Plant Phenotyping and interpretation of 3D data · 2015-03-19 · Plant Phenotyping and...

Elixir - Tech Track - Plant Phenotyping 17-3-2015

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Plant Phenotyping and

interpretation of 3D data

Rick van de Zedde

March 18th and 19th, 2015

ELIXIR Innovation and SME Forum: Data-driven innovation in the agri-food industries

Introduction

Rick van de Zedde, senior researcher/ business developer Computer Vision for 11 years at Wageningen University & Research centre in The Netherlands.

Background:

Artificial Intelligence, University of Groningen.

Focus: computer vision/ robotics

Contact: [email protected]


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Wageningen University & Research Centre

A university plus R&D organisations, mission statement;

“To explore nature and to improve the quality of life”.

Wageningen UR:

● 6.500 employees

● 8.000 students

● 1.900 PhD’s

● 106 countries

Wageningen UR - campus

Wageningen UR - Phenomics

- Chlorophyll fluorescence – Elisa Gorbe Sanchez

XY-table for phenotyping in a climate chamber

- NIR/ VIS hyperspectral – Franck Golbach, Gerrit Polder

- Roots in 3D (X-ray CT) – Marcel Meinders, Erik Esveld

- PhenoBot - Gerrit Polder, Marco Bink, Fred v. Eeuwijk

- MARVIN/ EPPN/ EU-COST action – Rick van de Zedde

- Field phenotyping – Erik Pekkeriet, Pieter Blok

www.phenomics.nl

http://www.phenomics.nl/


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Overview

Introduction

Wageningen UR – Phenomics

EPPN – WP3 ‘Novel sensors’

● 3D reconstruction of seedlings

● Young plants

Vision-guided robotics

Summing up/ future work

Plant phenotyping and food production

Challenge: produce food for 9 billion in 2050

Focus on improvement of crops (maize, rice, potatoes, tomatoes, etc).

Novel genotyping technologies ‘deliver’ new varieties much quicker;

● Faster and less expensive DNA sequencers

● More efficient breeding cycles (GM and ‘classical’)

Plants still need to be grown to determine yield, resistance against heat/drought stress, diseases , etc. (= phenotyping).

So an increase of capacity/ objectivity is required:

Opportunity for automated inspection, robotics, big data.



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European Plant Phenotyping Network (EPPN) – 5.5 M€

Goals:

1. Create a European integrated network/ community

2. Offer trans national access to EPPN facilities

3. Research – a. Novel sensors, b. Good practice phenotyping c. IT for high throughput

Website: www.plant-phenotyping-network.eu Wageningen UR is WP leader of WP3 Novel sensors

Grant Agreement No. 284443.

Overview

Introduction




● Young plants



http://www.plant-phenotyping-network.eu/






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MARVIN – 3D based seedling sorting

Rick van de Zedde (PL), Nicole Koenderink, Gerwoud Otten, Franck Golbach, Mari Wigham, ea.

Specs, Scale and speed

Current capacity: 19.500 seedlings/ hour is 185 ms/ seedling

10 GigE industrial machine vision camera’s, hardware triggered.

3D reconstruction technique: shape-from-silhouette/ volumetric

intersection which requires a very accurate 3D camera calibration.

Software runs on a fast Windows 7 desktop computer;

National Instruments Labview/ core engine using CINs (C/C++).

Database: SQL server/ .Net web-interface

Raw data collection issue - ±5 seedlings per second =

10 camera’s x 5 per second x 1.2MB per image = 60 MB / second

216 Gb / hour .... saving 3D models only = 0.5 Gb / hour


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Illustration of volumetric intersection

Figure 3: Six of 24 images of the used watering can.

6 camera views from a watering can


Principle of volumetric intersection

S C

U O B N

M F I I T D

T E E N D T I

A L

(a)

(b)

1 camera More camera’s results in more detail



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Volumetric intersection: cube with voxels



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Plant/ seedling phenotyping

Examples of strange phenomena:


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Individual tray info

3D measurements and reference method

Features:

Stem length

Leaf length/ width

# leaves

Leaf type (cotyledon/ true leaf)


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3D models and colour projection

Overview

Introduction




● Young plants




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Height:

4 m


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Plant architecture: - Biomass estimation - Length main stem - # side stems - Leaf angle/ length - Leaf shape - Internode length(s) - Individual leaf areas

Overview

Introduction




● Young plants




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Vision-guided robotics - planting rose cuttings

Goal: propagating roses from cuttings


Robot info - Cutting point & approach point


Specs: - Calculations within 500ms - Correct cutting point in > 95% in large test set


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Cut rose planting robot example


Robotic pot rose cutting planter

Rick van de Zedde (PL), Gerwoud Otten, Franck Golbach, Sanja Damjanovic, ea.


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Overview

Introduction




● Young plants



Summing up: accuracy and high throughput

Tough specs: accurate 3D models within max. 500ms

Focus on high-throughput phenotyping - so no turntable.

Multi-camera advantages:

- Single shot (one trigger = 10 camera images)

- Individual plants imaged on a fast moving conveyer belt



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Future work

Validation of the measured 3D traits using reference measurements

Using 3D for subtle colour differences on irregular surfaces.

Quantification of plant architecture of larger plants

Comparison of 3D methods of several partners within EPPN

We are interested in setting up a research collaboration


Acknowledgements

My colleagues at Wageningen UR:

● Gerwoud Otten

● Franck Golbach

● Gerrit Polder

● Nicole Koenderink

● Sanja Damjanovic

● Mari Wigham

● Gert Kootstra

All colleagues within EPPN


Plus industrial partners:

Enza (M. Klooster):

IsoGroup (P. Oomen):

WPK (E. vd Arend):

EU project:


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Questions ?

Thank you for

your attention!

Questions?

Contact:

[email protected]

Next slide = EU COST

http://greenvision.wur.nl/

Plant Phenotyping and interpretation of 3D data · 2015-03-19 · Plant Phenotyping and...

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