Piglet’s Big Brother: How AI Changes Agriculture

  • Sergei Nikolenko

    Agriculture, considered as something old-fashioned, is increasingly on the cutting edge of artificial intelligence

Piglet’s Big Brother: How AI Changes Agriculture
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Piglet’s Big Brother

Throughout the history of time, scientists and farmers have been working together, searching for ways to advance agricultural practices by utilizing cutting-edge technology to increase yields and profit, while ensuring crops and animals are happy and healthy. Now, we’re seeing the use of deep learning and specifically computer vision emerging in agriculture, revolutionizing the industry that feeds billions.  

Computer vision for crop analysis: don’t just see, observe

Computer vision for crop analysis

Modern agriculture is a huge industry. Feeding billions of people takes a lot of land, and it becomes virtually impossible to process everything by hand. In particular, devastating plant diseases and pest invasions have often led to failed crops, and with the modern scale of agriculture, it is hard to spot such an invasion early on.

This is an important area where computer vision algorithms can help. Plant breeders use computer vision to recognize crop disease, both in detail from close-up views of leaves and plants and recognizing early onsets of plant disease and pests from surveillance photos.

All of this research is usually based on the modern go-to approach to computer vision: convolutional neural networks (CNNs).

Note that computer vision can be understood very broadly here. In many situations, photos are not the best kind of data that can shed light on important aspects of plant life. Often it is even better to use remote sensing data such as hyperspectral imaging or 3D scanning, which are increasingly used in agriscience.

This kind of data is usually high-dimensional and closer in nature to medical imaging than photos: check out, for example, a crop surveillance system called AgMRI. This requires special models for processing, but it still has the spatial structure required to apply modern computer vision techniques, in particular, convolutional neural networks.

This is, pardon the pun, a rapidly growing field: for example, recently $37 mln were invested into the establishment of Plant Phenotyping and Imaging Research Centre at the University of Saskatchewan.

The entire institution is devoted to exactly to the purpose of gathering big data about crops, usually in the form of photos and remote sensing data, linking extracted phenotype data to plant genotypes, to improve agriculture around the world.

Robotics in agriculture

Robotics in agriculture

Meet Prospero, the adorable farming robot that can’t help but remind you of WALL-E. It can drill a hole and plant something inside, accounting for local features of the terrain while following the predefined general patterns.

Then it can tend the crops, working with each one individually. And when the time comes, it can harvest, again being able to treat each plant in exactly the way it needs.

The idea behind Prospero is swarm farming: watch this video and imagine an army of small Prosperos crawling through the fields, leaving neat, perfect lines of crops behind.

Interestingly, Prospero predates much of the latest deep learning craze. It appeared back in 2011, but still remains a prototype that has not seen widespread use yet. But by now, robots are overcoming agriculture, automating more and more repetitive tasks:

  • automated flying drones are spraying the crops, and are  much more accurate with dangerous chemicals than full-sized airplanes; the same spraying drones can also be used to gather the aerial photos needed for computer vision applications we discussed in the first part

  • specialized harvesting robots are being increasingly developed and used; we have had grain harvesters for a long time, but only now, with the help of modern AI in both computer vision and robotics, we can develop a robot that can pick strawberries

  • Hortibot recently developed by researchers at Aarhus University, Denmark, can identify and eliminate weeds, either removing them mechanically or precision-spraying with herbicides

While many of these robots are still prototypes or being tested in small circulation, it is fair to say that robotics and agriculture are clearly made for each other, with more agricultural work to be automated in the nearest future.

Video tracking for farm animals: piglet’s big brother

Video tracking for farm animals: piglet’s big brother

There is a third agricultural application that’s worth discussing, a pilot project that the Neuromation company is planning to launch this year, with the potential to revolutionize an entire industry—and a large one, too. The plan is to bring modern computer vision to an industry that has not yet received much attention from the deep learning community: animal husbandry.

Naturally, people have already tried to bring big data and automated tracking to farm animals. For instance, a Pakistani startup called Cowlar produced a wearable collar that tracks activity and temperature of cows and buffaloes, under a catchy slogan of “FitBit for Cows,” and French researchers have been developing face recognition for cows. In this project, automated data collection and computer vision are used in a previously overlooked trillion-dollar industry—pig farming.

The bulk of the costs in pig farming falls on feeding the animals, bringing them from piglets to maturity as efficiently as possible. So optimizing the feeding process is what pig farming is mostly about.

Farmers would gain enormously from having detailed information about how the pigs are progressing. Currently, animals are usually weighed only twice over their whole lives: before and after feeding. It would not be too hard to tailor the feeding to individual pigs, significantly improving the output, if we knew how each piglet is doing with the feeding.

But this is where the twist lies.  It's easy enough to weigh a pig, although it causes stress for the animal and stress leads to weight loss! This catch-22 means that while there’d be data on a small subset of select pigs, it would not be worth it to weigh every pig as often as desired- or, really, at all.

This is a novel, non-invasive approach to weighing animals: a computer vision model that will accurately estimate the weight by photo and video data. The result will feed into analytic machine learning models that will improve the feeding process, leading to more sweet and tender pork for next year’s New Year celebrations.

Summary: agriculture on the cutting edge

Summary: agriculture on the cutting edge

It is often customary to stereotype agriculture and farming as something old-fashioned and outdated. However, as we have seen, agriculture is increasingly on the cutting edge of artificial intelligence.

