February 2015
Eyes In the Sky By Shelley Jansky, an associate professor in the Department of Horticulture at the University of Wisconsin-Madison and a research geneticist at the USDA Agricultural Research Service in Madison, Wisconsin.

Drones are in the headlines. Rarely a week goes by without a story about these remarkable flying machines and their potential applications, from delivering packages and air strikes to surveying real estate and agricultural fields. Since the word drone” is often used in conjunction with military operations, this article will use the term unmanned aerial vehicle (UAV) instead.

How can a UAV improve the way we conduct agricultural research? The answer appears to be limited only by our own imagination. The most basic application is to monitor canopy development using a visible light camera. It is not practical to take extensive notes on every plot of every trial repeatedly throughout the growing season. However, it takes only minutes for a UAV to fly over a field and shoot photos of hundreds of plots, which can be archived and reviewed as needed. In fact, the aerial images provide a permanent record of each field plot. This allows us to go back and revisit a field while we are analyzing data collected later in the season. For example, a plot with a low yield in one replication may be found, in retrospect, to have been stressed during a short but critical time in the growing season.

Thinking beyond in-season comparisons, we can use images from UAVs to compare the growth of plants across years. We can compare canopy growth across many years and relate it to variables such as weather conditions and agronomic practices. Images taken above the crop canopy will likely uncover information about the history of a field that is not apparent on the ground. Decades ago, aerial photos taken by aircraft at our research station revealed differences in plant growth that could be tracked to rotation history. We can now follow this effect regularly and inexpensively using a UAV. The information can be used to tease out confounding factors that affect our field trials.

Aerial images provide an unbiased look at our research fields. Every year, I carry out a verticillium wilt trial in which varieties are grown on both fumigated and inoculated fields. I walk through these fields and score each plot for disease and vigor. Despite my best efforts to be consistent and unbiased, I know that the score I assign to a plot may be influenced by my view of surrounding plots. Consequently, a high vigor score in the inoculated field may not be the same as that in the fumigated field. However, a comparison of aerial images from the two fields is unbiased, especially if digital imaging software is used to measure vine size (Figure 1). Similarly, I can envision researchers comparing aerial images from regional trials such as those carried out by breeders evaluating potential new cultivars.

We are moving into applications that extend beyond simple measures of vine growth. Of course, flower initiation and the length of the flowering period, which are associated with maturity, can be recorded with digital images. Vine color data can aid in our scoring of maturity and the incidence of diseases such as verticillium wilt and early blight.

Images from a high-resolution camera can actually reveal stresses at the leaf level. The next step is to fly an infrared camera over the field to monitor biotic and abiotic stresses. Compared to normal plants, those with compromised vascular function are expected to have warmer leaves due to a reduction in evaporative cooling. The payload on a UAV does not need to be limited to cameras. We have flown petri dishes over our fields and captured fungal spores. Insects could be similarly sampled.

This is an exciting time to be doing agricultural research. Many powerful tools, including digital cameras, massive data storage, and imaging software are evolving along with UAVs. These complementary technologies work together to accelerate our progress toward the development of new strategies for improving agricultural production.

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