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Drone Technology

Benefits of Drones Technology in Agriculture:

1. Increase yields:

Improve production, efficiency and get higher yields by identifying problems before they happen with increased crop health and awareness and frequency.

2. Save Time:

Drones can be set up and deployed quickly. This ease of use allows farmers to gain information routinely or whenever they need it.

3. Plan for the future:

Generate precision orthomosaic for the better crop planning and land management.

Features of Drone Technology:

  • 40-60 times faster than manual spraying
  • Full spectral band capturing
  • Precision mapping
  • Thermal imaging

Agriculture Drones Solutions:

1. DJI AGRAS MG1-S Agricultural Drone

  • Ground sensing radar for precision flight
  • Interchangeable nozzles for optimizing spraying
  • Easy and intelligent operation planning
  • Rugged design and components


  • An easily integrated crop health sensor for consumer drones
  • Capture NIR imagery to monitor crop health
  • Access all of Sentera’s Field agent Software

3. DJI M100 with MICA SENSE

  • A flight platform design with customizability in mind
  • Carry multiple camera sensors and battery payloads
  • Mica sense is full spectral band sensor for even greater crop information

How Drones work in Agriculture Fields?

Many types of drones are available today, but not all are good candidates for farming. Those suitable for agricultural applications fall into two categories: fixed-wing and multi-rotor drones. The cost and payload capacity of both types is similar, and the hardware is becoming commoditized quickly.

Fixed-wing drones have long-range flight capacity, an advantage when a large area is to be covered. They are also crash tolerant; the one shown here is made from high durability foam.

Multi-rotor drones are faster to set up in the field and can take off and land vertically. The mission set-up is simple; there is no need to plan takeoffs and landings into the wind as must be done with a fixed-wing. For inexperienced operators, they are the easiest way to get up and running quickly.

With either type of drone, the actual flight process is relatively straight forward. Using software on a ground control device (typically, a tablet, laptop or smartphone), the operator draws an outline of the area to be surveyed on a Google map type of view. The software programs the flight, overlaying lines on the map to show the drone’s flight path. The information is uploaded to the drone over a wireless link. Takeoff, flight and landing are completely autonomous.

Although historically, drones designed for recreational purposes were not suitable for use as agricultural drones due to lack of range, low payload capacity, or poor flight planning capabilities, they are rapidly advancing. Some farmers have been able to successfully employ the more recent models to collect agricultural data.


Inexpensive consumer drones can be used out of the box to take a video or still photo from above a field, which may spot some problems. To really obtain value from an agricultural drone, however, other types of sensors must be considered, as well as tools to fly the drone in a pattern over the entire field and software to combine the sensor readings across the field into a single layer that is then analyzed and geo-referenced. Only in this format can a user then use a GPS-enabled smartphone or other device to walk to and inspect specific problem areas or combine the information with other data layers.

Drones collect information largely based on the light reflected by the crop below. For agricultural purposes, using a specific type of sensor can help growers collect data that indicates where issues exist so that they can take appropriate action.

Plants, of course, capture visible light to drive photosynthesis. However, near infrared (NIR) photons don’t carry enough energy for photosynthesis but they do bring lots of heat, so plants have evolved to reflect NIR light. This reflection mechanism breaks down as the leaf dies. Near Infrared sensors take advantage of this property by monitoring the difference between the NIR reflectance and the visible reflectance, a calculation known as normalized difference vegetation index or NDVI. A strong NDVI signal means a high density of plants and weak NDVI indicates problem areas on the field.

There are two schools of thought about choosing an NDVI camera. One approach is to use a multi-thousand dollar, purpose-built, camera that captures precise wavelengths. The added value of the additional narrow-band frequencies captured by these purpose-built cameras typically is not enough to offset their high price, except for a few specific use cases.

The preferred approach is to convert a high-quality consumer camera for agricultural imaging, which involves removing the lens and replacing a filter and sometimes replacing the lens itself. While both approaches deliver high quality NDVI data, we recommend getting started with a converted camera and only considering a purpose-built camera if the results are not adequate.

There is a lot of research activity focused on using other types of drone-mounted sensors for agriculture. The two most commonly mentioned are thermal cameras and hyper-spectral cameras.

Thermal sensors can read the radiated temperature of an object, and some of the newest models are light enough to be carried by a small drone. A thermal sensor might help identify how plants are using water, as those with access to more water appear cooler in an image. The challenge is that these temperature variations are minor and can be difficult to distinguish from the other factors that might heat or cool the plant, such as breezes, sun exposure, etc. More research is needed in this area.

