Precision Farming


Imagine you are a farmer riding along in your 50,000-acre wheat field early in the growing season. You push a button on your tractor to turn on its Global Positioning System (GPS) monitor, which pinpoints your exact location to within one meter. Touching another button, you display a series of Geographical Information System (GIS) maps that show where the soil in your field is moist, where the soil eroded over the winter, and where there are factors within the soil that limit crop growth. Next, you upload remote sensing data, collected just yesterday, that shows where your budding new crop is already thriving and areas where it isn’t. You hit SEND to upload these data into an onboard machine that automatically regulates the application of fertilizer and pesticides—just the right amount and exactly where the chemicals are needed. You sit back and enjoy the ride, saving money as the machines do most of the work. Congratulations, you are among a new generation of growers called "precision farmers."

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Satellite Image of Farmland


Does this sound like a science fiction scenario? It’s not. Even as you read this, there are already dozens of farmers around the United States and Canada who use satellite and aircraft remote sensing data to more effectively and efficiently manage their croplands.

"Precision crop management is still in the experimental phase," states Susan Moran, a research hydrologist with the U.S. Department of Agriculture and member of the NASA Landsat 7 Science Team, based in Tucson, Arizona. "But there is a significant number of farmers who use high technology and remote sensing data for precision crop management."

The U.S. Department of Agriculture, NASA, and NOAA are among key agencies contributing to this revolution in large-scale agriculture. The goal is to improve farmers’ profits and harvest yields while reducing the negative impacts of farming on the environment that come from over-application of chemicals.

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A new generation of farmers is using aerial and satellite remote sensing imagery (like this 4-meter resolution image from IKONOS) to help them more efficiently manage their croplands. By measuring precisely the way their fields reflect and emit energy at visible and infrared wavelengths, precision farmers can monitor a wide range of variables that affect their crops—such as soil moisture, surface temperature, photosynthetic activity, and weed or pest infestations. (Image copyright 2000 Space Imaging)

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Moran explains that a farmer, like any business person, needs good and timely information to succeed. To formulate an effective growing strategy, a farmer needs to know three things: (1) which conditions are relatively stable during the growing season; (2) which conditions change continually throughout the growing season; and (3) information to diagnose why their crop is thriving in some parts of the field and struggling, or even dying, in other parts. Increasingly, large-scale growers are using information gathered by aircraft and satellite-based remote sensors to help them gather these types of information.

Early detection of any change in growing conditions is the key to good crop management. While there is no match for a farmer’s own firsthand observations, it isn’t always possible for large-scale growers to survey all of their lands every week. In addition to watching out for pests, such as weeds and insects, farmers must also monitor variables like soil moisture and even plant disease outbreaks.


Near Infrared Image

A number of scientific studies over the last 25 years have shown that measurements in visible, near-infrared, thermal infrared, and microwave wavelengths of light can indicate when crops are under stress (Moran 2000). Using satellite- and aircraft-based remote sensors to precisely measure the wavelengths of radiant energy that are absorbed and reflected from the land surface, scientists can diagnose a wide range of growing conditions. For instance, these data can tell farmers where their crop is thriving and how efficiently the plants are photosynthesizing. Alternatively, remote sensing data can tell not only where, but why, their crop is under stress and help them diagnose the source.


This false-color composite image of the Maricopa Agricultural Center, a research farm in central Arizona, was acquired in June 1996 by the Daedalus sensor flying aboard a NASA aircraft. The vegetation growing in the fields is mostly cotton and alfalfa. Bare fields appear blue, while soils that have recently been leveled in preparation for planting appear white. Dark red areas show flood irrigation (through the crop canopy), which can be seen in the eastern part of field 31. (Image courtesy Susan Moran, Landsat 7 Science Team)


Thermal Infrared Image

Satellites and aircraft have the added advantage of allowing farmers to survey their entire land in mere minutes. "In the early days, when farmers had small fields, they knew from practical experience which sub-areas were wetter and more fertile," notes Craig Daughtry, a research physical scientist at the U.S. Department of Agriculture in Beltsville, MD. "But as farms have grown from a few hundred acres to [as much as] 50,000 acres, farmers start to lose touch with their fields. Remote sensing provides a fabulous tool for looking at changes on small scales of space and time."

