Using Remote Guidance In Agriculture to Meet Global Food Supply Needs
Discover the nature of remote guidance systems in the world of automated agriculture as they are used to accommodate labor shortages, maintain the world’s food supply, and elevate the efficiency and sustainability of farming.
In this article, we will explore automation in agriculture and its role in global food security, paying particular attention to remote guidance systems: what they are, what technologies they employ, and how, why, and where they are being used today.
Global Food Supply - Is Automation the Answer?
According to the latest projections from the United Nation's World Population Propects 2022, the world’s population is projected to grow to around 8.5 billion in 2030. By 2050, the population could reach 9.7 billion and then hit a peak of 10.4 billion in 2086. This global rise in population is putting increasing pressure on agriculture to produce more food.
These projections are startling, seeing as the world today is already battling global climate change, along with its debilitating consequences: floods and wildfires have ravaged countries throughout the world this summer, destroying homes and arable land. The devastating effects caused by the coronavirus pandemc and the war in Ukraine have also added to global food security risks. According to National Geographic, we would need to double our current crop supply by 2050 to keep our heads above water.
The use of GIS and GPS allow farmers to collect real-time data for a variety of tasks. Image used courtesy of Solinftec
Remote Guidance in Precision Agriculture
In a report from Brandessence Market Research and Consulting Pvt ltd., Brandessence predicts the agricultural robots market to grow from a value of nearly 4.6 billion USD in 2020 to almost 26.7 billion by 2027, with a compound annual growth rate (CAGR) of 28.7%.
Typically, these systems use geographic information systems (GIS) and Global Positioning Systems (GPS) to provide geospatial data collection in real time. Farmers can access accurate position information and manipulate and analyze data to undertake a variety of tasks. Applications for auto-guidance systems include crop scouting, soil sampling, tractor guidance, field mapping, yield mapping, and more.
Additionally, remotely controlled autonomous agricultural vehicles feature machine learning based camera and vision systems to avoid obstacles, identify crops, and monitor operations. The farming community is also seeing the arrival of AI-based systems that can help identify diseases among crops, determine the nutrient content required to replenish fields, as well as identify and control weeds, like Solinftec's new Solix Sprayer robot.
Solinftec’s Solix Sprayer robot, a remote guided system, for weed spraying. Video used courtesy of Solinftec
Automation in Agriculture
Solinftec's expansion of its Solix Ag Robotics solutions, the new Solix Sprayer robot, not only helps farmers scan and monitor fields—as do the existing Solix Scouting robots—but manages and detects weeds.
The Solix Sprayer detects and sprays weeds, delivering the spray into and not onto the plant. Solinftec says that this method of weed management stops the herbicide from drifting onto crops, helps minimize harm to the environment, and reduces soil compaction brought on by the use of larger machinery. The Solix Sprayer, along with Solinftec’s other Solix Ag Robotics solution, work in unison with its ALICE AI platform. Data from the Solix Sprayer can be accessed through the digital agricultural platform, as can spot-spray maps, analyses, and actionable insights.
John Deere’s fully autonomous tractor released earlier this year. Image used courtesy of John Deere
Another more well-known example of remote-guided agricultural machinery is John Deere's fully autonomous tractor, unveiled earlier this year. The tractor combines the company’s TruSet-enabled chisel plow, 8R tractor, GPS guidance system, and has pairs of stereo cameras (totaling six in all) that provide 360-degree object detection and the ability to calculate distance. Images captured by the cameras are fed into a deep neural network (DNN) which classified individual pixels in roughly 100 milliseconds. The DNN enables the tractor to detect obstructions and work out whether to stop or resume operation.
Using the John Deere Operations Center Mobile, farmers need only take the tractor to a field, configure it, and swipe from left to right to activate the machine so it can get on with its work autonomously. The John Deere Operations Center Mobile also allows users to access live videos, metrics, images, and data. Farmers can adjust the tractor’s speed and view notifications concerning machine health problems. Such notifications can be helpful to farmers because it allows them to optimize the tractor’s performance as quickly as possible.
Food Supply Chain Automation
As the world population grows and concerns of the global food supply rise, automation in agriculture is looked to as a solution. With projected mark growth, automated robotic hardware and software systems aim to accommodate labor shortages, maintain the world’s food supply, and elevate the efficiency, productivity, and sustainability of farming.