CROPP (Cultures Risks Observation and Prevention Platform) is an easy and user-friendly application designed to help farmers in monitoring their lands. The service will provide data gathered at two different levels. In situ measurements will be merged with satellite observations to give users practical information about possible risks that could threaten their crops.

This project is solving the Crop Alert – Learning From the Growers challenge.


Our goal is to provide farmers information about their fields' health status. The monitoring includes local sensors for short-term measurements while optical and radar imaging, acquired from satellites, are exploited to study the macroscopic evolution of any dangerous phenomena. In addition to the general atmospheric factors, we chose to focus our attention on a severe plague for agriculture in several countries: insects invasions.

The hardware comprehends several sensors to measure temperature, rain, soil humidity, sounds from harmful insects and an actuator able to drive them away. These components are stored in a small and low-cost device called Distributed Measurement Device (DMC). All the dmds of the interested area communicate via radio signals to the Data Collection Center (DCC), which is directly connected to the main server and updates data regularly.

The user can access this service through a smartphone app and a website which provide simple and straightforward advices to help the management of the field. In case of critical situations, the app is able to send an alert notification and the farmer himself can upload a feedback (text or image) to inform neighboring farmers about the problem. Our expert system also combines DCC data and stored information to estimate the infestation probability distribution.

The macroscopic observation uses an already existing satellite constellation in order to minimize costs. We chose a LEO sun-synchronous orbit for regular passages over the interested areas. Images at different wavelengths are sent to the ground station and elaborated to map the Normalized Difference Vegetation Index (NDVI) over the field. These results are then compared to reference environmental conditions and an output is used to indicate the progression of an eventual disease. Knowing the pests spread in advance can considerably help farmers contain its destructive consequences, and could also be useful to scientists as a model for future predictions.

For more info visit us at:

take a look at our short slideshow:

or just check out our demo on YouTube:

Project Information

License: GNU Affero General Public License 3.0 (AGPL-3.0)"

Source Code/Project URL:


Presentation Video -
Website -
Slideshow -
Demo -
Crowdfunding Campaign -


  • Mohamed Elhariry
  • Giorgio Severi
  • valentina celani
  • Andrea Di Ruscio
  • Andrea Gallegati
  • Simone La Fauci
  • Virginia Notaro
  • gabriele angeletti
  • Nicole Segala