Precision ag holds promise, but limitations persist

“Robotics is huge,” said Curtis Drozd, a third-generation corn, soybean and sorghum farmer in the southwestern Michigan town of Allegan. Drozd has invested in unmanned aerial vehicles, or UAVs, and GPS-integrated auto-steering computers on his tractors. Lest you think Drozd’s auto-steer system is rugged and reliable enough to allow his tractor to autonomously start itself in the barn, drive to the field, and complete plowing or planting all while he attends to other tasks on the farm, Drozd is quick to clarify: We still need the farmer.

“The computer does about 80 percent of the work, but it’s the other 20 percent that is the challenge,” Drozd said. Even with precision technology in place, skill and experience is necessary to navigate across rolling sandy knobs in the terrain or across persistently soft and wet heavy clay spots in the field where only an experienced driver at the controls will prevent the tractor from getting stuck. The maneuvers at the end of the row especially, the required turning of the tractor with its plows, planters, discs, chemical sprayers following in-line and train-like are “critical during planting operations … getting everything turned around is pretty tricky … the computer helps, but it doesn’t do everything,” he said.

As we look into the future for autonomous, driverless tractors it is clear that everything is okay, until it’s not – which is why the driver is still required. More precision data and refinements are needed in autonomous farm vehicles to identify and overcome the 20 percent of field-work processes that require human thought. Still, agribotics and precision agriculture offer other exciting possibilities.

Analyzing the crop from above
The farmer continuously needs to know the condition of crops in the field, but not just what’s visible from the road or from standing on the roof-top of a pick-up truck. The farmer needs to be able to see the whole crop, and therein lies the problem that UAVs set out to solve.

“UAVs help the farmer ‘find the needle in the cornfield,’” said Jim Love, light robotics manager at Beck Hybrids, Atlanta, Ind. “The field monitor, the farmer, nobody wants to walk the cornfields after the corn is waist high or taller.”

Drozd concurs.

Farmers without UAVs (often called drones) would, if they knew a local airplane pilot, pay for the gas to fly over the fields to monitor and assess the condition of the crop, Drozd said. They would have to rely on the memory of what they saw or a few photographs taken through the plane’s window, he added. It’s a low tech assessment.

Enter the UAV. Outfitted with specific cameras, the UAV can be flown over the fields while capturing visual reflectance data or thermal imaging. Colorimetric analysis of the images plotted and visualized by computer programs can reveal the exact area of the field and the need for localized water, nutrient, pesticide or herbicide applications. It is also a valuable tool for calculating the exact area of crop loss due to storms. In addition, thermal imaging technology can help distinguish areas of the field in which the crop is stressed. Plants that are healthy produce a different thermal image than stressed plants.

The more farmers know, the better their ability to make quality management decisions. Technology and precision agriculture and the big data it collects means return on investment is no longer simple to determine; it relies on more factors, not less.

There are a substantial number of UAV imaging technologies ready and available but future multi-spectral technologies will open the door to more sophisticated diagnostic tools. The future will not only replace the traditional field monitor observing that something is wrong; but will combine detailed mapping of where it is wrong, what is wrong, and provide recommendations on how to respond.

Such technology introduces conflict. “Nobody is going to tell my grandfather anything about corn,” Drozd said.

Love admits that a farmer’s long understanding of his crops and his fields and resulting expertise is still in the mix for making management decisions on the farm. Yet the future is here. Millennial farmers are becoming more dependent on data and technology to make decisions on the farm, Love said.

Brian Luck, who has a doctoral degree in agricultural and biological engineering and is a professor in the Biological Systems Engineering department at the University of Wisconsin-Madison, said that the aggregation of big data is a trending opportunity in agriculture. Luck called this “precision agriculture,” and it is anticipated to play heavily into the future of agriculture. The idea is for researchers and farmers to identify as many of the variables about plant growth patterns, field conditions, weather, nutrients, water use – as many factors as can be identified, to design sensors for taking measurements, transmit that data to a computer, then apply sophisticated statistical and algorithmic programs to determine the exact factors that impact growth, yield and ROI. The data “will finally provide farmers with unbiased information on which to make management decisions,” Luck said.

Advances in precision agriculture are translating into a data-driven hybrid selection and multi-variety planting based on the analysis of field conditions, i.e. coordinates are provided by the computer to the planter to automatically switch between any of two to four different hybrid seed types based on historical field profiles. The goal, of course, is yield improvement and its subsequent impact on ROI.

Opportunities revealed by precision agriculture, UAVs and robots are not limited to traditional field crops across the upper plains states, but extend into specialty crops as well. (See article on opposite page.) “UAV technology is just now being introduced into Wisconsin cranberry operations in order to detect insect infestations,” Luck said.

Additionally, Luck said work being done at the University of Iowa is developing small scale leaf sensors that are placed directly on the leaves of the plants.

Information about temperature, sunlight, UV-light, moisture and transpiration (all factors of plant metabolism) can be gathered real-time from individual plants and sent to databases where algorithms in computer analysis programs are continuously making recommendations to help the farmer with management decisions. In this way, precision agriculture and new technology can support efforts on the farm to benefit the environment. According to Luck, market introduction of this technology is “probably 10 years down the road.”

Luck went on to say that data aggregation, big data, is an essential component of these analyses. The more data that is collected, the more information that can be analyzed to reveal nuances about the crop, the fields and the farm. The challenge to this scenario is that “farming is highly competitive, especially with respect to yields, and farmers are traditionally reluctant to share data,” Luck said. Farmers are wary about how data obtained on their farm will be used in the future. Yet field conditions, crop conditions, all contribute to yield, and yield is both reputation and ROI.

This article is presented through a sponsorship from FarmerMac.