remote sensing

How Remote Sensing Powers Precision Agriculture

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Editor’s Note: Joseph Byrum is senior R&D and strategic marketing executive in Life Sciences – Global Product Development, Innovation, and Delivery at Syngenta. He is a regular contributor to AgFunderNews. Read his other articles here.


Growers today can take advantage of the latest in technology to help them maintain an edge in an increasingly competitive marketplace. That means securing the benefits of the information revolution to deliver advances in crop yield and farm efficiency from precision growing techniques enabled by data – lots of data.

Those benefits, of course, are only going to be as good as the underlying data provided by sensors, the devices that obtain information about an area — a field — often from a distance in what’s called remote sensing.

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Joseph Byrum

This information is collected, compiled and turned into actionable intelligence through the use of advanced data analytics. Think of the navigation app on a smartphone. At heart, it is a data analytics tool that runs through all of the possible routes to between your current location and your destination, estimating how long it will take to take one road compared to the next. Yet these apps are only as good as the traffic data that let the system know one road is closed and another is clogged with traffic. The job of sensors is to provide that data.

Origins of Remote Sensing

Remote sensing may be a relatively new thing to agriculture, but the concept has been around for quite some time. Back in the mid-19th century, man took to the skies in hot air balloons with bulky, primitive cameras they used to survey the land below and create highly accurate maps. World War I commanders soon came to rely on photographs taken from biplanes and blimps to stay informed about the enemy’s battlefield movements and to plan artillery strikes. These commanders understood that, if you can’t see it, you can’t manage it.

Over the ensuing decades, technology advanced rapidly, but this principle remained the same. Cameras became far less cumbersome, and aircraft became far more capable. The “bird’s eye view” provided by these systems became exponentially more effective in managing tasks as diverse as construction, mining, and archeology. In the 1960s, surveying was boosted into orbit as satellites gave us, for the first time, a look at the entire planet at a single glance, which provided insight into managing big picture issues from global temperatures to land use patterns.

The Potential for Remote Sensing in Agriculture

Modern sensing instruments have advanced far beyond simple photographic film. Today’s devices measure light, radiation, and heat by capturing different wavelengths of the electromagnetic spectrum. Ongoing electronics miniaturization and the popularity of commercial drones have made this equipment increasingly affordable, but the usefulness of these devices in agriculture took the greatest leap with the advent of GIS (geographical information systems), the technology that allows the organization and analysis of data and patterns related to specific locations on a map — such as a field. This made it far easier for combined systems to deliver information — actionable intelligence — related to a grower’s specific needs.

Remote sensing devices take measurements throughout a field over time so that the grower can analyze conditions based on the data and take action that will have a positive influence on the harvest outcome. For instance, sensors can serve as an early warning system allowing a grower to intervene, early on, to counter disease before it has had a chance to spread widely. They can also perform a simple plant count, evaluate plant health, estimate yield, assess crop loss, manage irrigation, detect weeds, identify crop stress and map a field.

A variety of sensors is available to perform one or more of these tasks. Which one will a grower need? It all depends. A small-scale vegetable farmer will have different needs than a commercial grain farmer managing multiple fields.

Sensor Platforms

Sensors can be grouped according to their enabling technology — ground sensors, aerial sensors and satellite sensors. Ground sensors are handheld, mounted on tractors and combines, or free-standing in a field. Common uses for these include evaluating nutrient levels for more specific chemical and nutrient application, measuring weather, or the moisture content of the soil.

Aerial sensors have become far more affordable with the advent of drone technology that places the bird’s-eye view of a field within reach of most farmers. They are also attached to airplanes, another relatively cheap option. The systems are capable of capturing high-resolution images and data slowly enough, at low altitude, to enable thorough analysis. Typical uses include plant population count, weed detection, yield estimates, measuring chlorophyll content and evaluating soil salinity. The downside of aerial platforms is that wind and cloud cover can limit their use.