The reason is that many agricultural problems are at the same time:

  • hard enough that they could not be automated before the advent of modern AI and deep learning: crops and pigs are alike, but not quite as alike as cars on a Henry Ford conveyor belt, so human work has been absolutely necessary up until very recently

  • easy enough that by now we already have the tools to tackle them, respecting the individual differences between plants and animals but at the same time learning to automate general techniques of handling them, driving a tractor on a field is easier than driving a car in human traffic, and measuring a pig’s weight easier than talking to people.

Agriculture is still one of the largest and most important industries on the planet, with even small increases in efficiency leading to huge windfalls due to the massive scale of things. For all these reasons the trend to automate agriculture is to continue into 2018 and beyond.

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How Artificial Intelligence (AI) Penetrates the Crypto World

  • Alex Morris

    The utilization of AI-powered instruments becomes the most recent trend in crypto trading

How Artificial Intelligence (AI) Penetrates the Crypto World
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Artificial intelligence is nothing new in the world of finance, since traders have been using traditional algorithms for decades. However, the new generation of AI technologies, which is currently entering the field of cryptocurrency marketplaces, may be a truly revolutionizing solution for traders who constantly have to deal with rife market manipulations.

Let’s find out how widespread the adoption of AI in the crypto space is and what benefits it gives to crypto holders.

Actives’ analysis

One of the most effective ways to predict the future value of assets is to make forecasts with the help of AI technologies.

There have been several platforms like Augur and NeuroBot that utilize AI-powered tools in order to analyze crypto assets and come up with near-precise price predictions that are based on crypto exchange rates.

AI-driven services come out as a low-cost solution for investment portfolio management problems. Such tools are able to functions 24/7 and are less prone to mistakes than humans are.

Conducting AI analysis of a specific cryptocurrency is crucially important to determine whether there is a positive/negative outlook and how successful a potential investment can be.    

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Artificial intelligence has been widely utilized in cryptocurrency trading which is usually a no-go zone for inexperienced traders due to a high market volatility. However, AI poses as a game changer, since it is able to remove a human factor and instantaneously determine possible risks before you even manage to place your order.

An AI-based Chinese crypto exchange Bibox already has more than 50,000 daily active traders. Because of AI-enhancement, traders are able to use unique tools for market analysis and forecasting.  

While there is always a room for ambitious investors who are able to go all-in, AI allows to significantly reduce the risk of losing money to almost non-existent by making almost 100 percent precise predictions. For beginners, this is a wonderful tool for in-depth market analysis that helps to make wise investment decisions without extensive knowledge in finance.

However, there is a lingering question– what’s the point of being a trader if AI tools are so powerful?  In fact, a human always stands behind artificial intelligence by designing specific algorithms that can either lead to big gains or tremendous losses.

Another issue with AI-powered crypto trading is that it requires huge processing power for precise results that no ordinary person can afford which places traders in an unequal position. Therefore, one can make a conclusion that AI technologies are more beneficial for big companies.

Community mood

There is already an algorithmic crypto-asset manager named NapoleonX that helps to gather data about the market mood (including their emotional state of investors, their sentiment and opinions). Understanding the community mood can help to identify behavioral patterns that are typical for the crypto space.

Similarly to the dot-com craze in the late 90s, crypto speculations are prevalent as the market still remains in the stage of its inception. A study shows that the crypto community is rather guided by emotions and mood than different economic data. Sentiment analysis that can be conducted with the help of artificial intelligence can be a game-changer in this industry where everyone strives to find out who will be modern day’s Google in the cryptocurrency world.

Free AI bots allow users to review market sentiment, emotions, opinions for Bitcoin and Ethereum, the two biggest cryptocurrencies on the market.

While sentiment can only range from positive to negative, there are different kinds of emotions included, such as excitement, fear, sadness, surprise and anger. You are also able to choose between social sources and media sources which may impose a different effect on a trader’s emotions.

Data related to sentiment and emotions is useful for predicting how the volatile the market will be in the future: highly positive sentiment, for example, may be a sign that you are dealing with a market bubble that is about to burst. When it comes to opinions, there are only two options for crypto bulls and crypto bears.

Also, check Matrix AI

Matrix AI (MAN) is considered to be a groundbreaking China-based blockchain platform that conveniently utilizes AI technologies for security enhancement by means of automated auditing and self-optimization.

The network offers as many as 50,000 transactions per second and, on top of that, its TPS rate is expected to increase up to one mln in the future.

Another important feature that may potentially lead to Matrix’s mainstream adoption is the ability to create smart contracts in your native tongue which makes them more accessible for anyone and eliminates the need to learn programming languages in order to use the blockchain technology (only 20 mln out seven bln people have extensive programming skills).

With Matrix, English, Chinese or any other language will be converted into programming one with the help of a code generator.

Although, Matrix AI currently sits at a very humble price of only $0.37, there are plenty of predictions that such a revolutionizing technology won’t go unnoticed and could become the next 1,000 percent ICO. Some really serious developers stand behind this project (including former Google and Microsoft employees). You can find more information about their impressive team on Matrix’s official website.

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Hiring AI devs

KickCoin (KICK), which currently makes into the top 150 coins by market cap, is also stepping up its AI game by expanding the team of developers. This Blockchain-based platform was specifically designed for organizing fundraising events (ICO, pre-ICO and so). KICK’s price is a bit underwhelming ($0.104, but performance indicators are showing an ongoing positive trend as crypto crowdfunding becomes more popular. With further utilization of artificial intelligence, KickCoin may significantly improve its positions.     

The new project named U.Community is still in the early stage of development, but it is a very ambitious endeavor, since they want to come up with a multifunctional platform that will a cryptocurrency exchange along with AI-powered instruments for market analysis. You can also see the list of other Blockchain projects that utilize artificial intelligence here.

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