Artificial Intelligence (AI) is likely to play a key role in relieving India’s agriculture sector from its stressful input conditions, catalysing a shift towards data-driven farming, said a new Nasscom-EY report on Thursday.

NEW DELHI: Artificial Intelligence (AI) is likely to play a key role in relieving India’s agriculture sector from its stressful input conditions, catalysing a shift towards data-driven farming, said a new Nasscom-EY report on Thursday. With increased government support, growing agritech providers, burgeoning start-up ecosystem, and rising AI adoption among the rural farming population, a strong transformation impetus is underway, said the report titled “Leveraging AI to maximise India’s agriculture output.”

Data consolidation (both at macro and real-time farm-level), lack of infrastructure awareness in data processing, and its availability have been some of the key challenges faced by the sector today. In addition to this, lack of awareness on agricultural inputs specific to the produce, access to quality seeds, lack of adequate mechanisation and irrigation infrastructure, scarcity of farmer capital, frequent disease outbreak, and inadequate storage facilities are the other value chain challenges faced by the sector.

“The Indian agriculture sector can utilise the potential of AI’s transformative capabilities through effective data practices,” Debjani Ghosh, President, Nasscom, said in a statement. “The Netherlands is a stellar example of effective AI adoption in agriculture. With just a small arable land, the country has become the world’s 2nd largest exporter of agricultural products by value leveraging technology and AI.

“For India to realise the full potential of AI, a coalition of government, industries, and startups in providing necessary infrastructure and policy support, enabling AI innovation across sectors, and mentoring and providing financial support to startups is imperative,” she said. Leveraging macro as well as farm-level data collected through sensors could help maximise yields and optimise the use of available resources, said the report.

Several AI-led use cases, such as precision agriculture and farm management, agricultural robots, automated weeding, crop quality and readiness identification, pest prediction and prevention, livestock monitoring and management, crop yield estimation, etc. can solve improving farm productivity. (IANS)

NEW DELHI: The government is considering measures to curb malpractices by foreign e-commerce companies, including widening the scope of group company as this goes to the root of controlling the sellers, said an official present at a meeting on FDI policy held on Thursday. The official said on condition of anonymity that the group should include affiliate and associate companies and prohibit direct and indirect control, be it equity or economic. A meeting to discuss FDI policy in e-commerce space was convened by the Department for Promotion of Industry and Internal Trade (DPIIT) on Thursday that was attended by 25 e-commerce companies. The FDI policy from its very beginning has not allowed foreign capital in the inventory based model and it is allowed only in pure technical infrastructure/platform which facilitate meeting of the buyer with the seller. Such a platform cannot act as a seller itself, directly or indirectly. The ill-effects of foreign capital dumping in the multi-brand retail and inventory based e-commerce space are well known, the official said. The official said the forbidden gap between inventory-based model and marketplace model has been crossed by some foreign companies by the way of legal creativity of exploiting either the loopholes or by creative interpretation of the policy that violate the policy in spirit. Such legal creativity include creating multi-level company structure to hide the real relationship between the marketplace entity and the sellers. The relationship between the leading foreign marketplace entities and some of the largest sellers on their platforms is no secret. Reports reveal the structure of the companies and show how these marketplace entities are engaged in control of inventory and their largest sellers. The continued violations create doubts about the legal sanctity of the policy, especially in the minds of new e-commerce players. Therefore, it is imperative that the rules are clarified to an extent that these are not subjected to such creative interpretations, sources said. The sources said that it must be the government’s endeavour to match the letters to the spirit of the policy so that it does not create any doubt, whatsoever, in the mind of any foreign marketplace company. These suggestions should be issued in the form of a new press note or amendment to the FDI policy, sources said. Some of the suggestions are as follows: Widening the scope of group company: This goes to the root of controlling the sellers. The group should include affiliate and associate companies and prohibit direct and indirect control, be it equity or economic. Preventing the misuse of B2B e-commerce: In either controlling the inventory or providing deep discounts through selected sellers by the way of capital dumping. Compliance to the provisions of the policy is very important. A clause in the manufacturing section seems to be an omission in the policy as the same is being used for retail trading of food products by some companies. A clause in manufacturing section cannot override the spirit of the retail trading policy, sources added. (IANS)

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Farming isn’t usually considered a high-tech sector. That’s been changing in recent years thanks to new innovations in areas like artificial intelligence (AI) and robotics. Farmers who adopt high-tech solutions are often able to increase yield, cut costs, and produce healthier food.

Perhaps that is why Alphabet’s X lab, the division responsible for launching Waymo, is eyeing farming as its next target area. The company recently revealed its latest “moonshot” project. Dubbed Mineral, it focuses on a robot that inspects crops and a platform designed to analyze that data.

