How AI Will Yield Major Benefits For Agriculture

Fueled by the rapid development of innovative technologies and agribusiness technology partnerships, modern agriculture is on the brink of a digital transformation process of the kind seen in many other industries.

In fact, research predicts that the agricultural AI market will grow by more than 25% per year to reach a value of $4 billion by 2026.

According to the same study, this impressive acceleration in AI adoption is due to “increasing data generation from sensors and aerial imagery of crops, improvements in crop productivity through deep learning techniques, and government support for the adoption of modern agricultural technologies”

Smart Agriculture

But where is the point of this technology-led innovation? For example, smart farming is an autonomous end-to-end system that collects and processes critical data sets to provide actionable insights. In practice, this could mean using sensors, cameras and drones to assess and determine optimal growing conditions. As a result, huge productivity advantages are possible across the board.

Another application of AI and other key technologies is the use of data to help shorten crop cycles. For example, by measuring and monitoring factors such as light intensity, temperature and nutrient levels, farmers can more precisely understand what is accelerating the production of each crop.

In fact, today’s most advanced agribusinesses typically implement an array of cameras, sensors, gateways, data storage devices, analytics tools, and an implementation layer to help farmers grow more with fewer resources. This includes minimizing the use of vital resources, from land and water to pesticides and herbicides.

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Equipped with infrared cameras, sensors and computer vision systems, it can monitor and measure crops in real time. From detecting changes in temperature and humidity to alerting farmers to the emergence of crop diseases, machine learning techniques are playing an increasingly important role throughout the production lifecycle. By doing so, they can monitor a wider range of factors with greater precision than using traditional methods.

speed up the production process

On a growing number of farms around the world, artificial intelligence is helping to speed up production processes and optimize the use of precious resources. In the UK, for example, a normal wheat crop cycle can take six to ten months in the field and four to six months in the greenhouse. By contrast, farms using “rapid breeding” driven by smart technology can shorten these life cycles to two to three months.

© Intellias

This also gives the farm the opportunity to run more production cycles per year. For example, an experiment by NASA found that exposing plants to bright light could result in six crop cycles per year—up from the previous limit of two. The scientists were able to achieve this increase in yield while maintaining the quality and yield of the associated crop, while significantly reducing the length of the crop cycle.

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This fits with a general need across agriculture to do more with less. It also plays an important role in facilitating the adoption of automation technologies and processes that can leverage the right data to deliver solutions tailored to the needs of each farm.

But how do the potential costs and benefits of these solutions compare with the outcomes? A typical smart farming system could cost between £250,000 and £400,000 to develop and could help add between one and four crop cycles per year, depending on the crop. By adding an additional crop cycle, the farm can recoup its technical infrastructure costs and still increase profits within the first year.

Additionally, operating a farm through a single point of control allows for smart, autonomous processes to be run with minimal intervention by the farmer. The time and labor cost savings from these AI-driven efficiencies alone can be substantial, especially as labor shortages take their toll on farms in the UK and beyond.

To help bridge this gap, effective operations management software can help track how AI systems control automated farming processes without requiring farmers to make multiple assessment visits. IoT systems can also be fine-tuned to ensure accurate monitoring and reporting on key areas of the farming process during the production lifecycle.

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There is no doubt that technology-led innovation is accelerating across the agricultural industry, and artificial intelligence technologies are likely to play a leading role in the efficiency and profitability of farms everywhere in the coming years. While there are no off-the-shelf smart farming systems to maximize productivity and yield, the combination of data analytics and IoT-enabled monitoring will help farmers find the right solution for their unique situation.

Looking to the future, agribusiness can use AI technologies with confidence to build efficient, sustainable and productive farms. In doing so, they can position themselves in the ideal position to balance production and profitability with environmental responsibility, helping farms meet the needs of each stakeholder.

about the author

Dmytro Lennyi is Intellias’ Senior Delivery Manager and AgriTech Practice Leader. Intellias is a trusted technology partner for top organizations and digital natives, helping them accelerate their sustainable digital journey. For over 20 years, Intellias has been structuring mission-critical projects and delivering measurable results to meet clients’ business needs. We’re contributing to the success of the world’s leading brands, including HERE Technologies, LG, Siemens, Swissquote Bank, KIA, TomTom, HelloFresh, Xerox PARC and Deloitte. Intellias enables businesses operating in Europe, North America and the Middle East to embrace innovation at scale.

Featured Image: © Mose Schneider


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