Once farmers can correctly diagnose exactly where and when there is a problem, the next step is to correctly apply the treatment.

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This false-color image shows the same scene as the image above, but it represents measurements made at thermal infrared wavelengths of the spectrum, to represent surface temperatures. Cool temperatures (blue and green) are associated with vegetation and hot temperatures (yellow and red) with bare soil. (Image courtesy Susan Moran, Landsat 7 Science Team)

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According to Moran, the term "precision farming" refers to the use of an information and technology-based system for within-field management of crops. "It basically means adding the right amount of treatment at the right time and the right location within a field—that’s the precision part," Moran explains. "Farmers want to know the right amounts of water, chemicals, pesticides, and herbicides they should use as well as precisely where and when to apply them."

Critical to the success of precision farming is the sophisticated new equipment that is now commercially available. Called "variable rate technologies," there are devices that can be mounted on tractors and programmed to control the dispersion of water and chemicals based upon the information gained from the remote sensors.

Thanks to the marriage of remote sensing data with GIS and GPS software tools, and on-tractor variable rate technologies, farmers no longer must treat a field of crops as one homogeneous unit. Charles Walthall, also a research physical scientist at the U.S. Department of Agriculture in Beltsville, recalls that the old way of doing business was planting a crop and then applying fertilizer evenly across the whole field.

"But now we’re characterizing zones within the field so we can optimize what inputs are needed to go into that zone according to what they need to produce the crop," Walthall says. "But if you limit your inputs—such as fertilizers, seeds, water, pesticides, or herbicides—to precisely where and how much is needed, you are putting less on the landscape. So the cost is less and energy is saved, which means better profit."


Tractor Mounted Imager
Critical to precision farming is the sophisticated new equipment that is now commercially available. The photo above shows a tractor with an imaging system attached on a track to obtain spectral measurements within a field. These measurements provide information about the health of a crop. [Photograph courtesy United States Department of Agriculture (USDA) Agricultural Research Service]


New Pesticide Applicators

Perhaps more significantly, it can mean there is much less chemical runoff from farms to negatively impact the environment. According to Walthall, state-sponsored agencies are passing laws limiting the types and amounts of chemicals that farmers can use. The State of Maryland, for instance, passed a law requiring farmers to have a documented "phosphorus management plan." (This law is particularly enforced in the Chesapeake Bay region where the runoff of phosphates into the bay can contribute to harmful algae blooms and other negative environmental impacts.) Other states are considering similar laws aimed at regulating the use of nitrogen. Too much nitrogen in the water supply is a health hazard to both humans and animals.

But by using the tools of precision farming, growers can specifically target areas of need within their fields and apply just the right amounts of chemicals where and when they are needed, saving both time and money and minimizing their impact on the environment.

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Ultra-low volume herbicide application methods (developed by Agricultural Research Service plant physiologist Chester McWhorter and colleagues) combined with new techniques to determine where pesticides are needed could significantly reduce the use of agricultural chemicals. (Photograph by Keith Weller, courtesy USDA Agricultural Research Service)

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Ironically, Earth’s most abundant resource—water—has become one of the most precious resources in the United States as rivers, lakes, and freshwater reservoirs are increasingly exploited for human use. Consequently, using precision farming techniques to refine "irrigation scheduling" is a research area of particular interest to Moran. She explains that in the southwest, irrigation is both difficult and expensive. There, she says, farmers have a tendency to over-irrigate, spending both more time and money than is necessary.

"I'm trying to provide new information that could be used by farmers to schedule irrigations to improve their profitability and use less water," Moran says. "Often times, farmers look at weather variables and then schedule irrigation based on that information. But if they had better information, they could use scientific models and equations to compute more precisely how much water their crop is using."

