Global Connected Services and Big Data Analytics in Farming Market Outlook 2019-2024 : John …

The global market report for the “Connected Services and Big Data Analytics in Farming Market” is a widespread report which provides both the …

The global market report for the “Connected Services and Big Data Analytics in Farming Market” is a widespread report which provides both the analysis on the highly important areas and the company comprehension which is guided by the industry experts. In the initial part of the report, the recent launches of the Connected Services and Big Data Analytics in Farming market are given along with the in-depth information about the past statistics to implement to the present situation of the market. The market players John Deere, KUBOTA, Accenture, IBM, Intel, Videophone, Tech Mahindra, Mahindra & Mahindra, Trimble, Cisco, AT&T, CNH Industrial are also covered in the market report.

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The Connected Services and Big Data Analytics in Farming market report also gives the geographical segmentation of the concerned market in detail. The progress plan of the concerned market is also given with keeping the primary segments, as far as the geographical segmentation is concerned, well explained.

It demonstrates various segments Type I, Type II and sub-segments Yield Monitoring, Soil Monitoring, Scouting, Others of the global Connected Services and Big Data Analytics in Farming market. The information is taken from different reliable resources on the web and the sector wise segment development is also estimated in Connected Services and Big Data Analytics in Farming market report. Other than that, the overall market size along with the most important factor, the improvement prices of the parts of the Connected Services and Big Data Analytics in Farming market are explained in detail.

Read Detailed Index of full Research Study

Some of the key players in the concerned market are also given in the market report. The particulars about these companies in the market which are directed towards the growth of the market particularly in the developing economies are very well explained in the Connected Services and Big Data Analytics in Farming market report.

There are 15 Chapters to display the Global Connected Services and Big Data Analytics in Farming market

Chapter 1, Definition, Specifications and Classification of Connected Services and Big Data Analytics in Farming , Applications of Connected Services and Big Data Analytics in Farming , Market Segment by Regions;

Chapter 2, Manufacturing Cost Structure, Raw Material and Suppliers, Manufacturing Process, Industry Chain Structure;

Chapter 3, Technical Data and Manufacturing Plants Analysis of Connected Services and Big Data Analytics in Farming , Capacity and Commercial Production Date, Manufacturing Plants Distribution, R&D Status and Technology Source, Raw Materials Sources Analysis;

Chapter 4, Overall Market Analysis, Capacity Analysis (Company Segment), Sales Analysis (Company Segment), Sales Price Analysis (Company Segment);

Chapter 5 and 6, Regional Market Analysis that includes United States, China, Europe, Japan, Korea & Taiwan, Connected Services and Big Data Analytics in Farming Segment Market Analysis (by Type);

Chapter 7 and 8, The Connected Services and Big Data Analytics in Farming Segment Market Analysis (by Application) Major Manufacturers Analysis of Connected Services and Big Data Analytics in Farming ;

Chapter 9, Market Trend Analysis, Regional Market Trend, Market Trend by Product Type Type I, Type II, Market Trend by Application Yield Monitoring, Soil Monitoring, Scouting, Others;

Chapter 10, Regional Marketing Type Analysis, International Trade Type Analysis, Supply Chain Analysis;

Chapter 11, The Consumers Analysis of Global Connected Services and Big Data Analytics in Farming ;

Chapter 12, Connected Services and Big Data Analytics in Farming Research Findings and Conclusion, Appendix, methodology and data source;

Chapter 13, 14 and 15, Connected Services and Big Data Analytics in Farming sales channel, distributors, traders, dealers, Research Findings and Conclusion, appendix and data source.

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Artificial Intelligence Is Helping Evaluate 1.1 Million Security Clearance Holders

While artificial intelligence is key to the future of background investigations, it will always be up to humans to make clearance decisions, an official …

The security clearance process is broken—a fact widely accepted by stakeholders in the public and private sector, legislative and executive branches of government, Democrats and Republicans. As federal leaders work on the largest process overhaul in half a century, artificial intelligence will play a key role.

In February, officials unveiled plans for Trusted Workforce 2.0, a framework that would shift suitability and security determinations from a one-time investigation followed by reassessments every five to 10 years, to an ongoing process that uses technology and private sector partners and data.

The first step in moving away from the old process is getting rid of all the paper, according to Terry Carpenter, the program executive officer for the National Background Investigation Service, the office overseeing the technical overhaul of the investigations process.

“The days of you filling out some form—online or in paper—submitting this form; having people go through that form; analyze your responses; decide which investigator to send out there to meet with your parents, your friends, your neighbors, who may all be in different states; to write up reports and assemble a package that grows as we do the investigation and come back to somebody to adjudicate the recommendation to say, ‘should this person get a clearance or not based on policy?’ We can’t do that anymore—that’s paper,” Carpenter said Thursday during the Government Analytics Breakfast Forum hosted by Johns Hopkins University and REI Systems.

