Finance

Neural network in artificial intelligence helps machines act like humans

Artificial Intelligence (AI) is a widespread phrase in the realm of science and technology, and its recent breakthroughs have helped AI gain more recognition for the ideas of AI and Machine Learning. AIs role allowed machines to learn from their mistakes and do tasks more effectively.

One of its breakthroughs is the Artificial Neural Network, which is inspired by the structure of the brain and assists computers and machines behave more like humans. This article will assist you in comprehending the construction and operation of AI Neural Networks.

Machine learnings main technique is artificial neural networks (ANN). These are systems based on neuron functions in the brain that will mimic how people learn. Both the input and output layers of neural networks (NN) are included, as well as hidden nodes containing units that convert input into the output so that the output layer may use the value.

These are the methods that programmers use to extract and instruct the machine to identify patterns that are multiple and diverse.

Neural networks (NN) are fed massive volumes of data in the beginning phases. In most cases, training is done by providing input and informing the network about what should be the output. Many smartphone makers, for example, have recently integrated facial recognition technology.

Each input is obtained via matching data, such as photographs of a persons face, iris, and numerous facial expressions, and all of these inputs must be learned. By providing accurate responses, it will be able to accommodate its internal data and learn how to improve its performance.

Rules must be designed in such a way that each node considers its own inputs from the previous layer when deciding what to send to the next layer. This is accomplished using a variety of ideas such as genetic algorithms, fuzzy logic, and the Bayesian gradient-based training approach. Basic object relationships rules are supplied to ANNs. When it comes to constructing the rules, the proper selection must be made.

One of the most significant technological hurdles is the time it takes to train networks, which frequently demand an acceptable level of computational power for even complex tasks. The second factor to consider is that neural networks are computer systems in which the user categorises the trained data and gets responses.

They have the ability to fine-tune the responses, but they do not have access to the specific decision-making process. This is why academics are working so hard, yet artificial neural networks have a huge impact on how people live their lives.

We have a lot of benefits from neural networks because we live in such a competitive society. They are powerful and adaptable because of their ability to learn from superior models. Furthermore, we do not need to create an algorithm to do a task.

That task does not necessitate internal systems. Because of their parallel architecture, these are particularly suited for real-time systems, as they respond quickly and have the fastest computational times.

Other fields of study, such as psychology and neurology, benefit from neural networks. Its utilised in neurology to study the brains internal structures and simulate parts of living creatures. The most fascinating feature of neural networks is the possibility of developing conscious networks in the future.

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What is the Role of Neural Network in Artificial Intelligence? - Analytics Insight