Rather than guessing their crop’s potential need for water based upon weather variables, farmers can use remote sensors to measure how much water their crop is actually using. This would give them a more accurate measure of how much more water it needs.


Irrigation System
This photo shows a movable linear irrigation system with an imaging system attached. Such devices can be programmed, based on remote sensing data, to provide the right amount of water at the right time to different areas within a field. (Photograph courtesy USDA Agricultural Research Service)


Crop Monitoring

The basic concept is that as a plant grows, it takes in sunlight, nutrients from the soil, and water to build plant structures during photosynthesis. Some of the incoming sunlight is reflected, while some is absorbed and either used for photosynthesis or converted into heat. Similar to the way humans perspire to cool off, healthy plants use a process called "transpiration" to keep cool. There are tiny pores on plants’ leaves, called "stomata," that can open to allow water droplets to evaporate, thereby releasing heat. But a plant that is under stress does not transpire well and begins to overheat. At a certain temperature threshold, the plant’s internal functions begin to break down. The plant begins to whither and change its texture or shape or color, or all of the above, and there is potential for damage. Remote sensors can measure the temperature of plants; or to be more precise, they can measure how much energy plants emit at thermal infrared wavelengths of the spectrum.

Moran explains that her computer model takes a given plant’s physical attributes into consideration when making its calculations. As they are gathered, new remote sensing data are also input into the model to indicate which variables–such as air temperature or soil moisture–are changing over time and by how much. She then uses mathematical formulas to relate the plants’ temperature to the surrounding air temperature and calculate how much water the plant is using. The model’s output value falls somewhere on an index scale of from 0 (meaning no stress) to 1 (serious stress and the crop is probably damaged). Corn, for example, could go as high as 0.4 on the crop water stress index and still produce a harvest, whereas cotton has a much lower stress threshold.

Moran concludes that if farmers are getting good and timely measurements of plant and air temperature, then they can program when and how much water to give each crop through an irrigation system. No more water would be used than needed, thus saving cost and conserving water.

Moran cites one study she conducted in Arizona to investigate the use of remote sensing data for scheduling cotton irrigations. Typically, those farmers irrigate ten times per growing season, but evidence showed that some of those farmers could achieve basically the same harvest with only nine irrigations.

"In those cases, one less irrigation saved more than all the cost of remote sensing data," she states. "Both [irrigation and satellite remote sensing data] are expensive. But then again many farmers are used to working together as a group. They are used to sharing. I’m hoping they could do the same with remote sensing data–purchase one scene over a large area to cover many farms, which would further offset the cost."

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These three false-color images over the Maricopa Agricultural Center were acquired by the Daedalus sensor flying aboard a NASA aircraft. The top image shows the color variations determined by crop density (also referred to as "Normalized Difference Vegetation Index," or NDVI), where dark blues and greens indicate lush vegetation and reds show areas of bare soil. The middle image is a map of water deficit, derived from the Daedalus' reflectance and temperature measurements. The image shows an ongoing flood irrigation in the northern Field 7 and Field 107, in which greens and blues indicate wet, bare soil and reds are dry, bare soil. The bottom image shows where crops are under serious stress, as is particularly the case in Fields 120 and 199 (indicated by red and yellow pixels). These fields were due to be irrigated the following day. (Image courtesy Susan Moran, Landsat 7 Science Team)

  Data Requirements for Precision Farming   Page 1

According to Moran, an optimum remote sensing system for precision farming would provide data as often as twice per week for irrigation scheduling and once every two weeks for general crop damage detection. The spatial resolution of the data should be as high as 2 to 5 square meters per pixel with positional accuracy of within 2 meters. Additionally, the data must be available to the farmer within 24 hours of acquiring them.

Turnaround time, she says, is more important to farmers than data accuracy. They would gladly accept remote sensing measurements that are as poor as 75 percent accurate if they were assured of getting them within 24 hours of acquisition. Unfortunately, says Moran, there are currently no Earth orbiting satellites that can meet all of a precision farmer’s requirements. But she is optimistic that this will change within the next 10 years as exciting new satellite remote sensing technologies emerge. Until that time, there are still a variety of sources of remote sensing data that farmers can use.