Before instituting the Trusted Workforce 2.0 framework, adjudicators would have to physically travel to interview people on every topic covered by the clearance investigation. Under the new guidance, investigators have the option to use other means to speed the process.

In October, Carpenter’s team rolled out a tool to digitize the front-end of the security clearance process: filling out paper forms like the SF-86.

The NBIS team developed a digital form that operates similarly to modern tax preparation software. Users are guided through a set of questions and document requests that ensure the data is entered in a structured format.

Another tool currently in development uses artificial intelligence to pull investigatees’ data from multiple sources, including public information, private data sources like credit reports and government data, either through interagency partners or data collected by investigators. The AI tool will gather and sort that data and present it to adjudicators in a structured, interactive format.

“We have a complete list of how the algorithm got to the recommendation. We can click on any piece of data in that decision-making chain and see it. And that gets packaged up as part of the supporting evidence for why decisions were made,” Carpenter said.

With digitized data sources and AI helping pull and sort the information, the investigative process can move toward a “continuous evaluation” model, in which clearance holders are regularly reassessed based on new information, rather than on an arbitrary, decade-long cycle.

“We already have 1.1 million names in as part of the initiative and it just goes through the data sources on some reoccurring schedule set by the business rules and if an anomaly comes up, then we look at the anomaly and decide what to do with it,” Carpenter said. “Maybe I got a speeding ticket last night and it popped up on the data source,” he offered as a hypothetical.

Carpenter said the team hopes soon to be able to pilot the system end-to-end with the lowest level suitability determinations. If that pilot succeeds, the team can add more data streams and capabilities in order to handle more sensitive assessments.

All this will be possible because the team looked at the problem as a whole, rather than just finding cool tools to apply to parts of the problem, Carpenter said.

“We moved the NBIS architecture to an enterprise data broker concept and we moved the data sources to one place,” he explained. “This gave us the ability to focus on control of that data. I don’t have to own it all—it doesn’t have to sit on my servers. … But we need to know that we have the connection, we have the ability to pull it when we need it, we know what the data is—what it represents—and we know the policies and agreements—the laws—that surround the use of that data.”

“We couldn’t rush to the quick answer,” he added. “We had to do some of these critical, I call them ‘hygiene steps.’”

No matter what, it will be up to humans to make the final decisions about suitability and trust, Carpenter said.

“No decision will be made by a machine. We will augment decision- making,” he said. “Instead of them spending weeks and months trying to find needles in a haystack, we’re using artificial intelligence and machine learning to present those needles, in a neat form with all the relevant information to how we found it for the human to make that final determination.”

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US Startup Raises $14.1 Mln for Blockchain-Based Payments Network for Retailers

New York-based blockchain startup Flexa has raised $14.1 million to develop a payments network for retailers. The development was announced in a …

New York-based blockchain startup Flexa has raised $14.1 million to develop a payments network for retailers. The development was announced in a press release published on April 11.

Per the release, Flexa has raised $14.1 million in funding from such participants as early stage token fund 1kx, investment firms Access Ventures and Nima Capital, and hedge fund Pantera Capital, which recently revealed that it was close to completing funding for its third venture fund, already raising $160 million.

The company intends to create a payment network for retailers that would reduce costs, overhead, and fraudulence by means of blockchain-based settlements. Flexa is also planning to release a mobile application through which customers could conduct operations with cryptocurrencies they already own.

Tyler Spalding, Co-Founder and CEO of Flexa, said that “the anti-fraud and cost benefits of global cryptocurrency payments are enormous, but there are many barriers to mainstream adoption for merchants and consumers alike. Flexa’s going to change that.”

Blockchain technology has become widely applied in the retail industry. Earlier today, United States food and drug chain Albertsons Companies announced it will use IBM’s Food Trust blockchain platform to track the supply chain for romaine lettuce, but aims to branch out into other products.

Last month, the U.S. Pork Board partnered with blockchain startup to test out a blockchain platform for pork supply chains. The collaboration will ostensibly enable the Board to use a blockchain-based ecosystem to monitor and evaluate sustainability practices, food safety standards, livestock health, and environmental protections.

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With ‘Problems Out There Waiting for Us,’ Public Health Practitioners Turn to AI for Help

PGP enlisted a group of young women from Syracuse to help them design the chatbot, which uses IBM’s Watson technology—specifically its natural …

“When I was 15, I didn’t know much about birth control, much less where to get it,” Yanira says.

