Farming is one of the world's most important industries. Arguably as the food crisis worsens, it will continue to become even more important. As it does, farmers will need help to increase crops yield and livestock growth. A large part of this help will come from technology and, more specifically, from artificial intelligence (AI) and machine learning (ML).

To put just how important AI and ML are to farming into context, BI Intelligence Research estimates global spending on smart, connected agricultural technologies as a whole will triple in revenue by 2025, reaching $15.3 billion.

Meanwhile, spending on AI alone in the agriculture sector is predicted to rise from $1bn in 2020 to $4bn in 2026 according to Markets&Markets. During the same period IoT (Internet of Things)-enabled agricultural (IoTAg) monitoring will be, PwC predicts, agriculture's fastest-growing technology with the potential to reach $4.5bn of global spend by 2025.

The reason agriculture is such fertile ground for AI and ML is that farms cover such a vast and differing surface area that it is very difficult to keep up with what is happening across what could be hundreds or even thousands of acres.

Those responsible for keeping agricultural land in top healthy and productive order need to know how weather, seasonal sunlight, animals, birds and insects will impact them at any point during their crop or livestock's annual cycle.

They also need to know how to generate the optimum return from their fertilizers, insecticides, herbicides, and planting irrigation cycles. Even the smallest adjustment can have a major effect on output.

These conundrums are perfect for AI and ML.

Algorithms can analyse huge amounts of data with complete accuracy, extrapolating the answers required to give famers, co-operatives, agricultural development agencies and even national governments the direction they need to improve agricultural yields and quality.

In this special report we will look at the ways AI and ML are helping the agricultural sector and, given the vital role innovation will play in enabling technology to do even more for farmers, how best to protect the new ideas that will inevitably shape agritech over the next decade.

HOW IS AI IMPROVING AGRICULTURE?

AI has already proved that the output from its real-time analysis of vast amounts of data from an equally vast breadth of sources and sensors can increase agricultural efficiencies, improve crop yields, and reduce food production costs.

Given the United Nations expect the world's population will increase by 2 billion people by 2050, food production will need to increase by 60% to feed them.

This means every opportunity to increase agricultural efficiencies, improve crop yields, and reduce food production costs must be taken.

AI and ML are already helping farmers achieve these aims. Outlined over the following pages are some of the practical applications that will yield the desired results:

  • PRICE FORECASTING

    Analysing the yield rates and quality levels of crops can help agricultural businesses at all levels negotiate for the best possible prices for their goods by determining the total demand for a crop against its pricing.

    Signs are this single application of AI could save agricultural businesses millions in lost revenue every year.

    Given the current state of the economy this cannot be ignored. This is why this point heads our list.
  • IMPROVED CROP YIELD PREDICTION

    Data captured by smart sensors and drones in real-time offers farmers a depth of data they have never had access to before. Once combined with data on moisture, nutrient levels and fertilizer taken by in-ground sensors, farmers have the insight they need to adapt their approaches on a field-by-field basis to optimise crop yields and work out how best to use specific fertilizers to maximise different crops and different areas of the farm.

  • SMART TRACTORS, AGRIBOTS AND ROBOTICS

    Large-scale agricultural businesses often can't find or afford enough employees. Robotics offers the perfect solution.

    They can irrigate and secure hundreds of acres of crops. They can accurately distribute fertilizers, pesticides, and herbicides depending on the individual needs of each row of crop which cuts cost and increases productivity. Using AI and lasers, state-of-the-art autonomous robots can even identify and kill weeds in real time, cutting time spent in the field and minimising herbicide requirements. The rapid steps forward in terms of the sophistication of these 'agribots' will only make their contribution more valuable in the future.

    Agricultural robotics can also collect invaluable data that can be added to the other data sets AI solutions are using for other purposes.

  • YIELD MAPPING

    Yield mapping is an agricultural technique that uses algorithms to find patterns from large volumes of data taken from in- or on-ground sensors and drones. The conclusions can then be used to improve the crop planning process to maximise potential soil yields for any given crop.

  • PEST MANAGEMENT

    Infrared camera data from drones can be combined with data taken from sensors on fields to give a highly accurate assessment of the plants' relative health levels and the current and potential threat posed by pests so that AI can future-model for pesticide use and deployment based on the findings.

  • HELPING GET CROPS TO MARKET MORE QUICKLY AND MORE SAFELY

    Track-and-traceability in all agricultural supply chains is now a must. Knowing exactly where things have come from and how long they have been in transit is key to preventing food wastage.

    The most advanced track-and-trace systems now employ advanced sensors to gain greater knowledge of every aspect of each shipment's condition. This not only prevents the quality of food suffering at any stage of the supply chain, it also provides the data the transporters need to optimise the length, cost, and conditions of transporting particular foods area by area.

  • FINDING THE RIGHT MIX OF AND BEST USE OF PESTICIDES AND HERBICIDES

    The combination of data collated from sensors and drones can be hugely effective in detecting crops' most vulnerable areas then use this insight to ascertain the optimal combination of pesticides and herbicides to reduce the risk of pests and weeds damaging or stunting any part of a healthy crop.

  • BETTER WATER MANAGEMENT

    AI can be used to optimise irrigation systems. It can not only measure and model crop irrigation to improve yield rates, but also find irrigation leaks to minimise the cost and impact of water wastage to help preserve what is becoming a more and more scarce natural resource.

  • MONITORING LIVESTOCK HEALTH

    One of the fastest-growing aspects of AI and machine learning in agriculture is the monitoring of livestock's vital signs, daily activity levels, and food intake. Better understanding of how every type of livestock reacts to diet and boarding conditions is helping farmers improve the way they are treated long-term, so they can deliver the best quality products.

  • REAL TIME SURVEILLANCE AND ALERTS

    One of the biggest threats to crops comes from domestic and wild animals. Until now they could destroy an entire crop in a remote field. Today, AI and ML driven video surveillance systems offer farmers an extra layer of protection for their fields and buildings, identifying and alerting you to breaches and even identifying employees versus unknown individuals.

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The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.