For countries whose economy relies significantly on agriculture, it is fundamental to quickly detect the outbreak of crop diseases and understand the dynamics of their propagation.
Nevertheless, especially in low-income countries, crop monitoring is still mostly left to a small number of insufficiently prepared extension workers, who perform manual and visual monitoring on site, gathering data with poor statistical reliability.
This is mostly due to the lack of skilled personnel, and of appropriate supporting infrastructure.
To face the above issues, we envision the use of a new pioneering network of flying monitoring drones to detect diseases and predict the speed and direction of their propagation while it is still in progress.
Supported by AI, the drones can detect field anomalies. Moreover, they can coordinate with each other to perform disease recognition at the desired level of fidelity, and forecast the direction and speed of the disease propagation.
Such a monitoring system will thus trigger prompt intervention to protect the smallholder farms not yet affected by the disease, to treat the affected plants when possible, and to enable protection policies to stop or mitigate the disease propagation, such as the creation of firewall lines and the adoption of pest control chemicals or medicines.
The availability of such a powerful tool, based on relatively cheap devices will ensure the adoption of pioneering technologies for food security, in areas where the local population is unprepared and poorly trained to recognize and face crop diseases, and where the economic growth of the country is slow.
Our proposed tools offer the prospect of early discovery of a spreading disease that will allow informed decision making by small farm holders, or by government offices in terms of pest and disease control interventions, as well as monetary investments in the most appropriate directions.
While the use of single, remotely controlled, drones for precision agriculture is currently a hot research topic, the current project aims at advancing the state of the art by integrating several key ingredients:
1. the use of a squad of multiple autonomous and cooperative drones;
2. the use of machine intelligence systems for the recognition of plants and related disease from cameras and processors located onboard the drones;
3. the integration of online available information on the environment, for prediction of future environmental conditions and factors (e.g. weather) that can potentially cause or foster a disease propagation;
4. the use of statistical sampling, the study of geographical correlation and spline interpolation of measurements to determine the frontline of a spreading disease and its direction;
5. geographical scalability, which is achieved by trading off a triangle of settings including accuracy of detection,
number of drones, and time to complete the monitoring tasks.
Joint autonomous path planning and dynamic task assignment on a squad of drones is something very new and unexplored so far.
The use of a machine intelligence system, with tunable fidelity, for recognition of plant diseases is also at a preliminary stage, and most
of the work has been done with same height imaging, while the use of drone based imaging is still a challenge for the specific angle of
sight which requires adapting existing images archives.
The results of the project are of interest to many other application scenarios requiring situation awareness and preparedness. Monitoring systems are widely recognized to be an invaluable tool whenever surveillance, intrusion or hazard detection are required to ensure prevention, prediction and protection against critical events such as natural disasters or security threats.
The project focuses on the important application of crops in fields and ecosystems monitoring, with a large impact on food security,
human safety, and global well being planet-wide. Additionally, the proposed system has the responsiveness, the robustness and the
autonomous intelligence that make it suitable as well for other important applications such as disaster monitoring and homeland
security.
The proposed monitoring systems will have a highly important impact in improving emergency response, providing critical information
and minimizing societal crisis.
The project will also advance the current body of knowledge on monitoring systems, by providing high level scientific papers published
in flagship international journals and conferences on heterogeneous network deployment, storage overflow prevention, topology
improvement and real-time communication.
There are also other broader impacts of the project in the application area on which we mainly focused.
The project will use the archives of PlantVillage. PlantVillage has already provided a significant benefit to society by providing free,
open access knowledge on plant health to over 3.5 million people around the world. PlantVillage is also a pioneer in AI with the release of its algorithms, images and data to the public domain.
Our proposal seeks to extend the use of PlantVillage integrating AI decision making on crop diseases into smart autonomous drone squads. Such an approach will immediately provide broader impacts.
Hence, besides the innovation in the realization of autonomous squad of drones for monitoring systems, the project will provide important innovations in the field of food security, as we will have a smart autonomous system collecting millions of data points on crop
type and disease in real work farms.
Our monitoring system, in the hands of food security agencies and governments, will produce an economic shift in low-income countries. The availability of such a powerful tool, based on relatively cheap devices (that are getting cheaper all the time), will ensure the adoption of pioneering technologies for food security, in areas where the local population is unprepared and poorly trained to recognize and face crop diseases, and where the economic growth of the country is slow.
Our proposed tools offer the prospect of early discovery of a spreading disease that will allow informed decision making by government offices in terms of pest and disease control interventions, as well as monetary investments in the most appropriate directions.