Ricerc@Sapienza

The world is rapidly approaching 10 billion people. Growing enough healthy food under the added stress of climate change
represents a significant challenge.
Agriculture technological development plays a major role in addressing the above challenge because especially in developing countries, people health, population growth and the same country economic survival, are almost exclusively based on agriculture production.
This brings up the need to foster the utilization of the most updated systems for pest and disease surveillance.
Nevertheless, even the most updated systems for crop surveillance are reactive where observations by farmers, survey teams or extension workers provide presence/absence maps that are intended to lead to responses. Given the infectious nature of pests and diseases this approach allows the pest/disease agent to expand, leading to epidemic outbreaks (e.g. rusts) or establishment where eradication is no longer considered feasible (fall armyworm in Africa). As such, the collected surveillance data leads only to a form of triage.

We propose a different approach which is a predictive platform that determines where diseases and pests are likely to occur using historical data, multi-scale weather models, remote sensing and Artificial Intelligence (AI) enabled ground observations.
We envision the use of a new pioneering network of flying monitoring drones to detect ongoing diseases and to predict
their development, and study the speed and direction of their propagation while the epidemic outbreak is still in progress and can be kept confined and under control.
Drones and ground devices can support each other to perform disease recognition at the desired level of fidelity, and forecast the direction and speed of a disease propagation.

Our proposed tools offer the prospect of prediction or early discovery of a spreading disease that will allow informed decision making to keep diseases grographically confined and eventually defeat them.