DATABIO PRECISION AGRICULTURE II
PILOT : PRECISION AGRICULTURE IN VEGETABLE SEED CROPS
DATA – DRIVEN BIOECONOMY
LOCATION EMILIA ROMAGNA
CONTACT NAME STEFANO BALESTRI
PILOT PRODUCT DESCRIPTION
This pilot will use satellite imagery and telemetry IoT for crop monitoring and yield/seed maturity estimation. The pilots will be run by C.A.C. in collaboration with VITO.
The crop involved in first year is sugar beet; according to the results achieved the model may be expanded to other seed crops, namely cabbage and onion. VITO will use satellite data to monitor the crops and will develop yield/seed maturity models. Telemetry IoT technology will be implemented by C.A.C. on 5 farms located in Emilia Romagna and Marche.
Specifically, as part of pilot innovative solution, an online platform will be used to provide satellite imagery, weather and soil data and yield/seed maturity predictions. VITO, in collaboration with a number of Belgian partners, has developed a web application “WatchITgrow®” for potato monitoring and yield prediction in Belgium. The existing WatchITgrow® application will “filled” with satellite, weather and soil data for the Italian pilot sites. To be able to provide maturity estimates developments are needed and it is necessary to collect field data. The data will be collected by C.A.C.
The farmer and pilot owners can use the satellite imagery to monitor and benchmark the maturity curve of seed crops till harvesting in correlation with weather and microclimatic conditions recorded on site through dedicated meteorological units.
A weather station will be installed in the vicinity of each field with sensors for air moisture and temperature, soil temperature, rainfall – remote monitored. Telemetry IoT stations will transmit data to the cloud infrastructure in the process of crop monitoring, biotic and abiotic stress diagnostic, alert and operational recommendations.
OBJETIVE OF THE PILOT
The main objectives for this pilot is:
- Identifying the potential for using satellite data and machine learning for monitoring crop development and maturity and the development of prediction models.
- Evaluating the comparative importance between the use of proximal wireless sensor network data and satellite data.
The main KPIs of the pilot are:
- Model accuracy: maturity/yield prediction accuracy showing an acceptable error rate when tested on data that it was not trained on.
- Revenue potential with alternative cropping strategy vs. what happened: Quantify increased revenue potential on historical data, .e.g. what was the accumulated value, vs what could have been achieved using conventional cropping techniques.
- System usage: Number of users of DataBio technologies – yield models and proximal
wireless sensors in seed crops. This is technology transfer and takes time to establish; for this project, a baseline will be measured first, then followed-up by monitoring usage after system deployment.