The market for agricultural drones is expected to grow from $864.4 million in 2016 to $4.2 billion in 2022, at a CAGR of 30.19% during the forecast period.
Source: Markets & Markets
The global agriculture drone market is expected to reach USD 3.77 billion by 2024.
Source: Grand View Research
What is the business value of agriculture drones?
Using drones for crop surveillance can drastically increase farm crop yields while minimizing the cost of walking the fields or airplane fly-over filming. Drones are most commonly used to survey fields and assess soil chemical composition, field status, crop spraying, and irrigation. The benefits to farmers include higher productivity and more efficient use of land, water, and fertilizer.
- Increased yields: Drones with machine vision and specialized algorithms can be used to find yield limiting problems.
- Time savings: Drones reduce the use of human labor in surveying crops and can cover up to 10 times more ground in a given amount of time. Drones are particularly effective in large farms or farms with complex geography or natural barriers such as streams and hedges.
- Crop health imaging: Drones can help farmers to see the real health of the plants by assessing factors such as the amount of sunlight being absorbed by plants in different areas of a field or the chemical composition. Fast remedial measures can be then taken to address bacterial or fungal infections.
- Water efficiency and other environmental benefits: Thermal cameras are able to detect cooler, well-watered field regions as well as dry hot patches. Farmers can use this data to adjust field irrigation and avoid wasting excess water.
- 3D mapping: Drones can provide accurate 3D maps and can update existing maps on a regular basis as the land is altered by human activity, weather or natural disasters.
How can the impact of agriculture drones be measured?
The best way to measure the KPI for agricultural drones is to measure the yield increase or reduction in inputs such as pesticides and water compared to historical data. Time, labor and energy savings related to surveyings activity can also be compared against historical data.
What are specific examples of drone use in agriculture?
1. Soil and field analysis: Drones produce precise 3D maps for early soil analysis, useful in planning seed planting patterns. After planting, drone-driven soil analysis provides data for irrigation and nitrogen-level management.
2. Planting: Startups have created drone-planting systems that achieve an uptake rate of 75% and decrease planting costs by up to 85%.
3. Crop spraying: Drones can scan the ground and spray the correct amount of liquid, modulating distance from the ground and spraying in real time for even coverage. This results in increased efficiency with a reduction in the number of chemicals penetrating into groundwater.
4. Crop monitoring: Time-series animations can show the precise development of a crop and reveal production inefficiencies, enabling better crop management.
5. Irrigation: Drones with hyperspectral, multispectral, or thermal sensors can identify which parts of a field are dry or need improvements.
6. Health assessment: By scanning a crop using both visible and near-infrared light, drone-carried devices can identify which plants reflect different amounts of green light and NIR light.
Positive performance requires relatively long uptime for the drones. It also requires a reliable setup for comprehensive field analytics.
Who is the typical owner of an agriculture drone system?
The owners of the fields are most often the investment decision makers for the purchasing of agricultural drones. They decide to purchase it to help them with farming and increasing yields. However, many companies also offer the use of drones as a service. This approach is preferred by small farmers as it negates the need to invest in capital equipment or training. Drones can then be used seasonally and factored as an operating cost.
Who typically operates an agriculture drone system?
Farmers are typically the drone system operators. Drones are relatively easy to operate and farmers usually do not have an IT team to operate the drones for them. As mentioned above, service providers also provide drones for periodic use. In this case, the service provider will typically manage the drone. A technician may or may not be required depending on the distance of the field from the service provider and the complexity of the job.
Which external stakeholders obtain value from drone data?
Both drone and sensor manufacturers, as well as researchers, have an interest in obtaining data. Drone and sensor manufacturers can use surveying data to understand user behavior and improve their designs. Researchers are often seeking to understand macro issues. The data from drones provides value data to guide their research. However, in both cases, the data is generally owned by the drone operator. As with other use cases, there are not yet standardized monetization models to incentivize data owners to provide access to their data to third parties.
What sensing technologies are deployed on drones?
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. Two types of sensors are installed most frequently on drones: Thermal and Hyper-spectral sensors.
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, and sun exposure.
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.
What communications systems are used to transmit data to and from drones?
The communication system varies based on the what kind of flight controller the drone uses. In most cases, the data communication protocols are ZigBee mesh network. Many drones can operate a flight pattern without connectivity, which is useful in large areas with poor reception.
How is data collected by an agriculture drone?
A drone is fundamentally a vehicle for transporting sensors, such as visual spectrum cameras or thermal sensors. Data is collected as the drone flies around the environment. The height of the drone, and its field of vision depends on the type and quality of the sensor. The higher the drone can fly given the sensing device, the more ground it can survey in a given period of time. Data is generally relayed to the cloud after the drone lands. While drones can transmit data in flight, this is often cost prohibitive. It is also generally not important since a time delay of 30 minutes or a few hours will seldom impact decision making.
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. Other agriculture drones provide data such as NDVI DVI, water trough map, health management zones, elevation contours, emergence uniformity, leaf area index, digital surface models, and crop height.
What challenges must owners of agriculture drones address?
Deploying drones is relatively easy. However several common challenges are related to the relative immaturity of drone systems and the lack of digital sophistication by typical users.
1. Too much data: One of the primary challenges for operators is that they do not understand how to filter and interpret the data in order to derive insights that can guide better decision making. This is particularly true when light or chemical analyses are deployed.
2. Automation: Setting up automated schedules requires basic programming capabilities that many operators lack.
3. Maintenance: Drones are durable but when in heavy use the require regular maintenance. Storms, attacks by wild birds, and collisions with debris can all damage either the drone or sensors installed on the drone.