The combined approach introduces the concept of “big data” to the world of farming and could have profound implications.

Working the Problem

The agriculture industry has a massive challenge ahead of it. Experts predict that global hunger problems will only continue growing as the Earth’s population expands over the next decade. Mineral’s website says, “To feed the planet’s growing population, global agriculture will need to produce more food in the next 50 years than in the previous 10,000—at a time when climate change is making our crops less productive.”

Obviously, the situation is less-than-ideal. It’s clear that traditional farming approaches won’t be enough to solve this dilemma. Fortunately, technology might be able to help.

A major part of Mineral’s plan revolves around a four-wheeled robotic prototype that somewhat resembles a moon rover. The team appropriately calls it a plant buggy. It can study crops, soil, and other characteristics of the environment over a large area thanks to a suite of cameras and sensors. Those findings are then compared with satellite photos and weather data, according to Nick Statt of The Verge.

Using that wealth of information, scientists are able to predict how plants will grow by using AI models. So far, the Mineral team is testing the approach with soybean crops in Illinois and strawberry fields in California.

The company says, “Over the past few years, the plant buggy has trundled through strawberry fields in California and soybean fields in Illinois, gathering high quality images of each plant and counting and classifying every berry and every bean. To date, the team has analyzed a range of crops like melons, berries, lettuce, oilseeds, oats and barley—from sprout to harvest.”

Data-Driven Farming

It’s clear that Mineral’s approach has real-world implications that are important for humanity’s fight against hunger. The idea of using tech to optimize plant growth isn’t something that Mineral is alone in pursing.

A number of other startups are exploring the same space. Some are even turning to alternative approaches like hydroponics and vertical agriculture to further enhance the effects of data-driven farming.

In the days to come, Mineral will continue to partner with farmers around the world to improve its system and adapt it to meet the unique needs of various agriculture segments.

Project lead Elliott Grant says, “Just as the microscope led to a transformation in how diseases are detected and managed, we hope that better tools will enable the agriculture industry to transform how food is grown.”

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Govipola, the cloud-based mobile app designed to facilitate direct selling platforms for fruit and vegetable farmers—a move aimed at cutting out middlemen and containing food inflation in Sri Lanka, will run on Microsoft Azure, a collection of cloud-based services for building reliable, scalable, and maintainable applications. The platform lowers the total cost of ownership on physical hardware and services from not having to manage IT infrastructure to deal with hosting, data security, and hardware maintenance.

Govipola is owned by Croptronix, an agri-tech company committed to the sustainability of farming in Sri Lanka. The company contracted Fortunaglobal to develop the app and deploy it on Microsoft Azure’s cloud and edge computing solutions. Fortunaglobal is a single vendor based in Colombo pioneering a wave of digital banking solutions that promote financial inclusion.

“Microsoft Azure decreased Govipola’s time to deployment and reduced Croptronix’s expenses on physical infrastructure and maintenance, said Suren Kohombange, Founder/CEO, Fortunaglobal. At Fortunaglobal, we’re committed to empowering businesses with inclusive technology like Microsoft Azure and leading the connectivity and access rates of their customers.”

Farmers who sell produce on Govipola now have access to a fair, transparent, mobile marketplace that offers higher prices for their goods. In the past, they often lost money, time, and products because sales depended on multiple layers of brokers to get their goods to markets and wholesale vendors.

Moving forward, Croptronix plans to build intelligence into the app by implementing Microsoft Azure’s AI + Machine Learning and Analytics services to derive actionable insights and measure product performance.

“At Microsoft, we believe any business should be able to integrate our open and flexible cloud computing platform,” said Hasitha Abeywardena, Country Manager, Microsoft Sri Lank & Maldives. ”Our partners have been doing a great job helping our customers to modernize their environments, to make a lot of those moves into the cloud. Poverty among rural farmers in Sri Lanka has been a particularly difficult problem to solve. That’s where Govipola and Microsoft Azure can make a difference as they reinvent both ends of the retail value chain.”

Microsoft Azure provides a range of cloud services, including those for Compute, Analytics, Storage, AI + Machine Learning, Networking, and Hybrid. Users can pick and choose from these services to develop and scale new applications, or run existing applications, in the public cloud. 

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Earlier this year, Microsoft had invited us to its Bengaluru campus for a two-day briefing on how it’s incorporating artificial intelligence (AI) in many of its business solutions, including Azure, Power BI, Teams, and Office 365. In addition to letting a few of its business partners explain how these AI-enabled services help them, the Redmond-based software giant had demonstrated its Garage-developed apps such as Kaizala, Seeing AI, and Soundscape.

