Vinko Lešić, Hrvoje Novak, Marko Ratković, Monika Zovko, Darija Lemić, Sandra Skendžić, Jelena Tabak, Marsela Polić, and Matko Orsag

Innovation centre Nikola Tesla

University of Zagreb Faculty of Agriculture

University of Zagreb Faculty of Electrical Engineering and computing

Zagreb, July 1, 2021

Rapid Plant Development Modelling System for Predictive Agriculture based

on Artificial Intelligence

Presentation outline

• State of the art

• Encapsulated design – microclimate chambers

• Big data creation

• Models, learnings and potential

state of the art

• Consulting based on sattelite imaging• Level of recognition of crop culture and development stage• Images are available through EU Copernicus sattelite system and portal

https://www.copernicus.eu/hr• „Free of charge” (images are openly available, ICT company required to apply

it for individual users)• Significant for climate changes and weather forecast• Promising deeper applications to agriculture, still in infancy (scientific

research domain)

• Consulting based on field samples• Sophisticated sensors and expert knowledge – still the best insights – occasional possibility, expensive

• Aerial samples (drones)• Level of disease recognition, pests or water deprivation with already visible consequences (multi-spectral cameras)

• Precision agriculture

State of the art today: digital agriculture

State of the art today: precision agriculture• Consulting based on field sensors

• Mesh grid of networked sensors:• humidity and chemical soil content (pH value)

• accurate chemical analyses (e.g. nitrogen level) - expensive

• „Internet of Things” – estimated to bring 20% yield increase

• Real-time trends of field state

• Croatia (e.g.):• Agrivi d.o.o. – monitoring platform for agriculture productio

https://www.agrivi.com/hr

• FER & AF – custom sensors for specific applications, contact: Prof. Vedran Bilas

• FER, FERIT & PIO (Osijek) – open platform for integration of large number of sensors and field monitoring, contact: Prof. Ivana Podnar Žarko

• High-progress currently:• active area in research domains

• commercial solutions available

Example of sensors in precision agriculture

Exemplary field monitoring plaftorm by Agrivi d.o.o.

Ultrasonic probe for measuring plant water apsorption (e.g. on vine)

• Electrophysiology• Measuring various electrical impulses of the plants (analogy

to nerve system)

• Vivent, Switzerland https://www.phytlsigns.com

• Yara, Norway https://www.yara.com

• FER (mentioned earlier), SENSIRRIKA

• Still undergoing R&D

• Combination of approaches

• „Fusion” of measurements• Cross-section of large number of measurements from various sensors

• Complemented by aerial measurements

• Potentially combined with sattelite imaging

• Emphasis on data processing

• Comprehensive digital data

• Receommendations to producers

• Most od todays H2020 projects and applications

• Large number of advertised commercial solutions (which are really not there yet)

State of the art tomorrow: predictive agriculture

• Artificial intelligence (AI) in agriculture• AI is rapidly developing in large number of areas

• Delayed in agriculture due to lack of the „big data”:• Agriculture is at digitalization stage (ongoing)

• Accumulating the measurements (slow, yearly cycles)

• Lack of correlated outputs (usually only measuring climate and soil content)

• Today mostly available only on level of image processing

• Enable predictions in agriculture

• → yield increase – expected over 20% increase

• → resource efficiency and eco production

• → climate changes mitigation

State of the art tomorrow: predictive agriculture

encapsulated design

AgroSPARC

Smart and predictive agriculture for resilience

to climate change

Microclimate chambers

Microclimate chambers• Microclimate control

• Air temperature• Airflow (wind and CO2)• Air humidity• Soil humidity• Soil pH value• Photosynthetic photon flux

density (LED lighting)

• Plant condition identifiers• Leaf area index• Growth rate• Spectral image light intensity:

• Near-infrared – water content• Red edge – disease condition

• Green band – photosynthesis level

Rapid Plant Development Modelling System

• 4 microclimate zones

• 40+ sensors

• 8-12 plant containers per device

• 4 devices

Rapid Plant Development Modelling System

Sunlight to LED – spectrum

Sunlight to LED – spectrum

„Full light” commercial LED light datasheet

Sunlight to LED – spectrum

PAR (400 – 700 nm)

PAR – Photosynthetically active radiation

FAR – Far-red radiation

FAR (700 – 850 nm)

„Full light” commercial LED light datasheet

Sunlight to LED – intensity

• S. Chandra, H. Lata, I. A. Khan, and M. A. Elsohly, “Photosynthetic response of cannabis sativa l. to variations in photosynthetic photon flux densities, temperature and co2 conditions,” Physiology and Molecular Biology of Plants, vol. 14, no. 4, pp. 299–306, 2008.

