by Darcy Cook and John Anderson
Shortline manufacturers know the numbers: The world population today is 7.5 billion—roughly double what it was in 1960. That number is projected to be 9.7 billion by 2050. This means farmers need to produce more with the same amount of land, and that puts pressure on the agriculture industry to make dramatic gains in food production.
Fortunately, we are experiencing a technological revolution that makes such a task less daunting.
GPS technology has facilitated precision agriculture techniques that led to greater efficiency through auto-steer, variable rate application, prescription application, overlap control, and more. Precision agriculture technology based on GPS has laid a foundation of precision control in the industry. The potential of the technology is vastly expanded with tools such as the Internet of Things (IoT), machine automation, and big-data technology.
Farmers typically are willing innovators of new technologies, especially when that technology is proven to bring gains in efficiency and is compatible with existing infrastructure. Because of that willingness, farmers today enjoy greater crop yields while reducing use of seed and fertilizer.
That willingness also helps position agriculture as the ideal industry to be the first major adopter of autonomous machines. There is a solid business case for increased profitability. The high cost of equipment and shortage of skilled machine operators makes autonomous systems an economic option. And, those three previously mentioned technological developments are now ready to come together to make autonomous farm machines a reality. Let’s look at each one.
IoT and Connectivity
IoT describes embedded electronics that have internet connectivity capability. The meteoric expansion of the internet, combined with cost-effective electronics that can be built into any device, have resulted in connected objects for a vast variety of purposes: refrigerators, fitness gadgets, industrial machines, cars, and more. IoT technology has resulted in a huge amount of real-time information provided by advanced sensor systems that can be used to improve business and operations.
In agriculture, this means the connectivity between moving machines such as tractors and implements and standing objects such as grain bins and dryers. By connecting the two, farmers have real-time, sensor-driven data about farm operations. The advancement of IoT has resulted in reliable, robust, and secure ways to share data in real time between machines and remote users, paving the way for remote operations and the ability to monitor autonomous machines.
(Note: Rural America faces a unique problem with internet connectivity. It is often not available on farms. The response to the problem has come from both local and federal resources, and we are moving quickly toward fully connected farms.)
Big Data (Data Analytics)
With the data now available through IoT technologies and advanced sensors, the emerging challenge is for farmers to get the most out of it. The millions of pieces of data available through live sensors is potentially enormously valuable, but that value diminishes if farmers have to analyze it in a spreadsheet. The next step is to automate the analysis of this raw data to drive decision-making on the farm.
Data analytics technologies have evolved rapidly in recent years. There are three main types of data analytics: descriptive analytics, predictive analytics, and prescriptive analytics. Each provides distinct value to the end user.
Descriptive analytics presents the data in a form that can be readily understood. This answers the “who,” “what,” “when,” and “where” questions. Examples include showing crop yields for each field on a farm, or tracking the location of grain in carts, bins, and trucks.
Predictive analytics, as the name suggests, forecasts what will happen. It supports farmers seeking tomitigate challenges and seize opportunities. Predictive analytics can be used to schedule preventive maintenance on machines based on operating data that anticipates a breakdown.
Prescriptive analytics pursues answers to the questions “why” and “how.” This is a deeper analysis that seeks to understand what’s driving certain circumstances and how to adapt. Agronomists use prescriptive analytics to develop detailed understandings of growing conditions, which helps them set application prescriptions of seed and fertilizer based on soil conditions.
Farm management systems have grown quickly to meet the demand for data analytics in agriculture, but the market has been fragmented. Tractor manufacturers have developed connected farm solutions, but most of them are compatible only with a specific brand. GPS companies have provided special-function options for precision farming, but they work only with GPS-enabled devices. A third group of companies has focused on overall farm management solutions independent of specific equipment. The challenge facing this group has been gaining access to machine information, specifically information from implements, which are critically important to data collection.
While none of these systems provides the complete and integrated solution farmers need, the openness of the third group—the farm management solutions—presents an opportunity. Farmers want to link together third-party systems, and they need a way to combine the data analytics generated by the control systems that drive their machine functionality with the farm management software. This has resulted in standards such as the ISOBUS FMIS and Task Controller, but the missing piece is the software and equipment databases, like JCA’s Cumulus Farm API, that links it together.
It is essential that these various system components work together. Only then can we achieve the complete autonomous farm.
Across industries, highly automated machines are growing in number. The automotive industry has contributed to the growth with a build-up of technology such as vision systems, object detection, and autonomous driving technology. These tools are proven, robust and ready to be applied in agriculture.
What farmers need, however, cannot simply be replicated from the auto industry. Autonomous farm equipment technology requires not only movement but also automation of the implement function, such as seeding, spraying, and harvesting. This requires integrating precision ag functions based on GPS systems with other automated systems, and even automating movements that today are operator-controlled. The industry has arrived at a moment in its history in which the technology is there to support autonomous equipment.
A Vision of The Future
As production of autonomous machines gains traction, the cost will drop, and small machines will make more economic sense than large ones, since machines will no longer be limited by available operators. These smaller autonomous machines can provide more flexibility.
Farms of the not-too-distant future will consist of more (but smaller) machines that run as directed by the farmer. Farmers’ decisions will be based on real-time data and analytic tools that transform the data into digestible information about the state of the farm. The farmer’s role will shift, and the farm will operate with a new level of efficiency.
Past decades have focused on the advancement of the tractor, but with the build-up of technology toward autonomous vehicles, and the continued focus on precision agriculture, the focus shifts now to the advancement of implements. These developments bring a radical shift and call into question the future of the tractor.
For roughly the past century, the tractor has been a key part of farming operations, and its existence has been taken for granted. It has provided not only the horsepower needed to pull implements through the field and supply those implements with adequate hydraulic, electrical, and in some cases, pneumatic services, but it also has provided a home for the operator. As a result, implement manufacturers have had to make their equipment work within the controls, displays, connections, and power provided by the tractor. Autonomous technology changes this dependency.
Autonomous vehicles can work around the clock, and a network of vehicles can get the job done concurrently without additional labor. This means that vehicles can be smaller, which reduces the reliance on high horsepower and eliminates the need for a machine operator. So why is there a need for a tractor? Function-specific agricultural machines can be developed that will include their own smaller drivetrains at a lower system cost.
The Path to Autonomy for Equipment Manufacturers
If manufacturers of agricultural equipment are not nervous about where they fit in the emerging technology landscape, they are not paying attention. Implement manufacturers have been building more smart technology into the machines they manufacture, which provides more precision control, and real-time data increases farm efficiencies. Equipment is evolving with more automation, hence the capacity to coordinate complex operations: more sensors to monitor all aspects of the machine operation, and greater connectivity, control, and analysis.
The path toward autonomous systems will happen naturally by adding intelligence to the machine in value-added steps. Implement manufacturers need to be diligent in identifying ways these technologies can be added to the machines they build to add immediate value to farmers. They will likely need technology partners like JCA Electronics to help them navigate this way forward. The technology will move forward quickly, because its value is indisputable.
Darcy Cook is VP of engineering and general manager at JCA Electronics, and John Anderson is president of JCA Electronics, a member company that offers expertise in advanced control systems and autonomous technology, as well as electronics and wire harness manufacturing for the farm equipment industry.