Hyper-spectral sensors record many wavelengths of both visible and invisible light. Although the size and price of these cameras are coming down, they are still large and expensive. The promise of these sensors is that they might be able to identify the specific type of plant merely by measuring the color of light that it reflects, which would make it easy to pick out things like herbicide-resistant weeds. However, calibrating these cameras to work on a low-flying drone in a farm environment where the light conditions vary as much as they do is a problem that needs to be solved before hyper-spectral cameras can deliver.

Data Processing and Analysis

When a drone collects data over a field, the camera takes several hundred still images as it flies a “lawnmower” pattern back and forth across the field. These images then performed to make the results useful.

A computer with specialized software can process the images locally, which can be time consuming on several levels. Gaining mastery of the software initially and achieving repeatable results, flight after flight, can be a steep learning curve. The processing itself can tie up the computer for hours each time new data is loaded, suppressing productivity and requiring the operator to be present to monitor progress.

Alternatively, processing can be done using a purpose-built, web-based service. In this model, the operator performs the flight, runs software that automates image collection, and uploads to the cloud. There, the images are processed rapidly and returned to users. The advantages of the software as service model are several: the operator is free to walk away and do other things while processing happens in the background; there’s no need to invest in pricey hardware or software; and, users are always benefiting from the latest release and the newest reports.

One of the most useful reports that a drone can produce is an NDVI image. This takes the stitched images from an NIR camera and applies a “false coloring” to highlight problem areas. While there is no standard color scheme for these reports, Agribotix colors the image with the strongest signals appearing green, transitioning to yellow, then red, and eventually to gray for the weakest parts of the field.

Different services will supply the results in different formats. Almost all will provide results in a GeoTiff download, which is an image file with GPS coordinates. Another common format is KMZ, which enables viewing the images in Google Earth.

A less common, but a very important format is the shape file. These images divide the field into zones and assign a value to the crop density in each zone. This format is useful to have, as it can be read directly by many farm data packages, allowing the drone layer to be easily combined with other data sources to create prescriptions for variable rate application. Finally, some services supply the results directly on a map in a web browser, in some cases allowing users to use a smartphone to view their location on the results while in the field to ground truth.

Scope of Drone Technology in Pakistan to Control Pest, Insect and weeds in Crops

Massive financial losses by crop weeds and pest attacks are possible to avoid now after the Punjab (Pakistan government has first time across the country granted using Unmanned Air Vehicle drone technology in agriculture sector for the application of pesticides on crops, monitoring weeds, pests and nutritional deficiencies. Drone will kill crop pests and monitor weeds’ growth at massive level in almost all the agriculture districts of Punjab.

Agriculture officials say that farmers would have to arrange or purchase their own drones for agriculture purposes. The farmers have been required to submit an application with respective district Deputy Director Agriculture Extension and he would forward the application to District Implementation Committee comprising of Deputy Commissioner and District Police Officer of concerned district where the drone is required for agriculture purposes. Drone will kill crop pests and monitor weeds’ growth at massive level in almost all the agriculture districts of Punjab.

The DIC would issue No-Objection Certificate after deeply studying all cases. The NOC would carry all information about drone utilization including time scale, place where the drone is required.

The DIC would be responsible for the security and safety of sensitive places located across the agriculture field where the drones are required.

The advent of such new technologies in era of computers and electronics will revolutionize the contemporary agriculture in Pakistan. Growers say the drone technology also helps to capture the differentiation in fruiting, color, growth of the plant – about what’s going wrong and then the farmers are issued proper advisories by the scientists – whether it’s about less watering, fertilizers, more or less chemicals etc. This is quite a next generation assessment of crops.

The age of the crop, if it is a thirty-day crop or a forty-day crop; what should be the height, the color configuration or the fruiting level of the crop? Drones can help us identify if the crop is not fruiting as per the norms, if it’s over fruiting or if it’s mellowing down in color configuration. The drones can be configured to navigate in a 30 meter by 30-meter plot to assess the damage. Agriculture officials say the drone technology would safe crops from destruction after technical monitoring of weeds and pest attack at large scales.


The future Drones will allow farming to become a highly data-driven industry, which eventually will lead to an increase in productivity and yields. Due to their ease of use and low cost, drones can be used for producing time series animations showing the precise development of a crop. Such analysis could reveal production inefficiencies and lead to better crop management. With those possibilities in mind, it can be assumed that this technology will transform agriculture into a high-tech industry for the first time, with decisions being based on real gathering and processing of data. Thus, agriculture’s prime concern is not the drone’s speed or flexibility, but the type and quality of data it can obtain. So the industry will primarily push for more sophisticated sensors and cameras. Another objective will be to obtain drones that will require a minimal level of training and be highly automated.


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