This is a keynote presentation at a workshop that was organized by Marconi Labs Coltano on 'Wireless technologies in agriculture. The presentation describes the general trend of digitalization in agriculture and food production. This is further illustrated by the IoF202 use case 'Big Wine Optimization' that demonstrates use of the Internet of Things (IoT) in agri-food. From this use case an overall, integrated approach to work on digital innovation is deducted which emphasizes both technological as well as organizational aspects. It also briefly introduces the potential use of 5G illustrated by a use case of the weed detection robot using a pre-5G network in The Netherlands. Recommendations to leapfrog development are provided for three distinctive scenarios ranging from 'no smart farming yet applied' to '5G implementation available'. The presentation ends with conclusions, that are also provided in Italian.

1. IoT and 5G in Agriculture: opportunities and challenges
Villa Medicea, Coltano, Italy, 28 October 2021
Dr. Sjaak Wolfert, Sr. Scientist/Theme Ambassador ‘Digital Innovation in Agri-Food’
MLC workshop ‘Wireless Technology in Agriculture’

2. The Digital Transformation of Agri-Food

3. The cyber-physical management cycle for decision-making
Smart Sensing
& monitoring
Smart Control
Smart Analysis
& Planning

4. 4
33 use case projects
Source: www.iof2020.eu/trials
Internet of
Food and
Farm 2020
Innovation Action:
2017 - 2020
30 M€ funding by
DG-CNCT/AGRI
Large-scale
uptake of IoT in
the European
farming and food
sector

5. UC3.2. BIG WINE
OPTIMIZATION
IoT tools for sustainable wine production, wine quality management and shipping
monitoring

6. Sustainability Footprint per Bottle
Differentiation at the retail level
3 IoT products/services:
1. Wine sustainability tool
2. Wine quality control
3. Wine shipping monitoring

7. 1. Development of the Wine Sustainability Tool
data
measure
Challenges:
✓ Dealing with huge amounts of data
✓ Time-consuming to process them
✓ Real-time decisions
adjust
management practices
Core features:
✓ Professional dashboard
✓ Single, easy-to-use interface

8. data
adjust
management practices
measure
MVP2
MVP1 MVP..

9. DATA
Data science and information
management
Capture
Store
Transfer
Transformation
Analyse
• Management
information
• Actionable
Knowledge

10. Ecosystem Development

11. Business Modelling,
Governance & Ethics
Product Factsheet
Key Performance Indicators (KPIs):
Production costs: -10%
wine
sustainability
tool
wine
grower
€250/
year
consumer
€ ??
€ ??
Price setting
Access and
control over all
data?
Use data also for
other purposes?
Only for
members?
Tool available for
all wine growers?
Biased view on
sustainability?
Effect on market
position?
Pesticide use: -10%
Wrong decisions: -10%
Monitoring time: -15%

12. 1. DESIGN
3. EVALUATION 2. IMPLEMENTATION & TESTING
Business Modelling, Governance & Ethics
MVP2
MVP1 MVP..
Data Science & Information management
Ecosystem Development
LARGE
SCALE
An overall, integrated approach

13. www.IoF2020.eu

14. 5G smart farming applications
14
This Photo by Unknown Author is licensed under CC BY-NC
Weed Detection
Robot
Real-time haulm
killing drone
Water quality
measurements 5Groningen
5G Rural Integrated
Testbed (5GRIT)
5G Range for
remote areas
Smart Farm
Innovation Valleys

15. Weed detection robot using pre-5G
15
Autonomous weed robot: cameras and spot sprayer
attached to the vehicle to control plant specific
weeds.
Detection is a power- and data consuming operation
that requires costly hardware.
→ Offloading computational operations and data
storage to a nearby infrastructure (cloud computing)
reduces costs and energy consumption to extend
operational time in the field. Photo 1 Weed detection robot (“Agrointelli Robotti
spuit plant-specifiek | LandbouwMechanisatie,”
n.d.)
Performance
• > 90% of weeds accurately detected and sprayed while < 5% of the main crop
sugar beet was sprayed incorrectly.
• Uplink 120 Mbps and 25 ms latency (upload images, processing images and
downloading locations of plants)

16. Recommendations to leapfrog development
16
Scenario
I 5G implementations
available
II IoT implementations
available, without 5G
III No Smart Farming
and Food production
applied
Aggregation level Decision-making level
Management cycle
Extend from farm level
to food system or
cross-sectoral/rural
Close management
cycle by including
control mechanisms
Extend individual business to
supply chain and/or public
decision-making
Start at the farm
level to explore the
benefits of 5G
Start at monitoring
level, gradually
expand to analysis
and control
Start at individual business
decision-making level to
explore the benefits of 5G
Identify actors at
different levels that
want to start with
smart farming and
food production
Start at the
monitoring level with
most promising
technology
Start at the individual
business decision-making
level with most promising
technology

17. ▪ There’s a clear potential in digitalization for
Sustainable Food Systems
▪ The same digital data can be used for multiple
purposes
▪ Technical and organizational issues should be
addressed at the same time
▪ Digital innovation should take place in real-life
context by use case projects following a multi-
disciplinary, collaborative, agile approach.
Conclusions

18. ▪ Esiste una evidente potenzialità nella digitalizzazione per avere
sistemi alimentari sostenibili
▪ Gli stessi dati digitali possono essere usati per molteplici scopi
▪ Le questioni tecniche e organizzative dovrebbero essere
affrontate contemporaneamente.
▪ L’Innovazione digitale dovrebbe avvenire nel contesto della vita
reale mediante l’impiego di progetti con un approccio agile
collaborativo e multidisciplinare
Conclusioni

19. Thanks for your
attention!
19
Dr. J. (Sjaak) Wolfert
Strategic Sr. Scientist Digital Innovation in Agri-Food
Wageningen University & Research
sjaak.wolfert@wur.nl