In a style quite similar to Microsoft’s, Google invited us to its Roppongi Hills office in Tokyo for a one-day briefing titled, “Solve… with AI” earlier this week. The briefing was headed by Jeff Dean, a Senior Fellow and AI Lead at Google. While Microsoft’s briefing on AI mostly revolved around solutions that tackle IT business challenges, Google’s briefing addressed solutions aimed towards the “social good”. Product leads from Google AI explained how the company’s technology is being put to use in areas like healthcare, environmental conservation, agriculture, and others. Google invited a few of its business partners to add inputs and examples during the briefing.

Introduction

The briefing began with Dean delivering the keynote address in which he explained the basics of machine learning (ML), which is a subset of AI that involves training a computer to recognise patterns by example, rather than programming it with specific rules. He explained how neural networks can be trained to identify patterns that are either too vast or complex for humans with the use of relatively simple mathematical functions. ML models are developed for this purpose.

Apart from employing them in its own products, Google offers ML tools along with some reference implementation information to researchers and developers to build AI-enabled software. Examples of such tools include the open-source TensorFlow software library, CloudML platform, Cloud Vision API, Cloud Translate API, Cloud Speech API, and Cloud Natural Language API. Google incorporates ML models in its offerings, including Search, Photos, Translate, Gmail, YouTube, Chrome, etc.

Dean used the example of an air quality monitoring tool called Air Cognizer to demonstrate how TensorFlow is used in everyday mobile app development. Air Cognizer is an app developed in India as part of Celestini Project India 2018. It can help detect the air quality level of the surrounding area by scanning a picture taken through the Android device’s camera. Dean went on to say that that was only one such example of developers and researchers using Google’s machine learning tools to create AI-enabled apps and services. After Dean’s introduction, other Google AI team leaders took the stage one by one to talk about other areas in which Google’s ML efforts are making a difference.

Healthcare

Lily Peng, Product Manager for Google Health, came on stage after Dean’s introduction to talk about how Google’s AI ventures help in the field of healthcare. “We believe that technology can have a big impact in medicine, helping democratize access to care, returning attention to patients and helping researchers make scientific discoveries,” she said during her presentation. She supported her statement by citing three specific areas in which Google’s ML models are seeing success: lung cancer screening, breast cancer metastases detection, and diabetic eye disease detection.

Google’s ML model can, according to the company, analyse CT scans and predict lung malignancies in cancer screening tests. In the tests conducted by Google, the company’s model detected 5 percent more cancer cases, thereby reducing false positives by over 11 percent compared to radiologists. According to Google, early diagnosis can go a long way in treating the deadly disease but over 80 percent of lung cancers are not caught early.

In breast cancer metastases detection, Google says its ML model can find 95 percent of cancer lesions in pathology images. Google claims that pathologists can generally only detect 73 percent of cancer lesions. Its model can scan medical slides better, which are each up to 10 GigaPixels in size. Google says it’s also more successful in detecting false positives than doctors. Google says that it has found that the combination of pathologists and AI was more accurate than either alone.

Google says that, with the help of its sister company Verily, it’s becoming increasingly more successful in treating diabetic retinopathy. The company is currently piloting the use of its ML model for detection of cases of diabetic retinopathy in India and Thailand. Google believes that there’s a shortage of doctors and special equipment in many places, which is one of the reasons the disease isn’t caught early, leading to lifelong blindness amongst patients.

Environmental conservation

Julie Cattiau, a Product Manager at Google AI, explained how wildlife on the planet has decreased by 58 percent in the past half a century. According to her, Google’s AI technology is currently helping conservationists track the sound of humpback whales, an at-risk marine species, in order to prevent losing them altogether to extinction. In one bioacoustics project, Google has apparently partnered with NOAA (National Oceanic and Atmospheric Administration), which has collected over 19 years worth of underwater audio data so far.

Google says that it was able to train its neural network (or “whale classifier”) to identify the call of a humpback whale within that 19-year-long audio data set. During her presentation, Cattiau said that this was a big challenge for the researchers partly because the sound of a humpback whale can easily be mistaken for that of another type of whale or ships passing by. Google believes that its AI technology was successful and helpful in the project as listening for the call of a whale in a data set that vast is a task that would take a human being an inordinate amount of time to complete.

Topher White, the CEO of Rainforest Connection, was one of the many partners invited by Google to participate in the briefing. With the use of a proprietary technology, Rainforest Connection prevents illegal deforestation by listening for sounds of chainsaws and logging trucks in rainforests across ten countries and alerting local authorities. Its technology involves the use of refurbished solar-charged Android smartphones that use Google TensorFlow to analyse the auditory data in real-time from within a rainforest. According to White, deforestation is a bigger cause of climate change than pollution caused by vehicles.