*Original figure

Sunlight to LED – intensity

• S. Chandra, H. Lata, I. A. Khan, and M. A. Elsohly, “Photosynthetic response of cannabis sativa l. to variations in photosynthetic photon flux densities, temperature and co2 conditions,” Physiology and Molecular Biology of Plants, vol. 14, no. 4, pp. 299–306, 2008.

*Original figure

~ 200 – 750 W of LED light (per m2)

Sunlight to LED – intensity

• e.g. solarium beds

• 4.5 – 7.5 kW

• CFL light (less efficient, focus on UV)

Sunlight to LED – intensity

• e.g. solarium beds

• 4.5 – 7.5 kW

• CFL light (less efficient, focus on UV)

MIGRO blog (https://www.migrolight.com)

Leaf area index

• Measuring:• mm paper

• hand tools (scanners)

• hemispherical photography estimation

• LiDAR-data estimation

• Camera approach:• robotic vision

• convolutional neural networks –requires „big data”

• different angle photos → 3D model → synthetic dataset

AM350 LAI scanner

Leaf area index

• Measuring:• mm paper

• hand tools (scanners)

• hemispherical photography estimation

• LiDAR-data estimation

• Camera approach:• robotic vision

• convolutional neural networks –requires „big data”

• different angle photos → 3D model → synthetic dataset

obtained blender model

big data creation

… …

Black box modelling

?inputs outputs

? ?

…

Black box modelling

?inputs outputs

…? ?

climate soil biochemistry

BBCH scale – winter wheat

• J. C. Zadoks, T. T. Chang, and C. F. Konzak, “A decimal code for the growth stages of cereals,” Weed Research, vol. 14, no. 6, pp. 415–421, 1974.

…

Black box modelling

?inputs outputs

BBCH [1-100]

1. germination

2. plant formation and maturation

3. grain maturation

Temperature [°C ]

Humidity [%]

PPFD [mol / m2 / sec]

Nutrients [pH]

Leaf area index [mm2 ]

LAI change [ mm2 / day ]

Spectral intensity [%]

CO2 [ppm]

…

…

Black box modelling

?inputs outputs

BBCH [1-100]

1. germination

2. plant formation and maturation

3. grain maturation

Temperature [°C ]

Humidity [%]

PPFD [mol / m2 / sec]

Nutrients [pH]

Permutations

Leaf area index [mm2 ]

LAI change [ mm2 / day ]

Spectral intensity [%]

CO2 [ppm]

sample every 15 min – 1 h

every BBCH stage

[4, 4.5, 5, … , 39.5, 40]

[0, 5, 10, … , 95, 100]

[0, 25, 50, … , 1725, 1750]

[4, 4.5, 5, … , 9.5, 10]

…

Rapid Plant Development Modelling System

• 4 microclimate zones

• 40+ sensors

• 8-12 simultaneous plants per device (4 devices)

• 2 years of experiments• 64-96 harvests per year

• 5000 microclimate permutation scenarios

• 6 mil. data entries of correlated inputs and outputs

Software architecture• 28 – Real-time controller

• 34-40 – Control loops for microclimate conditions

• 29 – Communication and interface controller

• 41-42, 47-48 – User interface algorithms

• 43-46 – Data processing algorithms

• from patent application P20191572A

models, learnings and potential

Predictive agriculture

Predictive agriculture• AI modules:

• yield prediction

• optimal fertilizertreatments scheduling

• optimal pests treatment scheduling

• optimal watering scheduling

• grain moisture prediction

• wheat behaviour in hypothetical climate (climate changes)

• etc.

Predictive agriculture• AI modules:

• yield prediction

• optimal fertilizertreatments scheduling

• optimal pests treatment scheduling

• optimal watering scheduling

• grain moisture prediction

• wheat behaviour in hypothetical climate (climate changes)

• etc.

• Emulated dataset

• Field measurements

• Data fusion (future work)

• Dataset and models publicly available

Getting smarter data

• interdisciplinary approach:• meetings and field visits, domain knowledge exchange, field workers, policy makers

• new opportunities (for AI) – pests, microclimate prediction, sensors, soil chemicals

• kernels (grains) are already shaped at BBCH 20-30 • significance of proportions

Behind the scenes…

Behind the scenes…

AgroSPARC

4 zones: temperature, humidity, lighting, watering, nutrients

40+ sensors

8-12 simultaneous plants per device

64-96 harvests per year

5000 microclimate scenarios

6 mil. data entries

AI models

Acknowledgement

Project Smart and Predictive Agriculture for Resilience to Climate Change is sufinanced by European Union Regional Development Fund under grant no. KK.05.1.1.02.0031.

Content of this publication is a sole responsibility of its authors

Projekt Napredna i prediktivna poljoprivreda za otpornost klimatskim promjenama sufinancirala je Europska unija iz Fonda za regionalni razvoj, br. KK.05.1.1.02.0031.

Sadržaj publikacije isključiva je odgovornost njezinih autora

Thank you!vinko.lesic@icent.hr