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Agriculture surveys are specialized geomatics products, focused on using multispectral and hyperspectral imagery to classify vegetation and identify crop stress, enabling precise application of agrochemicals and water resources
In early days unmanned aerial vehicles may not have been used in the agriculture industry but if there is one industry that has gained a lot because of these drones, it is agriculture. Our drones are increasingly being used by farmers to help with:
The application of UAV technology in agriculture around the globe is increasing at a phenomenal pace. A number of studies from around the world suggest the adaptation of UAVs for agricultural purposes will be the largest single non-military beneficiary of this fast growing technology.
Farmers are able to protect their plants from various diseases as our drones help in identifying symptoms of diseases through imaging of the field from a height. These drones also collect important data about the soil with the help of NDVI (Normalized Difference Vegetation Index) imaging. The use of UAVs in fields has helped farmers a great deal as they need not carry out manual inspections of the crop to protect it. These drones are lightweight and they can fly over the field to reach every part of it. The high resolution images taken by our drones tell a farmer a lot about the health of the crop and the quality of soil to take appropriate action.
Precision agriculture is a farming management concept based on observing, measuring and responding to inter- and intra-field variability in crops.
The goal of precision agriculture is to more efficiently apply a farm’s limited resources to gain maximum yield. A primary method for doing that is to minimize variability of crop health within and across fields. To learn more about precision agriculture, read this excellent overview published by The Economist
Due to its nature, precision agriculture requires a LOT of data to work. The three main types of data include:
Drones are really just a new, high-precision way to obtain geo-tagged images from the air. Compared with other aerial survey methods, drones generate more precise and more frequent data about the condition of crops. This data is used in many ways to improve the performance of a farm’s operation.
For surveying fields of less than 50 hectares in size, drones are cheaper than manned aircraft surveillance, manned scouting and satellite imaging.
Drones are used to gather a variety of image-based data about the condition of crops, fields and livestock including:
For livestock operations, drones can be used to monitor the location, status and movement of animals over time with more frequency and at a lower cost than other means.
Drone data is used to do farming jobs more effectively and efficiently, including:
By some measures, 80% of the global drone industry revenues touch agriculture in some way.
But why would farmers – some of the most risk-averse people on Earth – adopt such a new technology?
Perhaps it’s because agriculture drones offer clear advantages over other crop monitoring methods including satellite imaging, manned scouting and manned aircraft. These advantages include:
Most agriculture drones depend on multi-spectral imaging to spot problems with a crop’s health; specifically, they look at changes over time in visible light (VIS) and near-infrared (NIR) light reflected by crops. These images are taken over time by drones, manned aircraft or satellites.
It is possible to detect plant health from these images because plants reflect different amounts of visible green and NIR light, depending on how healthy they are. By measuring the changes in visible and NIR light reflected from a crop, we can spot potential health issues.
This image explains the general idea:
To monitor changes in plant health over time, drone images are processed to calculate a tracking index called NDVI (normalized difference vegetation index), which is a measure in the difference between light intensity reflected by the field in two different frequencies:
NDVI is the ratio of near infrared (NIR) reflectivity minus visible red reflectivity (VIS), divided by NIR plus VIS:
NDVI = (NIR-VIS)/(NIR+VIS)
Here is what you see when you compare a normal camera image of a winter wheat field to a NDVI-processed image of the same field:
VIS and NDVI images of winter wheat field (courtesy Agribotix)
Notice how the NDVI-enhanced image (right) does a great job separating the healthy wheat stalks (green) from dying edges (red) and the dry earth (black/brown).
There is some debate over whether NDVI is the right index or whether the simple difference between light spectrums (NIR – VIS) is more useful.
NDVI is the most popular index calculated using drone data, but there are many others. Some may be more or less important to your farm, depending on your situation. Some of the more popular indices include:
If you want to dive deep into this emerging science of agriculture image processing, here’s a list of the latest research.
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