Febriadi Pratama, the Co-Founder of Gringgo Indonesia Foundation, was another one of the many partners invited by Google for the briefing. The foundation, which is a recipient of the Google AI Impact Challenge, is currently using Google’s ML models to identify types of waste material using image recognition in the Indonesian city of Denpasar. Pratama said during his speech that the project was effectively helping the foundation rake up plastic in a city where there’s no formal system for waste management.

Agriculture

Raghu Dharmaraju, Vice President of Products & Programs at the Wadhwani Institute for Artificial Intelligence, was also one of the partners invited by Google to participate in the briefing. The institute uses a proprietary Android app along with pheromone traps to scan samples of crops for signs of pests, which, in a large farm in India, can potentially wreck a farmer’s harvest. The app uses ML models developed by Google. In his presentation, Dharmaraju said that the solution developed by the institute was notably effective in detecting pink bollworms in cotton crops in India.

Flood forecasting

Sella Nevo, a Software Engineering Manager at Google AI, took the stage to talk about the company’s flood forecasting initiative. According to him, dated, low-resolution elevation maps make it hard to predict floods in any given area. SRTM, the provider of elevation maps, hands out data that’s nearly two decades old, he said during his presentation. In a pilot project started last year in Patna, Google was able to produce high-definition elevation maps using its ML models with the help of data taken from satellites and other sources in order to forecast floods. It was then able to alert its users about a flood incident in Gandhi Ghat. The flood alert was sent out as a notification on smartphones.

“The number one issue is access to data, and we have tried to tackle that. With different types of data, we find different solutions. So, for the elevation maps, the data just doesn’t exist. So we worked on different algorithms to produce and create that data for stream gauge measurements. For various satellite data, we purchased and aggregated most of it,” Nevo told us in an interview. According to him, Google is trying to produce elevation maps that can be updated every year, unlike the ones given out by SRTM.

Accessibility

Sagar Savla, a Product Manager at Google AI, took the stage to talk about Google’s Live Transcribe app. Available in 70 languages currently, the app helps the deaf and hard-of-hearing communicate with others by transcribing speech in the real world to on-screen text. The app is developed using Google’s ML models to ensure precision in its transcription. For example, the app can tell whether the user means to say “New Jersey” or “a new jersey” depending on the context of the sentence. Talking about the app and its development, Savla said that he had used it with his grandmother, who, despite being hard of hearing, was able to join in on the conversation using the Live Transcribe app in Gujarati.

Julie Cattiau returned to the stage to talk about Project Euphonia, a Google initiative dedicated to building speech models that are trained to understand people with impaired speech. The initiative could in the future combine speech with computer vision, she said during her presentation. For example, people who suffer from speech impairments caused by neurological conditions could use gestures such as blinking to communicate with others. Cattiau said that the company’s ML models are currently being trained to recognise more gestures.

Cultural Preservation

Tarin Clanuwat, a Project Researcher at the ROIS-DS Center for Open Data in the Humanities, went on stage about an ancient cursive Japanese script called Kuzushiji. Although there are millions of books and over a billion historical documents recorded in Kuzushiji, less than 0.01 percent of the population can read it fluently today, she said during her presentation. She fears that this cultural heritage is currently at risk of becoming inaccessible in the future owing to disuse in modern texts.

Google says that Turin and her fellow researchers trained an ML model to recognise Kuzushiji characters and transcribe them into modern Japanese. According to Google, the model takes approximately two seconds to transcribe an entire page and roughly an hour to transcribe an entire book. According to test data, the model is currently capable of detecting about 2,300 character types with an average accuracy of 85 percent. Turin and her team are working towards improving the model in order to preserve the cultural heritage captured in Kuzushiji texts.

Summary

Google seems convinced it’s headed in the right direction when it comes to applying machine learning the right way for social causes. In the future, we can expect Google to take on more such projects, where neural networks are trained to understand data sets that hold keys and clues to hitherto insoluble problems in areas never tried before. At the same time, more and more developers and researchers should be able to incorporate Google’s open-source TensorFlow library in their projects as long as Google continues to provide support and reference material for it.

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It has been a widely acknowledged fact that big data has become a big game changer in most of the modern industries over the last few years. As big data continues to permeate our day-to-day lives the number of different industries that are adopting big data continues to increase. It is well said that when new technologies become cheaper and easier to use, they have the potential to transform industries. That is exactly what is happening with big data right now. Here are 10 Industries redefined the most by big data analytics-

Sports

Most elite sports have now embraced data analytics. In Premier League football games, cameras installed around the stadiums track the movement of every player with the help of pattern recognition software generating over 25 data points per player every second. What more? NFL players have installed sensors on their shoulder pads to gather intelligent insights on their performance using data mining. It was analytics which helped British rowers row their way to the Olympic gold.

