Ricerc@Sapienza

The world population is estimated to grow and reach about 10 billions in 2050, significantly raising the demand for food.
Especially in developing countries, where the economy is almost entirely based on agriculture, the increasing demand for food is exasperated by climate changes and by scarce infrastructures and farm tools.
New approaches and technologies are needed to mitigate the effects of climate changes and spreading plant diseases, to produce sustainable food, and eventually meet the UN zero hunger goal.
Currently adopted technologies for crop monitoring mostly rely on human operators, who plan the monitoring operations and analyze the collected data.
Unfortunately, these approaches fail in providing timely support and disease detection in large areas, allowing the pest/disease agent to expand, leading to epidemic outbreaks (e.g. rusts), before being detected.

Differently from current solutions, this project proposes an autonomous monitoring system to continuously monitor the crops, detect diseases, and, based on historical data, provide predictions about the most critical crops.
We consider the use of a squad of aerial drones, which monitor crops and autonomously adapt their trajectories, upon detection of ongoing diseases or detection of critical anomalies.
The drones collect data and directly analyze them using a special-purpose Artificial Intelligence (AI) algorithm.
In detail, we provide and test the AI models for plant disease detection, which use images of plant leaves; we design the algorithms and protocols for path planning and task assignment for monitoring and data collection; and finally we integrate a forecast module to predict critical zones and adapt the drone trajectories accordingly.

We envision that our system may help farmers to increase food production, by reducing disease spreading thanks to timely detection and intervention.