A recent PricewaterhouseCoopers study revealed that, For agriculture, prospective drone applications in global projects were valued at $32.4 billion.
The same study forecast that agricultural consumption would increase by 69% from 2010 to 2050, To cope with demand, the agriculture industry may need to find ways to improve food production methods.
According to a report by the MIT Technology Review, drones in agriculture are used for soil and field analysis, planting, crop spraying, crop monitoring, irrigation, and health assessment.
This article intends to provide business leaders in agriculture with an idea of what they can currently expect from AI-driven drones. We hope that this report allows business leaders to garner insights they can confidently relay to their executive teams to make informed decisions when thinking about AI adoption.
Drone Applications for Agriculture
Switzerland-based Gamaya offers a drone-mounted hyperspectral imaging camera, which the company claims combines remote sensing, machine learning, and crop science technologies.
The company explains that hyperspectral cameras measure the light reflected by plants. It claims to capture 40 bands of color within the visible and infrared light spectrum, 10 times more than other cameras which only capture four bands or color.
The application uses machine learning to process the imaging data into information by comparing the captured images with those in its database and assigning specific conditions with a color. For instance, red shows soil deficiencies, blue is bare soil, white means there are weeds in the soil, green means there are weeds on the crop, black means healthy crops.
Gamaya’s technology is capable of mapping and distinguishing the weeds from plants. It is also able to identify other plant stresses such as disease and malnutrition, as well as chemical inputs in the soil.
Neutrala has developed the Neurala Brain, its technology works with the NVidia TX1 GPU drone and captures five to eight frames per second.
The AI enables a drone to recognize the parts of a tower or line identify defects such as corroding or broken parts that need to be replaced or fixed. A human will still need to need to review parts of the videos or images captured by the AI.
Iris Automation developed the Iris Collision Avoidance Technology for Commercial Drones, an application that allows drones to observe and interpret its surroundings and moving aircraft to avoid collision.
The company states that this application is suitable for use in agriculture, mining, oil and gas, and package delivery. Specific to agriculture, the company claims that the drone application is capable of assisting farmers in surveying crops, planting seeds, and controlling pests while interacting with other drones safely.
SenseFly offers the Ag 360 computer vision drone, which captures infrared images of fields to help farm owners monitor crops at different stages of growth and assess the condition of the soil.
The company claims that the can access airspace data and live weather updates to help plan and monitor the drone.