Hospitality

Hotel and the luxury industry have turned to advanced analytics solutions to understand the secret behind customer satisfaction initiatives. Yield management in the hotel industry is one common use of analytics which is an important means to tackle the recurring peaks in demand throughout the year in consideration with other factors which include weather and local events, that can influence the number and nationalities of guests checking in.

Government and Public Sector Services

Analytics, data science, and big data have helped a number of cities to pilot the smart cities initiative where data collection, analytics and the IoT combine to create joined-up public services and utilities spanning the entire city. For example, a sensor network has been rolled out across all 80 of the council’s neighborhood recycling centres to help streamline collection services, so wagons can prioritize the fullest recycling centres and skip those with almost nothing in them.

Energy

The costs of extracting oil and gas are rising, and the turbulent state of international politics adds to the difficulties of exploration and drilling for new reserves. The energy industry Royal Dutch Shell, for example, has been developing the “data-driven oilfield” in an attempt to bring down the cost of drilling for oil.

And on a smaller but no less important scale, data and the Internet of Things (IoT) is disrupting the way we use energy in our homes. The rise of “smart homes” includes technology like Google’s Nest thermostat, which helps make homes more comfortable and cut down on energy wastage.

Agriculture and Farming

The power of AI has embraced even traditional industries like Agriculture and Farming. Big data practices have been adopted by the US agricultural manufacturer John Deere which has launched several data-enabled services that have led farmers to benefit from the real-time monitoring of data collected from its thousands of users worldwide.

Education

Education sector generates massive data through courseware and learning methodologies. Important insights can identify better teaching strategies, highlight areas where students may not be learning efficiently, and transform how the education is delivered. Increasingly educational establishments have been putting data into use for everything from planning school bus routes to improving classroom cleanliness.

Banking and Securities

Securities Exchange Commission (SEC) has deployed big data to track and monitor the movements in the financial market. Big data and analytics with network analytics and natural language processors is used by the stock exchanges to catch illegal trade practices in the stock market.

Retail traders, Big banks, hedge funds and other so-called ‘big boys’ in the financial markets use big data for trade analytics used in high-frequency trading, pre-trade decision-support analytics, sentiment measurement, predictive analytics, etc.

This industry also heavily relies on big data for risk analytics including; anti-money laundering, demand enterprise risk management, “Know Your Customer”, and fraud mitigation.

Entertainment, Communications and the Media

The on-demand music service, Spotify uses Hadoop big data analytics to collate data from its millions of users across the world. This data is used and analyzed to give customized music recommendations to its individual users. Over the top media, services have relied big on big data to offer customized content offerings to its users. An important move in the growing competitive market.

 Retail and Wholesale Trade

Big data has in a big way impacted the traditional brick and mortar retailers and wholesalers to current day e-commerce traders. The retail and whole industry has gathered a lot of data over time which is derived from POS scanners, RFID, customer loyalty cards, store inventory, local demographics, etc. Big data is applicable to the retail and wholesale industry to mitigate fraud, offer customized products to the end user thereby improving the user experience.

Transportation

Big data analytics finds huge application to the transportation industry. Governments of different countries use big data to control the traffic, optimize route planning and intelligent transport systems and congestion management.

Moreover, the private sector uses big data in revenue management, technological enhancements, logistics and to gain a competitive advantage.

Big data is improving user experiences, and the massive adoption change has just begun.

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The United Nations reports that about 1/3 of the food produced globally each year is lost or wasted, and I’d reckon that number is not too surprising. Those of us in the United States see evidence of waste each time we go out to eat or do a weekly purge of jam-packed refrigerators. Outside the waste, however, there’s a greater problem many of us don’t realize. Just as the amount of food wasted globally is skyrocketing, the global demand for food is, ironically, set to rise.

With exploding populations, global warming, and less land available for cultivation, we’re actually facing a global food shortage of epidemic proportions. How will we manage to feed and sustain 9 billion humans estimated to populate planet earth by 2050? And how will we support the 59-98 percent increase in food consumption that population is likely to need? Like many issues humans are facing in the world today, we are seeing digital transformation in agriculture, most specifically in the form of artificial intelligence (AI).

Sensors and Data

By far, the greatest development in agricultural technology (AgTech) comes in the form of connected sensors and the IoT. As you’d expect, successful agricultural production in digital transformation is becoming a numbers game. With the help of AgTech, connected farmers are beginning to share data, and make improvements in input, efficiencies, and operations processes, largely due to AI-driven sensors. These sensors can be ground, aerial, or machine-based, and all hold huge potential for agricultural production.

