|
 Predicting the amount of nitrogen needed by a particular crop in
a particular year is tricky. The first step is understanding
crop nitrogen status in real time, but it’s neither realistic
nor scalable to measure leaf nitrogen by hand throughout the
course of a season.
In a first-of-its-kind study, a University of Illinois research
team put hyperspectral sensors on planes to quickly and
accurately detect nitrogen status and photosynthetic capacity in
corn.
“Field nitrogen measurements are very time- and labor-consuming,
but the airplane hyperspectral sensing technique allows us to
scan the fields very fast, at a few seconds per acre. It also
provides much higher spectral and spatial resolution than
similar studies using satellite imagery,” says Sheng Wang,
research assistant professor in the Agroecosystem Sustainability
Center (ASC) and the Department of Natural Resources and
Environmental Sciences (NRES) at U of I. Wang is lead author on
the study.
“Our approach fills a gap between field measurements and
satellites and provides a cost-effective and highly accurate
approach to crop nitrogen management in sustainable precision
agriculture,” he adds.
The plane, fitted with a top-of-the-line sensor capable of
detecting wavelengths in the visible and near infrared spectrum
(400-2400 nanometers), flew over an experimental field in
Illinois three times during the 2019 growing season. The
researchers also took in-field leaf and canopy measurements as
ground-truth data for comparison with sensor data.
The flights detected leaf and canopy nitrogen characteristics,
including several related to photosynthetic capacity and grain
yield, with up to 85% accuracy.
“That’s close to ground-truth quality,” says Kaiyu Guan,
co-author on the study, founding director of the ASC, and
associate professor in NRES. “We can even rely on the airborne
hyperspectral sensors to replace ground-truth collection without
sacrificing much accuracy. Meanwhile, airborne sensors allow us
to cover much larger areas at low cost.”
Remote sensing picks up energy reflected from surfaces on the
ground. The chemical composition of leaves, including their
nitrogen and chlorophyll content, subtly changes how much energy
is reflected. Hyperspectral sensors detect differences of just 3
to 5 nanometers across their entire range, a sensitivity
unmatched by other remote sensing technologies.
“Other airborne remote sensing technologies pick
up the visible spectrum and possibly near-infrared, just four
spectral bands. That’s not even close to what we can do with
this hyperspectral sensor. It’s really powerful,” Guan says.
The researchers see a use for their findings in the popular
Maximum Return To Nitrogen (MRTN) corn nitrogen rate calculator.
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Wang explains, “Under our approach, we can detect the
nitrogen status of the crop and make some real-time adjustments for
the agricultural stakeholders. MRTN provides recommended nitrogen
fertilization rates based on the economic tradeoff between soil
nitrogen fertilizer rates and end-of-season yield. Our
remote-sensing approach can feed plant nutrient status into the MRTN
system, enabling real-time crop nitrogen management. It can
potentially shift the current recommendations based on pre-growing
season, soil-centric fertilization to a diagnosis based on real-time
plant nutrition, improving agroecosystem nitrogen use efficiency.”
Importantly, the research team worked out the best mathematical
algorithm to detect nitrogen reflectance data from the hyperspectral
sensor. They expect it will be put to use as newer technologies come
on board.
“NASA is planning a new satellite hyperspectral mission, as are
other commercial satellite companies. Our study can potentially
provide the algorithm for those missions because we already
demonstrated its accuracy in the aircraft hyperspectral data,” Wang
says.
Guan says bringing this technology to satellites is the end goal,
enabling a view of every field's nitrogen status early in the
growing season. The advancement will allow farmers to make more
informed decisions about nitrogen side-dressing.
Ultimately, of course, the goal is to improve the environmental
sustainability of nitrogen fertilizers in agronomic systems. And
Guan says precision is the way to get there.
“Essentially, you can't manage what you can't measure. That is why
we put so much effort into this technology.”
The article, “Airborne hyperspectral imaging of nitrogen deficiency
on crop traits and yield of maize by machine learning and radiative
transfer modeling,” is published in the International Journal of
Applied Earth Observation and Geoinformation [DOI:
10.1016/j.jag.2021.102617]. This research was supported by the U.S.
Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E)
SMARTFARM (MBC Lab and SYMFONI) projects.
The Department of Natural Resources and Environmental Sciences is in
the College of Agricultural, Consumer and Environmental Sciences
(ACES) at the University of Illinois Urbana-Champaign.
The Agroecosystem Sustainability Center is jointly established by
the Institute for Sustainability, Energy and Environment (iSEE), the
College of ACES, and the Office of the Vice Chancellor for Research
and Innovation (OVCRI) at Illinois.
[Sources: Sheng Wang, Kaiyu Guan,
News writer: Lauren Quinn] |