Teen pregnancy is a real problem in Syracuse. For girls between ages 15 and 17, the rate is 21.8 per 1,000, compared to 18.8 in the U.S. as a whole. And for black and Hispanic teenagers, the rates are much higher: 65.6 and 53.3 per 1,000, respectively. A study found that 80 percent of those pregnancies were unplanned.

That same research, conducted by Public Good Projects (PGP), the public health nonprofit behind the chatbot, found that a major cause behind the high pregnancy rates was these young women’s limited understanding of sex.

“What does a condom look like? They couldn’t tell you,” says Joe Smyser, the CEO of PGP.

Finding useful, accurate information can be hard for teens. Random internet searches run the risk of finding incorrect information, while reputable sources can be written in dry language that doesn’t reflect the lived experience of teenage girls. Going to parents isn’t always an option, either.

But Layla is available 24/7, and no question is off-limits. As the opening prompt says, Layla wants to be considered “your new best friend with all the deets about those uncomfortable topics like birth control, sex, and STDs.”

PGP enlisted a group of young women from Syracuse to help them design the chatbot, which uses IBM’s Watson technology—specifically its natural language processing abilities—to make the chatbot nimbler at answering questions phrased in a variety of ways. Although cutting edge AI powers the site, everything from the resources it recommends, to the language it uses, to its aesthetic look and feel were designed by local teenagers who need it most.

“We didn’t want this to come off like a healthcare department,” says Tiffany Lloyd, Layla’s community manager. “It was created by brown girls, for brown girls.”


Jason Wagner, 16, and Deziah Thomas, 15, are two youth influencers that helped to promote Layla, a chatbot designed to help young people of color get reliable information about sexual health, including where to find additional resources and services. Image credit: Tiffany Lloyd, Layla

AI for the Globe

The field of public health has the ambitious goal of preventing the spread of diseases and extending life across human populations that span everything from small communities to entire continents. It’s a daunting challenge with new problems constantly cropping up, often with insufficient resources to tackle them. Those challenges have led public health workers to look to AI to find novel ways to solve global health issues like tuberculosis and teenage pregnancy.

One of the earliest and best-known attempts to use big data for public health—done before AI research really took off—was predicting the spread of flu from Google searches. While Google has ended its internally run research, the company has added new AI capabilities and still passes data along so that places like Boston Children’s Hospital can continue the work. By going through electronic health records, flu-related Google searches, and historical flu activity in a given location, the AI-powered tool has demonstrated the ability to spot flu outbreaks two weeks before the Centers for Disease Control (CDC) can.

This approach is not entirely new. Disease surveillance systems now routinely track people researching symptoms in Google searches, as well as more traditional, expensive-to-gather clinical data. But more sophisticated data crunching algorithms exist for marketers: Layla developer PGP, for example, used a tool originally designed to monitor “brand health,” a mix of various metrics companies look at to see how their company is perceived by the public, and repurposed it for tracking opioid use and mental health trends.

“No one in academia understands Twitter the way marketers do,” Smyser says.

With data from a wide range of traditional and social media sources, including closed captioning from local television, newspapers, Facebook, Twitter, Instagram, and YouTube, PGP is generating new insights into opioid use. But it’s only possible with natural language processing, a subset of AI that parses written or spoken word—like the Layla chatbot does with IBM’s Watson. In this instance, the AI learns to find stories and tweets about opioids, even if slang is used, without having to be taught every different way the drug use might be referenced.

Right now, the system picks up about 55,000 references to opioids a day. These might include everything from a tweet from President Trump boasting of a fentanyl bust to Facebook posts of dealers hawking a product. With such a large stream of data, experts are able to conduct a lot of previously out-of-reach analysis. Smyser is sharing what PGP is learning with presentations to agents from the Drug Enforcement Agency (DEA) and community groups, but there are limits to how much small, community-based nonprofits like his can help.

“There is no red phone for me to pick up and tell CDC they need to be aware of this,” Smyser says.

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Comparing of 2U Inc. (TWOU) and Synchronoss Technologies Inc. (NASDAQ:SNCR)

This is therefore a comparing of the analyst recommendations, profitability, risk, dividends, earnings and valuation, institutional ownership in 2U Inc.

This is therefore a comparing of the analyst recommendations, profitability, risk, dividends, earnings and valuation, institutional ownership in 2U Inc. (NASDAQ:TWOU) and Synchronoss Technologies Inc. (NASDAQ:SNCR). The two are both Application Software companies that compete with one another.

Earnings and Valuation

Gross Revenue Price/Sales Ratio Net Income Earnings Per Share Price/Earnings Ratio
2U Inc. 411.77M 9.38 38.33M -0.71 0.00
Synchronoss Technologies Inc. 325.84M 0.69 262.04M -5.14 0.00

Table 1 showcases the gross revenue, earnings per share (EPS) and valuation of 2U Inc. and Synchronoss Technologies Inc.