On the ground, for instance, sensors can monitor the quality of plants, soil, animal health, and weather. They can determine the best place to plant for the highest yield, and how much to plant to prevent waste. In the air, drones and satellites can monitor crop health and pest disease, preventing the surprise of a lost crop at harvest time. Farm equipment can also capture data on anticipated crop production. For instance, high-speed planting equipment can provide “as planted” estimates on crop yield and harvest output, allowing farmers to plan for sales forecasting, overflow and shortage. That’s not all. Robotic harvesting equipment can even use AI to pick ripe fruit and vegetables at just the right time, saving time, manpower, and waste. Talk about digital transformation in agriculture!

John Deere is just one company doing “precision ag” well today, developing technology to help connected farmers determine where best to plant and when to harvest. They can even help farmers manage equipment remotely from a centralized control center, allowing for even greater time efficiencies. (It goes without saying that when it comes to digital transformation in agriculture, the companies that thrive will be the ones that move from tractor provider to tech provider most seamlessly. Kudos to John Deere.)

Still, the benefits aren’t just to the farmers. Blue River Technologies, for instance, shows that they can reduce the use of herbicides by 90 pper centby moving from broadcast spraying to targeted spraying using data pulled from AI sensors. Less herbicides are good for all of us—human and earth alike. Clearly ,digital transformation in agriculture isn’t just good for food production, it’s good for the health of the planet.

Research and Development

Just like AI is helping speed up pharmaceutical trials by decreasing the length of the trial and error phase of development, it’s doing the same thing for agriculture. The AI teams at Monsanto, for instance, found that algorithms could help them more quickly determine which hybrid plants would grow best in real-life planting conditions, saving massive amounts of product development time. For instance, in the past, Monsanto would evaluate corn hybrids for years in the field before bringing them to market—a process that could take eight years from discovery to commercialization. The breeding program would select about 500 breeds for trials—a process that was cost and time prohibitive. Using an algorithm that used the past 15 years of molecular marker and field trial information, they’ve shaved an entire year off the breeding process. That’s an incredible leap, especially considering the population surge we’re about to encounter in coming decades. What’s even better: connected farmers globally will theoretically be able to share this type of information, allowing for greater and faster product development not just on Monsanto farms, but around the world.

Image Recognition

Another exciting development: image recognition in AI. Google is working to train AI to recognize 5,000 species of plants and animals, which would improve drone ability to detect pest disease and crop damage. This advancement is huge, as it would allow farmers to monitor their acreage far more quickly and accurately than they ever have before, and to understand pest patterns over time.

Reaping the Harvest of Digital Transformation in Agriculture: More Numbers and Bodies are Needed

Despite the huge potential in AgTech right now, there are also a few concerns. For one, like any AI process, AgTech relies on data. But in a market where data is produced at annual intervals, data collection can be slow and difficult. The agricultural field has also found it difficult to compete with other tech-savvy industries in attracting young AI talent. I’m hoping a younger generation, wanting to find purpose in their work, will be drawn to this promising market. Because when it comes to digital transformation in agriculture, there is potential to impact not just farmer productivity, but billions of human lives.

The post 4 Ways Artificial Intelligence Will Drive Digital Transformation In Agriculture appeared first on Artificial Intelligence.

How far are we from a computer being able to decide what variety or hybrid should be planted in a field, how it is fertilized, and prescribe crop protection chemicals needed? The promise of artificial intelligence (AI) has been a popular discussion in the media, not only for agriculture but for a variety of applications. But what is AI, how does it work in farming plus what does it mean and how does it impact agriculture?

One of the most common ways this new technology is discussed for use in agriculture is helping to make seed selection recommendations for individual fields. How does this work? How can a computer be programmed to know what seed to plant? There are a couple key aspects needed to make this new selection process work. One of the main items is the use of machine learning which represents a unique subset of algorithms within artificial intelligence. Machine learning is simply the ability for a computer to learn how to do things better or evolve without really being directly programmed by a person. The computer programming, or algorithms, start with a basic ability to perform a certain task.

