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How you can use Thermal maps to detect crop stress

According to a prominent farmer from Lowell, Indiana, the best way that a farmer can detect soybean or corn disease is by taking their temperatures. The farmer uses thermal cameras to detect diseases before the crops get discolored long enough and allow the infections to spiral out of control. He goes on to add that plants have their own way of responding to infections the same way animals and human beings do. They may even develop high temperatures and respond by closing down the system responsible for evaporation and respiration. It is even possible to detect slight changes as small as 1 degree F using thermal images and the images can even detect diseases beneath a layer of leaves.

These cameras are more accurate and complicated than the technology we have today and they pick up energy emissions though they do not register the dissimilarities in colors.

Any difference in temperature is captured with white standing for the hottest, violet for the coolest while yellow, blue and red represent the in between temperature.

The highflying farmer goes on to note that the relative dissimilarities are important because they help in the distinguishing of sick plants from healthy ones.

An agronomist from Michigan has been using these thermal images and he says that they have saved him the time and burden of having to scout through farms to locate diseases. He further points out that he uses the technology to detect the amount of water moisture in crops beforehand and corrects the situation before they begin to wilt and get stressed because the slightest level of water that is administered in time will drastically cut down on stress levels in crops. Additionally, the agronomist says that the information gathered from these images is useful for making decisions in the following year.

Another notable farmer from Lowell, Indiana agrees with these sentiments. He is in his first year of using thermal cameras and he too performs his own farm scouting. The farmer says that he no longer needs to keep walking through his farms to detect crop stress and disease that could be developing in his crops. The images taken from these thermal cameras do the job for him.


What impact does the previous crop have?

A certain farmer by the name of Wietbroch has noted that a clear separation in colors between a 58 and 95-acre area of soybean farm with crops of about 2 feet high. This disparity prompted him to call a seed dealer whose field representatives showed up and concluded that it should be attributed to the fact that the corn varieties left on these farms the previous year were different. They noted that the remains of the corn the previous year were not totally utilized by the soybeans. This led to a difference of between 2-3 bushels in the yield of soybeans. The difference has prompted the farmer to discontinue the use of that particular corn hybrid the following tear.

Specifically, Wietbrock needed photos of the 100 acres that he had acquired and planted this year. The initial images were able to show the lines of concentration in the crops. The farmer adds that these images are a very valuable resource since they guide him to the exact areas on the farms that need attention because he can use them to detect any discoloring in the crops.

Another elated farmer also notes that field images provide a great learning curve. He adds that though he may not solve every detected anomaly at a go, he has a basis of knowing what the issues are and how they can negatively impact yields.




A valuable support for crop scouting

Sutton, a farmer who benefits from the technology believes that thermal images can help crop scouts to enhance their work on the farm because they can use the temperature’s disparities to get to the exact problem area on the farm as the crops are growing.

He points out that the fact that these cameras take flights around every fortnight it is a great relief and alternative for scouts having to keep walking through the farm some thrice of four times each year. This is compounded by the fact that your chances of you striking and detecting a problem area are boosted by the guiding capabilities of such images.

He however clarifies that thermal images are not a substitute for farm scouting but rather an enhancement and complement. A farmer or scout will need to get down to the ground and establish the exact cause of the disparities in temperatures. He gives an example where a red area in a photo was used to detect a lack in nitrogen after walking to the affected area. He administered nitrogen to that spot and the images that were taken afterwards showed that the issue had been resolved.

The farmer has been glued to this technology ever since he captured the first thermal images some four years before. He remembers that before he took these photos, his natural eyes had convinced him that all the soybeans on his farm were well. However, the photos indicated a clear difference in the color of the crops, thus reversing the previous assumption that it was due to a difference in crop variety or the dates when the crops were planted.

The images were able to pick the compactions that were brought about by the cattle that grazed in those areas. Those areas had warm yellow appearance and also some red appearance in the patches of land that had more wetness and higher levels of compaction. The farmer noted that this led to a great decrease in the overall soybean per acre with a drop of seven bushels being registered per acre in the year that followed. Furthermore, the losses in crop yield also affected wheat for three years in a row.

Furthermore, the images brought to the farmer’s attention that his combine was spreading the residues in a manner that was not even across the rows and the compactions were as a result of the auger cart’s traveling. He was shocked on realizing how some little acts of carelessness could have such long term consequences on his farms and crops. However, he is grateful that by use of these thermal images, he can observe things in retrospect and avoid such in the future.


Sutton’s scouting services have spread all the way to the Midwest regions of the United States, including Texas. A yield estimating devise has been added with the ability to shoot thermal images towards the end of July or the beginning of Aug. It is able to apply a color palate with three ranges that a farmer can use to conduct an analysis   in the yields. The data that this estimating machine gathers can further be correlated with the thermal temperature that covers the whole farm.


A scientist’s view of thermal images

According to an ecosystem scientist at the Michigan State University, a thermal image is a snapshot in real time. For instance, if the crops are in dire need of water, the camera becomes hot and that gives you the responsibility of figuring out the actual reason behind the dehydration.

The scientist loves to use a multiplicity of sensing machines with a number of flights during crop growing seasons as he scouts for stressed plants. When he is out on the fields researching, he is usually armed with a drone that has the latest equipment’s such as a radiometer with high resolution, a scanner that uses light and a thermal camera.

Another geoscientist at the University of Nebraska Lincoln also has a thermal camera that captures thin dissimilarities in crops for more than 60 months. He and his team have observed a difference in plants that are experiencing low moisture. He has also participated in remote sensing for four decades while carrying out crop sensing for over 24 years at the University’s center for Advanced Land Management Information Technologies.

In closing, the scientist argues that detecting anomalies early is just as vital as timing is to farmers and it can’t be downplayed because it has fundamental impact on the production of the farm. He continues to argue that the two instruments used on the airplane to capture image data are equally good because each one of them has its special role to play. The thermal camera for instance expands the capacity of the plane to capture thermal data.


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