Maricopa Agricultural Center

For instance, the Enhanced Thematic Mapper Plus (ETM+) aboard Landsat 7 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard Terra provide good spatial and spectral resolution (up to 15 meters per pixel) as well as good scientific calibration accuracy. But used by themselves these satellites’ revisit cycles are too infrequent to be of use to precision farmers. Moran is also interested in the new Advanced Land Imager (ALI) and the hyperspectral Hyperion sensor aboard the newly launched EO-1. But none of these satellite sensors have the desired temporal resolution, as they "see" a given patch of ground only once every 16 days, on average. The Moderate resolution Imaging Spectroradiometer (MODIS), aboard Terra, sees a given patch of ground almost every day (high temporal resolution), but its highest spatial resolution is only 250 meters per pixel–too coarse for precision farming but perfect for regional to global-scale research.

Moran points out that precision farmers may use a combination of satellite and airborne remote sensing data to meet their needs. There are a growing number of commercial companies that serve their local regions by flying remote sensors aboard aircraft that collect data in visible and near-infrared channels at spatial resolution ranging from 0.3 to 1 meter per pixel and have turnaround times of less than 24 hours. For instance, it may be possible to purchase commercial overflights periodically during the growing season to fill in the gaps between acquisitions of satellite remote sensing data.

Moran uses a computer model to integrate the various data into a simulation of the growing conditions in the field. The model allows her to overcome some of the spatial and temporal limitations in the Landsat 7 data by interpolating or predicting the changes that occur over time and space that the satellite cannot see.

Ultimately, where does Moran see all this new precision farming technology heading? She envisions a day when commercial companies can serve farmers with new scientific tools that she calls "decision support systems." In much the same way banks and brokerages provide financial advice and accounting services to business persons, there will be companies using decision support systems to routinely map field boundaries as well as weed, pest, or disease outbreaks. These companies will track when a farmer plants, waters, and applies fertilizers or other chemicals. Based upon all this information, the companies will help the farmers develop good precision management strategies throughout the year to maximize their harvest yields while saving them time and money.

"In a dream world, this is what should be happening," Moran muses. "That would be the entire precision agriculture application in a nutshell. We don’t yet have such a system. But do we have the technology to build one? Yes!"

  • References
  • Moran, M.S. and J. Irons, 2000: "New imaging sensor technologies suitable for agricultural management," Intl. J. Rem. Sens. (submitted).
  • Moran, M.S., 2000: "Technology and techniques for remote sensing in agriculture," Assoc. Appl. Biol. and Rem. Sens. Soc. Conf. on Remote Sensing in Agriculture; June 26-28, Cirencester, England; p. 1-10.
  • Moran, M. Susan, 2000: "Image-Based Remote Sensing for Precision Crop Management–A Status Report." Proceedings of the Conference American Society of Civil Engineers; Feb. 27-March 2, pp. 185-93.
  • Moran, M. Susan, 2000: "Image-Based Remote Sensing for Agricultural Management–Perspectives of Image Providers, Research Scientists, and Users." Proceedings of the 2nd International Conference on Geospatial Information in Agriculture and Forestry; Jan. 10-12.
  • Moran, M. Susan, Y. Inoue, and E.M. Barnes, 1997: "Opportunities and Limitations for Image-Based Remote Sensing in Precision Crop Management." Remote Sensing of the Environment, vol. 61, pp. 319-46.

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This false-color image was acquired by Landsat 7's Enhanced Thematic Mapper Plus (ETM+) in August 1999 over the Maricopa Agricultural Center in central Arizona. Bright red areas are irrigated fields. According to Susan Moran, although most satellite sensors currently lack the desired spatial and temporal resolution, they may still prove to be valuable information resources for precision farmers as these data are incorporated into better computer models that allow farmers to interpolate how growing conditions change over time and space. (Image courtesy Susan Moran, Landsat 7 Science Team)