One common example that people have participated in training an algorithm — and maybe didn’t know it is one — is the CAPTCHA system, which is often used to “prove you are a human” when logging into a website. The main purpose of the CAPTCHA system is to provide a higher level of security for websites, but at times it also learns from people. The system has been used in the past to train algorithms to better identify text. As people are looking at jumbled images and typing in the letters they see in an image, the machine learning algorithm verifies someone is a person when the letters are obvious while it learns and updates the algorithm for letters not initially known in an image. Once a sufficient number of people look at a letter and the majority agree on what letter it is, this information is fed back, or used to “train” the algorithm what letter is being displayed. As this process is repeated thousands or millions of times, the computer system improves its ability to recognize letters. This well-trained computer algorithm can now be used to digitize old texts that are difficult to see, and time-consuming for an actual person to try and transcribe. This training process for algorithms is used extensively today. Companies everywhere are developing machine learning algorithms and then presenting the computer with different information and letting it know the correct answer so the system learns, and is eventually able to complete the task on its own, accurately and quickly.

So now that we know how machines learn and artificial intelligence can be developed, how does that lead to a computer making a seeding recommendation? The next step in the process is to look for a doppelganger. This is where the big data term comes in to work with AI to solve problems. Companies working to provide some sort of AI-driven tool are always talking about how much data they have in their systems, and with good reason. It takes a lot of data to train the algorithms, but what are they being trained for in this instance? The idea is that in a certain soil type, with a certain weather pattern, and as many other variables as can be accounted for, seed variety X will have yield Y. If you have a big enough database of information, you can train the computer system to determine what the “best” variety is for a field. The system is essentially trained to look through all the data and find field conditions that are a doppelganger or look just like the field being asked about. Digging through (commonly called data mining) all this data allows the AI to make the determination variety XYZ is the best for this field since there is data from a bunch of other parts of different fields that have the same or similar conditions where that variety yielded better than other varieties grown in those conditions.

This is great. The computers can crunch through all this data and help farmers make more informed decisions. But why haven’t these products taken over the market yet? One hurdle, as mentioned above, is having enough data available. If you are trying to match up all the different variables for a field you need a lot of different combinations of soil types, weather, seed, fertility, planning dates, etc. You also need this data to be standardized and read into a system, and most important, for it to be accurate. As an industry this is something we struggle with. Think of a yield file from a combine. Often you’re lucky if the correct crop type was captured in the data and the yield sensor was calibrated this season. Most people in precision ag have seen fields of 250-bushel yields in “soybean” fields where the operator didn’t bother to label it as corn. For the AI system to be able to recommend a variety we not only need the correct crop type documented but we also need to know what variety was actually planted, when, and at what population to really be able to train the algorithms and mine the data.

Another obstacle is there are just a lot of variables to account for in farming. Most obvious is weather — it isn’t too hard to have a record of what happened in a field from a weather standpoint. However, knowing what the coming season is going to look like is a little trickier. So not only do you need to have all the different data about when, how, where, and which seed was planted, you need to know if it is going to be a hot dry year, or cool and wet. Forecasts are continually improving, largely from the application of the same type of AI systems to model weather to know what is coming. However forecast accuracy is really only something you can really trust out a few days, or maybe a week or two. Knowing what the next 6 months are going to be like is still fairly hard to predict for the most part, but again things are always getting better as the machines learn more.

Even though there are some hurdles the technology by its very nature is continually improving. Additionally, we as an industry are getting better at capturing accurate data these algorithms can use to continue to improve.

Machine learning is not only being used to help recommend seeds, the technology is also being applied in one way or another in applications like:

• Automated machine adjustments (combine, planter down force, etc.)
• Weather forecasting
• Disease or pest identification, image recognition
• Disease and pest movement
• Machine maintenance and break-down prediction
• Field accessibility or harvest advisory type estimations
• Irrigation and water management
• Nutrient use and fertility recommendations
• Autonomous machines or robots

One other important aspect of AI to keep in mind is the data itself. Hopefully based on this article people understand why so many companies are interested in farmers sharing data with them. Anyone developing these machine learning algorithms needs tremendous amounts of data to train their systems, otherwise their product could be less accurate than a competitor with more data. This also shows why some people have been describing data as a new form of currency. Farmers should keep this in mind as they are looking at new software tools asking for them to share their data. The data is very valuable and important to train these algorithms, but also very difficult to quantify the monetary value of any particular set of data fed into the system. Individually the data records have little value, their true significance comes though when combined with many other records. It is important all parties know what is going on, who needs what data, what they are doing with it, and what benefits each party receives from sharing it. This is an exciting time to be involved with agriculture and AI will undoubtedly have a substantial impact on how farming is done for years to come.

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For thousands of years, the survival of farmers’ crops — and finances — have been inextricably linked to the weather. Whether growing corn on the plains of Nebraska or wheat in the mountains of southeastern Turkey, Mother Nature’s seemingly unpredictable temperament has forced farmers to find innovative ways of better understanding soil moisture, rainfall, and crop health throughout the ages.

