L'agricoltura del futuro tra dati satellitari, IoT e dati sul campo (May 18th 2018)

1. www.agricolus.com
a.cruciani@agricolus.comAndrea Cruciani – co-founder & CEO
L’agricoltura del futuro tra dati
satellitari, IoT e dati sul campo

3. The field at
the centre of
the system
Coordinating
Agricultural
activity
Monitoring
productivity
Preventing
diseases

6. IoT
Forecast Models
Scouting Satellites / UAVs
Decision
Support
Systems
A complete agronomic platform

7. Cloud Computing
Dati
satellitari
Dati SAPR
Osservazioni
dirette e di
laboratorio
Sensori
Meteo
Farmers
Engagement
Shapefile
CSVREST
Interoperabilità
GEO-DATA
Dati
ISOBUS
WEB
Mobile
ISOBUS
Analisi spaziale
ReportPrescriz.VigoriaSuoloResa
Pest
monitoring
Dal campo
direttamente al
sistema
Dal campo
direttamente al
sistema
Dal campo
direttamente al
sistema Dal sistema
direttamente al
campo
Dal sistema
direttamente al
campo e viceversa
Realtà Aumentata
Comandi vocali

8. Advanced daily
farm management

9. Marketing
R&D
Data Analysts
GIS – Remote Sensing
Andrea Cruciani
CEO
Antonio Natale
COO
Diego Guidotti
R&D
board of directors
Scientific and Technical Partners
Università degli
Studi di Perugia
EU Projects, accelerators & awards
H2020 - Phase 1
Helpdesk
Multidisciplinary team: R&D + industrial know-how
Software dev
Agronomists
Business Development
Operations
User eXperience

10. Data usage for agriculture

13. IoT Data sources

14. IoT weather station data

15. IoT data feeds our analysis tools
statistical analysis
machine learning
deterministic models:
• crop models
• pest models

16. Nutrient balance

17. Pest Modeling - olive fruit fly

18. Remote Sensing

19. Multispectral remote sensing
resolutions
a. 30 mt
b. 10 mt
c. 2 mt

20. Remote sensing data feeds
geospatial analysis

21. Scouting

22. Field data collection

23. Agricolus’ international
large scale experiences

24. 1. Striacosta albicosta
An Early warning system in Agricolus with these features:
• Monitoring: Agricolus monitor the trappings and the field scouting; data can be shared with other farms
• Modelling: The model can be used to pre-alert farmers defining a date when begin the fields scouting to
detect the egg presence; after collecting some traps data in Ontario we can validate and calibrate the
model to local condition;
2. Vomitoxin
Monitoring: monitoring the crop and the residues, perform statistical and geospatial analysis on monitoring
data;
Corn phenology: the models estimates the main crop stages according with crop varieties FAO class; the crop
phenology can be used to estimate the risk of infection for Fusarium, Aspergillus, Penicillum;
Canada - Ontario pilot

25. Pilot – University and TFP (technical and financial partners)
45 km2 - paddy plot
• Ground-truth data from mobile applications
• Multispectral data from Sentinel2A and Sentinel 2B
• Synthetic Aperture Radar (SAR) data from Sentinel1A and Sentinel1B
• Crop growth modeling
Upscaling perspective
Rice Monitoring at Country Level involving
• Local, Regional and
National Governments
• Statistics offices
• Agricultural Direction
Mali – agricultural statistic framework

26. a.cruciani@agricolus.com
Andrea Cruciani - CEO
Making precision farming easier
for EU and worldwide
farmers
• Up to 20 % fertilizers and treatment savings
(return on in investment in 2 years)
• Improve and certify trasparence on production
quality «from farm to fork»
• Interacts with farm machineries