Vijay Kumar, the dean of the University of Pennsylvania’s School of Engineering & Applied Science projected during a TED Talk earlier this year that flying robot advances could soon completely revolutionise agriculture, decreasing input requirements like water by up to 25% and improving yields by around 10% through Precision Farming.
What Is Precision Farming?
Precision Farming is the idea of flying airborne robots over orchards to essentially map them out, creating a precision model which informs farmers as to exactly what each specific plant needs in regards to fertiliser, water and pesticide.
How Do These Intelligent Flying Robots Work?
These autonomous flying robots are not related to the commercially purchasable drones available on the market today. These state-of-the-art bots aren’t equipped with GPS, instead they rely on on-board sensors, laser scanners and cameras to navigate their environment and establish their position by scanning their surroundings and using a method of triangulation to pin-point where they are. The bots then put together a map of their environment so they can travel safely, avoiding obstacles.
2 Critical Benefits Of Such Technology
These bots can create high-resolution maps (5 centimetres resolution) which lets a person outside the building deploy these without needing to enter the premises.
By building a connected network of these flying drones using integrated computation, communication and sensing technologies, they are able to work together to skillfully map-out whole farmers orchards. Their inter-connectivity allows them to sense their neighbouring drones, resulting in one single robot leading the rest of the flying swarm.
A Closer Look
These flying machines allow users/farmers to do various things including:
Count the number of fruits on each and every tree in an orchard. This allows the farmer to project yield and enhance the production chain further along.
Models of plants can be captured, from which 3-dimensional reconstructions can be created, which are used to predict canopy size, this is then correlated to the leaf area of each plant resulting in the leaf area index. This index provides a gage of plant health and the photosynthesis ability of each.
Infra-red and visual sensory information can be merged to compute indices such as the normalised difference vegetation index (NDVI).
Much more easily identify the early on-set of chlorosis which is signalled by yellowing leaves by reporting where exactly in the orchard the problem is stemming from.
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