Table 2 demonstrates the net margins, return on equity and return on assets of 2U Inc. and Synchronoss Technologies Inc.

Net Margins Return on Equity Return on Assets
2U Inc. -9.31% -6.2% -5.2%
Synchronoss Technologies Inc. -80.42% -35.2% -13.9%

Volatility & Risk

A 0.76 beta means 2U Inc.’s volatility is 24.00% less than Standard & Poor’s 500’s volatility. Synchronoss Technologies Inc.’s 30.00% less volatile than Standard & Poor’s 500 volatility due to the company’s 0.7 beta.


7.6 and 7.6 are the respective Current Ratio and a Quick Ratio of 2U Inc. Its rival Synchronoss Technologies Inc.’s Current and Quick Ratios are 1 and 1 respectively. 2U Inc. has a better chance of clearing its pay short and long-term debts than Synchronoss Technologies Inc.

Analyst Recommendations

The next table highlights the given recommendations and ratings for 2U Inc. and Synchronoss Technologies Inc.

Sell Ratings Hold Ratings Buy Ratings Rating Score
2U Inc. 0 0 8 3.00
Synchronoss Technologies Inc. 0 1 0 2.00

2U Inc. has an average target price of $87.25, and a 31.40% upside potential. Synchronoss Technologies Inc. on the other hand boasts of a $7 consensus target price and a 24.33% potential upside. Based on the analysts opinion we can conclude, 2U Inc. is looking more favorable than Synchronoss Technologies Inc.

Institutional & Insider Ownership

The shares of both 2U Inc. and Synchronoss Technologies Inc. are owned by institutional investors at 0% and 46.2% respectively. Insiders held roughly 1.3% of 2U Inc.’s shares. Comparatively, 4% are Synchronoss Technologies Inc.’s share held by insiders.


Here are the Weekly, Monthly, Quarterly, Half Yearly, Yearly and YTD Performance of both pretenders.

Performance (W) Performance (M) Performance (Q) Performance (HY) Performance (Y) Performance (YTD)
2U Inc. -1.95% 22.8% 22.47% -20.03% -17.62% 40.41%
Synchronoss Technologies Inc. 0.88% 11.3% 32.12% 31.47% -14.1% 29.97%

For the past year 2U Inc. was more bullish than Synchronoss Technologies Inc.


2U Inc. beats Synchronoss Technologies Inc. on 12 of the 12 factors.

2U, Inc. provides cloud-based software-as-a-service (SaaS) solutions for nonprofit colleges and universities to deliver education to students. Its cloud-based SaaS platform solutions include online campus, an online learning platform that enables its clients to offer educational content together with instructor-led classes in a live, intimate, and engaging setting through proprietary Web-based and mobile applications. The companyÂ’s integrated back-end applications launch, operate, and support clients’ programs, as well as provide clients with real-time data and analytical insight related to student performance and engagement, student satisfaction, and enrollment. It also offers a suite of technology-enabled services, including content development and student acquisition, admissions application advisory, student and faculty support, student field placement, accessibility, immersion support, faculty recruitment, and state authorization services. The company was formerly known as 2Tor Inc. and changed its name to 2U, Inc. in October 2012. 2U, Inc. was founded in 2008 and is headquartered in Lanham, Maryland.

Synchronoss Technologies, Inc. provides cloud solutions and software-based activation for connected devices worldwide. The companyÂ’s products and services include cloud-based sync, backup, storage and content engagement capabilities, broadband connectivity solutions, analytics, white label messaging, and identity/access management that enable communications service providers, cable operators/multi-services operators, original equipment manufacturers with embedded connectivity, and multi-channel retailers, as well as other customers to accelerate and monetize value-add services for secure and broadband networks and connected devices. It also provides Synchronoss Enterprise solutions, such as secure mobility management, data and analytics, and identity and access management solutions for the financial, telecommunications, healthcare, life sciences, and government sectors; and Synchronoss Personal Cloud platform that delivers an operator-branded experience for subscribers to backup, restore, synchronize, and share their personal content across smartphones, tablets, computers, and other connected devices. In addition, the company offers software as a service for the organizations to securely manage, control, track, search, exchange, and collaborate on sensitive information inside and outside the firewall. Its products and platforms are designed to enable multiple converged communication services to manage across a range of distribution channels, such as e-commerce, m-commerce, telesales, customer stores, indirect, and other retail outlets. The company markets and sells its services through direct sales force and strategic partners. Synchronoss Technologies, Inc. was founded in 2000 and is headquartered in Bridgewater, New Jersey.

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