Propelled by the rapid development of innovative technologies and agribusiness-tech partnerships, modern farming is on the verge of the kind of digital transformation process seen across many other industries.
In fact, research predicts that by 2026, the AI in agriculture market will grow at over 25% per year to reach a value of $4 billion.
According to the same study, this impressive acceleration in the adoption of AI is due to the “increasing implementation of data generation through sensors and aerial images for crops, increasing crop productivity through deep learning technology, and government support for the adoption of modern agricultural techniques.”
But where is this tech-led innovation being focused? Smart farming, for example, is an autonomous end-to-end system that can gather and process key datasets to give actionable insights. In practical terms, this can mean using sensors, cameras and drones to assess and identify optimum growing conditions. In doing so, there is scope to deliver huge productivity benefits across the board.
Another application for 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 instance, farmers can more precisely understand what accelerates production for each type of crop.
Indeed, today’s most advanced agribusinesses will typically implement a range of cameras, sensors, gateways, data storage devices, analytics tools, and an implementation layer to help farmers use less to grow more. This includes minimising the use of important resources from land and water to insecticides 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 lifecycle. In doing so, they can monitor a wider range of factors with greater precision than is practical by using traditional methods.
Accelerating production processes
Across an increasing number of farms worldwide, AI is helping to speed up production processes and optimise the use of valuable resources. In the UK, for instance, a normal wheat crop cycle might require six to ten months in fields or four to six months when grown in a greenhouse. In contrast, farms using smart technology-powered “speed breeding” can reduce these lifecycles to as little as two to three months.
This also gives farms the opportunity to run a greater number of production cycles each year. One experiment conducted by NASA, for example, found that the exposure of plants to intensive light regimes could result in six crop cycles per year – up from the previous limit of two. Scientists were able to achieve this production boost while also maintaining the quality and yield of the crops in question, and do so while significantly reducing the length of the crop cycle.
This fits in with a general need across agriculture to do more with less. It is also playing a significant role in boosting the adoption of automation technologies and processes that can harness the right data to offer a solution customised to the needs of each farm.
But how do the potential costs and benefits of these solutions stack up against the results? A typical smart farming system might cost between £250,000 to £400,000 to develop and may help grow between one and four additional crop cycles per year, depending on the crop. By adding just a single additional crop cycle, a farm could recoup its tech infrastructure costs and still add profit within the first year.
In addition, operating a farm via a single point of control makes it possible to run smart, autonomous processes with minimal farmer intervention. The time and labour cost savings of these AI-driven efficiencies alone are significant, especially when labour shortages are taking an increasing financial toll on farms in the UK and beyond.
To help bridge this gap, effective operation management software helps track the way AI systems are controlling automated farming processes without farmers having to make multiple assessment visits. IoT systems can also be fine-tuned to ensure accurate monitoring and reporting across key areas of the farming processes during the production lifecycle.
There is no doubt that tech-led innovation is accelerating throughout the farming industry, with Artificial Intelligence technologies likely to play a leading role in the efficiency and profitability of farms everywhere in the years ahead. And while there is no off-the-shelf smart farming system to maximise production efficiency and yields, a combination of data analytics and IoT-enabled monitoring will help farmers find the right solution for their unique circumstances.
Looking to the future, agribusinesses can look with confidence to their use of AI technologies for building efficient, sustainable, and highly productive farms. In doing so, they can put themselves in the ideal position to balance production and profitability with environmental responsibility to help farms meet the needs of every stakeholder.
About the Author
Dmytro Lennyi is Senior Delivery Manager and AgriTech Practice Leader at Intellias. Intellias is a trusted technology partner to top-tier organizations and digital natives helping them accelerate their pace of sustainable digitalization. For over 20 years Intellias has been building mission-critical projects and delivering measurable outcomes that meet our clients’ business needs. We are contributing to the success of the world’s leading brands, among which are HERE Technologies, LG, Siemens, Swissquote Bank, KIA, TomTom, HelloFresh, Xerox PARC, and Deloitte. Intellias empowers businesses operating in Europe, North America, and the Middle East to embrace innovation at scale.
Featured image: ©Mose Schneider