Agricultural business

development with intelligent

data analytics

M.Sc. Petri Linna

Tampere University of Technology

MIKÄ DATA

11.6.2018 2

Multilevel operational group

Farmers

6 different farms

Companies

Rural advisors

organizations

Aigiant

Research

University of Helsinki

University of Turku

Tampere university of technology

Pyhäjärvi institute

Administrative

organizations

Spiral includes

- Official

coordinating

meetings

- Workshops

- Seminars

- Weekly

project tasks

meetings

- Customer

meetingsMIKÄ

DATA

Acricultural challenges

Viability/profitEnvironmental requirements

Precision agriculture

Amount of manageable data

Closed softwares and services

Internal variations of the block

Weather phenomena

Data ownership

Access to MyData

Satellites

Fieldsensors

Harvestmapper Open data

Historydata

Farming is full of differentkinds of data

Drones

MIKÄ DATA-projectThe project will develop an intelligent data

analysis service that will highlight:

• Variations in soil types

• Nutrient levels (e.g. potassium and

phosphorus)

Project schedule 2017/2-2019/12 (3years)

DATA

DATA SOURCES

Drone Specs

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Drone: Airinov solo 3DR

Camera: Parrot SEQUOIA sensor

- 5 spectral bands (red, green, red edge,

near infrared and RGB)

Drone flights with multispectral camera

Parrot

https://www.parrot.com/eu/business-

solutions/parrot-disco-pro-ag

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Trimble

Combine harvest,

yield data sensor

Satellite data,

Digital globe, worldview 3

Satellite data, European

Space Agency, Sentinel-2

New data sources

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- Claas Isaria

- Davis Vantage pro2

- Drainage diagrams

Next steps

- Soil measurement with x-ray?

Service

Development environment

• Kuva palvelusta

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Based on GeoServer

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UI and Backends platform

from www.oskari.org

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Time series tool for WMS

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Data-analytics &

Artificial Intelligence

AI – Neural networks

• Neural network model

– based on field data and yield data of combine

harvester

• PhD student, Petteri Nevavuori developes

this area

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Artificial Intelligent

• Why we need so much data?

– AI tells

• What data is important

• Predicts the field yield

– After AI model is ready, data is needed less

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Project steps

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Data

- Satellite

- Drone

- Combine harvester

- Yara Nsensors

- Soil data

New sources

- Weather stations

- Claas Isaria

- Drainage diagrams

- Precise soil

measurement?

Working of data:

- Correction

- Integration

- Analyses

- Correlations

- AI

ServiceIntelligent data Service:

- Architecture of service

- Use & education guides

- UI & Backend development

Data Sources:

Goal of intelligent service:

- Soil variation

- Nutrient levels (potassium,

phosphorus)

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http://drones2018.utu.fi

Thank you

11.6.2018DI Petri Linna 28

petri.linna@tut.fi

@petrilinna