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High Fidelity Data Collection for Precision Agriculture with Drone Swarms

Project Description:

Projections from United Nations show that by 2050 we will need to produce 70% more food. However, agriculture already takes over 38% of the land and it is the largest user of freshwater in the world. What can we do to improve the way food is produced? We propose high precision agriculture! It uses big data to support decisions increasing productivity and reducing the use of land, water, fertilizers, pesticides, herbicides and fungicides. The use of more intelligent methods is also beneficial to biodiversity, changing the way natural resources are managed from an one-size-fits-all approach to a tailor-made solution. Yet, traditional data sources are known to have limited resolution and even low altitude remote sensing (e.g. airplanes or unmanned aerial vehicles - UAVs) can only see from a fixed perspective: above. Additionally, according to PwC there’s a $32.4bn market for UAVs in the agriculture industry. This project proposes to improve productivity and sustainability by increasing the precision of the data collected down to the individual plant level with the use of Artificial Intelligence (Deep Convolutional Neural Networks) powered autonomous micro aerial vehicle swarms capable to fly among crops (e.g. corn, soybean and oats). With the high resolution data collected by a swarm of small and cost effective drones, farmers will be able to take advantage of all machine learning technology already available to optimize food production, maximize yield and minimize impact in the environment.

My role in this project:

This project was accepted at the winter 2019 call from the IVADO Postdoctoral Scholarships Program.