How AI Will Yield Major Benefits For Agriculture
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.
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.