One fact that doesn’t change, however, is that crops need water. And, whether they get it from natural rainfall or modern irrigation methods, crops need it consistently at key growing points to remain healthy and abundant.

In 2018, new technologies in agriculture are changing the stakes. Farmers, agronomists, and field researchers are turning to a superior set of tools to help them understand the complexities of moisture for their crops. And while conversations among growers still center around weather forecasts and local rain gauges, thanks to these virtual tools we now have a deeper understanding of current and future moisture conditions, and how they impact crops.

Models and data analytics not only recap what is already occurring between water and plants across expansive rows of corn, they can actually predict what will come in the hours, days and weeks ahead. This gives farmers and agronomists a chance to understand what is happening at field level, so they can build a plan that gives them a distinct advantage over the natural elements for the first time in history.

In the field, predictive modeling shows promise

While irrigation is not used in all areas of production agriculture, many parts of the country rely on it to grow crops. The need is especially great when it comes to specialty crops grown in regions where water is scarce, like California and Arizona. Even in the Midwest, where water is more available, the energy (such as electricity) needed to pump that resource to the crop can make the process too costly.

With a robust data set of ag-specific, hourly, and daily weather information going back several decades, there are tools available today that can help farmers and field researchers predict future outcomes by combining weather data with soil, crop, and grower-provided information. This data drives a series of models that look at the interaction between the plants, soil and weather, which can then be used to better predict when certain things are going to occur and what you can do about them to be a better manager.

And while in many cases high-quality sensing serves as a reliable method of data collection, the resulting information can then help to model future scenarios, eliminating the extensive need for physical probes or in-field sensors, and ultimately saving farmers precious time and money.

Models use robust data to improve decisions

Predictive models are only as good as the data they contain. This technology uses artificial intelligence and neural networking that work in much the same way as the human brain functions to execute decision making and operational functions across the body. These agricultural models get smarter and better as they ingest more data, learning from the processing and analysis churned out of prior information run through their frameworks. These advanced systems track complex sets of information gathered by various devices, including harvesters and other farm equipment, looking to identify any correlations and building crop models automatically. They work more quickly than traditional decision-support tools — and at a fraction of the cost.

This unique ability to manage critical information — to inform decision analysis in an automated system at both the atmospheric and soil levels — is a key advancement for farmers looking to improve their odds, but also for crop science companies. In fact, digital ag teams at several crop science companies are using predictive models to help deliver this more accurate, scalable data model to significantly more farmers, as well as to support the scientists’ own research and development efforts.

Better decisions boost farm confidence and profits

Annual electricity bills to run an irrigation system in the central part of the country can easily run to $50,000 or more. The stakes are much higher in the west, where water is scarce and sensitive crops are more valuable on a per-acre basis. In either case, more responsible use of water in agriculture is increasingly important to the farmer’s bottom line and to the sustainability of agriculture as a whole.

The true measure of success comes back to the same variables that drove farmers on the American plains to track weather and innovate irrigation systems in years gone by — a strong desire to be efficient with water and technology in support of a sustainable and economically viable crop.

Predictive modeling is transforming the way farmers make critical decisions about the lands they farm, leading to more sustainable and productive yields with less costly hardware involved. To drive the proliferation of this technology, predictive analytics companies have been partnering with international crop science companies, which cover many million hectares of farmland spanning multiple continents, in support of their R&D efforts, as well as the tools they develop for farmers. Ultimately, this means they can help a far greater number of farmers than if they only focused our efforts on working directly with the farmers themselves.

Like most innovations in machine learning, this technology is designed to constantly and consistently self-improve — whereby as each new crop year passes, more robust data is added to the model, training it to better respond and predict outcomes based on the latest atmospheric realities. This year’s observations may be the beginning of even better results to come, where weather, soil and crop information combine to unearth greater opportunities for farmers and consumers while saving precious time and money.

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Artificial intelligence will soon be powering robots that will essentially replace farmers to pick vegetables. Thanks to the global warming, the increasing demand in agriculture, and the lack of ample land for farming, robotics is the solution seem by many to meet the demand of the exploding global population.

An American startup, Root AI, is working towards the direction of making indoor farming the next big thing in agriculture where robots, and not humans, help produce and harvest the crop with maximum optimisation of resources.

One of the first inventions coming from Root AI is a mobile robot for indoor farming facilities. Using the cameras and sensors on it, the robot is capable of picking tomatoes the right way and assess the health of the crops, conduct operations such as pruning vines, observing and controlling ripening to cultivate crops, TechCrunch reported.

Root AI is expected to begin the pilot tests from 2019. Given that the US, and other parts of the world, are facing a labour shortage in farming, the concept of indoor farming with AI at the core is likely to gain popularity

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