Satellite sensors provide coverage of vast land areas and are especially useful for monitoring crops status, calculating losses from severe weather events and conducting yield assessments. Initially, such systems were tailored to the needs of the military and government, not agriculture. So the main downside, aside from cost, was that these systems were tasked in advance — usually months — to look at a specific area at a certain time. Worst of all, cloud cover could ruin that expensive purchase. Now many governments have opened up satellite imaging databases to the public, providing an important and accessible resource for understanding crop conditions.

The Sensors Themselves

As with the choice of platform, appropriate sensor types will vary from farm-to-farm. A grower must ask what he intends to measure, and why, and which sensor type is best suited to the crop management and planning task at hand. In the past, there wasn’t a choice. The only sensor was camera film that captured a narrow slice of the electromagnetic spectrum — visible light. Now sensors go far beyond that, measuring short-wavelength gamma radiation at one end and low-frequency radio waves at the other.

Farmers find the most useful information closer to the visible spectrum, as color can be used to measure a plant’s chlorophyll levels and provide insight into a plant’s health and growth status. Simple red-green-blue sensors can provide color information, but more sophisticated data are available by peering into the near-infrared and short-wave infrared spectral bands.

The way leaves reflect light in the infrared spectrum changes if a plant’s cell structure is damaged, or if its water content is abnormal. The most consistent mathematical model to express this is called the normalized difference vegetation index, or NDVI. With near-infrared and red-edge (NIR and RE) sensors, NDVI can identify stressed crops much more precisely, giving the farmer more time to take corrective action.

The same sensors can also be used to identify a soil salinity problem over time, which can be a sign of poor irrigation that threatens crop yields. Salty soil has a higher reflectance than normal soil, a difference that shows up in the infrared spectrum and on thermal cameras.

Thermal cameras peer into long-wavelength infrared bands and measure heat, often represented as colors. So a farmer, shortly after irrigating his field, can send a drone equipped with a LWIR sensor and readily see, from the colors on the map, areas in the field that aren’t receiving enough water. This helps get the irrigation right from the beginning, preventing yield loss.

Radar and microwave sensors on satellites cut through weather conditions and provide powerful agricultural monitoring data across entire continents. Some sensors are designed to capture broad swaths of the electromagnetic spectrum, while others are tailored to measuring narrow slices that are relevant to a specific type of analysis. Which one is right for the grower is going to depend on what the grower intends to accomplish.

Outside the electromagnetic spectrum, there are the ubiquitous GPS sensors that offer precise positional data that make things like self-driving tractors possible. Various types of weather stations allow the logging of environmental conditions, such as rain, temperature, sunlight and other factors that provide insight into crop performance.

What’s the Right Sensing Option?

The more sophisticated the sensor, the higher the cost. Farmers must always weigh the potential for increased yield against the capital investment cost of each sensing platform.

In many ways, the problem is that a farmer interested in remote sensing has too many options. The most effective use of remote sensing would be to have a collection of sensors measuring multiple aspects of the plant growing process, but so far technology suppliers are not making this easy. They tend to offer proprietary solutions that don’t play well with sensors from other vendors. That leaves farmers in a Wild West situation, with no data standards and limited interoperability. Data sharing is inconvenient, if not impossible.

As is always the case, the market can sense these needs, and some startup companies have formed to offer integrated hardware and software solutions. It will just take time for these packages to mature and become viable, high-value propositions for most growers. Likewise, organizations like the Open Data Institute are working to promote open standards that will promote agricultural innovation.

Sensing technologies are evolving rapidly, which means it’s often up to farmers to use trial and error to determine what off-the-shelf products can deliver a “quick fix,” and which can be scientifically validated to contribute to increasing yield and profits over time.

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One thought on “How Remote Sensing Powers Precision Agriculture”

  1. Actually the history of remote sensing is older than what is reported in the article. In 1038 AD – Al Hazen an Arabian mathematician from Baghdad in Iraq explained the principle of the camera obscura to observe sun eclipse.

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