How will artificial intelligence bring significant benefits to agriculture?

Driven by the rapid development of innovative technologies and partnerships between agribusiness and technology, modern agriculture is on the verge of the kind of digital transformation process we are seeing in many other industries.

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

According to the same study, this astonishing acceleration in AI adoption is due to “increased implementation of data generation through sensors and aerial imagery of crops, increased crop yields through deep learning technology, and government support for the adoption of modern agricultural technologies.”

Smart farming

But where is the focus for this technology-led innovation? Smart farming, for example, is a self-contained system that can collect and process key data sets to yield actionable insights. In practice, this could mean using sensors, cameras, and drones to evaluate and determine optimal growing conditions. In doing so, there is scope to deliver huge productivity benefits across the board.

Another application of AI and other key technologies is using data to help shorten crop cycles. By measuring and monitoring factors such as light intensity, temperature and nutrient levels, for example, farmers can understand precisely what speeds production for each type of crop.

In fact, today’s most advanced agricultural companies will implement a set of cameras, sensors, gateways, data storage devices, analysis tools, and an implementation layer to help farmers use less to increase growth. This includes minimizing the use of important resources from land and water to pesticides and herbicides.

Equipped with infrared cameras, sensors and computer vision systems, crops can be monitored and measured in real time. From detecting changes in temperature and humidity to alerting farmers to the emergence of crop-based diseases, machine learning technologies are playing an increasingly pivotal role throughout the production life cycle. In doing so, they can monitor a broader range of factors with greater precision than is practical using traditional methods.

Speed ​​up production processes

Across a growing number of farms around the world, AI is helping speed up production processes and optimize the use of valuable resources. In the United Kingdom, for example, a normal wheat crop cycle might require six to ten months in the fields or four to six months when grown in a greenhouse. In contrast, farms that use “rapid breeding” technology powered by smart technology can reduce these life cycles to as little as two to three months.

© Intellias

This also gives the farmer the opportunity to run more production cycles each year. One NASA experiment found, for example, that exposing plants to intense lighting regimes can lead to six crop cycles per year—up from the previous limit of two. The scientists were able to achieve this increase in production while also maintaining the quality and yield of the crops involved, and to do so while significantly reducing the length of the crop cycle.

This fits with the general need across agriculture to achieve more with less. It also plays a significant role in driving the adoption of automation technologies and processes that can harness the right data to deliver a solution tailored to each farm’s needs.

But how do the potential costs and benefits of these solutions stack up against the results? A typical smart farming system would cost between £250,000 to £400,000 to develop and could help grow between one to four additional crop cycles each year, depending on the crop. By adding just one additional crop cycle, the farm can recoup the costs of its technical infrastructure and add a profit within the first year.

In addition, operating a farm via a single point of control makes it possible to run intelligent, independent operations with minimal farmer intervention. The time and labor cost savings of these AI-driven efficiencies alone are significant, particularly when labor shortages are causing increasing financial losses on farms in the UK and abroad.

To help bridge this gap, effective process management software is helping to track how AI systems control automated farming operations without farmers having to go through multiple evaluation visits. IoT systems can also be tuned to ensure accurate monitoring and reporting across key areas of farming operations throughout the production lifecycle.

There is no doubt that technology-led innovation is accelerating throughout the agricultural industry, with AI technologies likely to play a leading role in the efficiency and profitability of farms everywhere in the coming years. And while no smart farming system is ready to maximize production efficiency and yields, a combination of IoT-enabled data analytics and monitoring will help farmers find the right solution for their unique circumstances.

Looking into the future, agricultural companies can confidently look forward to using AI technologies to build efficient, sustainable, and highly productive farms. In doing so, they can ideally position themselves to balance production, profitability, and environmental responsibility to help farms meet the needs of each stakeholder.


About the author

Dmytro Lennyi is Senior Director of Delivery and AgriTech Practice Lead at Intellias. Intellias is a trusted technology partner for top-tier organizations and digital natives, helping them accelerate the pace of sustainable digitalisation. For more than 20 years, Intellias has been building mission-critical projects and delivering measurable results that meet our customers’ business needs. We contribute to the success of the world’s leading brands, among them HERE Technologies, LG, Siemens, Swissquote Bank, KIA, TomTom, HelloFresh, Xerox PARC and Deloitte. Intellias enables companies operating in Europe, North America and the Middle East to adopt innovation at scale.

Featured image: © Mose Schneider


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