Introduction Rick van de Zedde, business developer/ project leader - - PDF document

▶
introduction
SMART_READER_LITE
LIVE PREVIEW

Introduction Rick van de Zedde, business developer/ project leader - - PDF document

Aug 13, 2022 378 likes •630 views

28-9-2014 Agrofood Robotics and Automation: From Farm to Table IEEE RAS TC on Agricultural Robotics and Automation Webinar #021 (AgRa) Rick van de Zedde Wageningen UR 26 th of September 2014 Introduction Rick van de Zedde, business

slide-1
SLIDE 1

28-9-2014 1

Agrofood Robotics and Automation: From Farm to Table

Rick van de Zedde

Wageningen UR – 26th of September 2014 IEEE RAS TC on Agricultural Robotics and Automation Webinar #021 (AgRa)

Introduction

  • Rick van de Zedde, business developer/ project leader

for 10 years at Wageningen University & Research centre in The Netherlands

  • Background:

Artificial Intelligence, University of Groningen. Focus: computer vision/ robotics

Contact: rick.vandezedde@wur.nl

slide-2
SLIDE 2

28-9-2014 2

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 - GreenVision

  • GreenVision - the Wageningen UR centre of expertise on

computer vision.

  • Introduce new technology / scientific novelties into the agrifood

industry together with industrial partners.

  • 25 computer vision researchers within Wageningen UR.

Coordinated by: Rick van de Zedde, Erik Pekkeriet, Gert Kootstra and Gerrit Polder

  • One of the largest computer vision research groups in the

(Dutch) agrifood industry.

http://greenvision.wur.nl

Contact: rick.vandezedde@wur.nl

slide-3
SLIDE 3

28-9-2014 3

Outline

Food inspection: from farm to table

  • Farm/ breeding/ phenotyping
  • Post-harvest quality inspection
  • Packaging and robotics
  • Retail
  • Future perspective of R&D

Contact: rick.vandezedde@wur.nl

Outline

Food inspection: from farm to table

  • Farm/ breeding/ phenotyping
  • Post-harvest quality inspection
  • Packaging and robotics
  • Retail
  • Future perspective of R&D

Contact: rick.vandezedde@wur.nl

slide-4
SLIDE 4

28-9-2014 4

Mechanical intra-row weeders

Erik Pekkeriet (PL), Pieter Klop, Jochen Hemming, ea.

MARVIN – 3D based seedling sorting

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

slide-5
SLIDE 5

28-9-2014 5

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

slide-6
SLIDE 6

28-9-2014 6

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.

Contact: rick.vandezedde@wur.nl

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.
slide-7
SLIDE 7

28-9-2014 7

slide-8
SLIDE 8

28-9-2014 8

slide-9
SLIDE 9

28-9-2014 9

Vision-guided robotics

Goal: propagating roses from cuttings 3D reconstruction and plant architecture.

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

slide-10
SLIDE 10

28-9-2014 10

Rose robot – automated planting Wageningen UR – Phenobot

Gerrit Polder (PL), Fred van Eeuwijk, Marco Bink, Gerie van der Heijden, ea.

slide-11
SLIDE 11

28-9-2014 11

Outline

Food inspection: from farm to table

  • Farm/ breeding/ phenotyping
  • Post-harvest quality inspection
  • Packaging and robotics
  • Retail
  • Future perspective of R&D

Contact: rick.vandezedde@wur.nl

Post-harvest quality inspection

  • Automation in fruit/ vegetable production is widely used.
  • Apple, oranges, mango’s, etc. using optical sorters.
  • Individual products are analysed and graded.
  • Common inspection method: several images of one

product while continuously rotating on ‘wheels’. Alternative approach: 3D reconstruction and shape analysis developed for bell peppers (irregular shapes)

Contact: rick.vandezedde@wur.nl

slide-12
SLIDE 12

28-9-2014 12

Laser triangulation: shape measurements

Contact: rick.vandezedde@wur.nl

3D shape analyses – multiple views

Result of 6 sides Result based on one view Feature calculation like: Shape - identify middle

  • f lobes

Shape analysis: Block, point or ... shape

Contact: rick.vandezedde@wur.nl

slide-13
SLIDE 13

28-9-2014 13

3D shape analyses of bell peppers

  • Quality criteria:
  • Length/width
  • Diameter
  • Number of lobes
  • Shape regularity
  • Curvature

NB: additional sensors required for colour/ defects.

Contact: rick.vandezedde@wur.nl

Lettuce handling with robots

  • Kolen orienteren video hier toevoegen

Contact: rick.vandezedde@wur.nl

slide-14
SLIDE 14

28-9-2014 14

Outline

Food inspection: from farm to table

  • Farm/ breeding/ phenotyping
  • Post-harvest quality inspection (2)
  • Packaging and robotics
  • Retail
  • Future perspective of R&D

Contact: rick.vandezedde@wur.nl

Optical bulk sorting

Contact: rick.vandezedde@wur.nl

slide-15
SLIDE 15

28-9-2014 15

Video - impression

Franck Golbach (PL), Gerwoud Otten, Roeland v. Batenburg, ea.

Recording quality in high-speed mode

Contact: rick.vandezedde@wur.nl

slide-16
SLIDE 16

28-9-2014 16

Specs

  • Detection and reject of products based on
  • Colour defects (brown/ yellow/ green spots)
  • Shape (length, width, curvature, complex shapes).
  • Capacity:
  • Conveyor belt speed up to 5 m/s
  • 10.000 objects/ second, monitored with 4 camera’s.
  • 20 - 30 tons per hour (with French fries) = 3 truck loads
  • 4 linescan camera’s (2k pixels)/ Matrox Solios eCL framegrabbers/

PC-cluster with windows 7 plus Linux

  • Patented ‘intelligent puffing’ product removal– shape based reject

Contact: rick.vandezedde@wur.nl

Hyperspectral / NIRS

  • Measurement of quality aspects such as:
  • Diseases/ product quality
  • Moisture/ starch content
  • Foreign materials in bulk streams
  • Non-destructive and very fast (1–25 ms)
  • Hardware:
  • Spectrometers (260 – 2500 nm)
  • Hyperspectral line-scan NIR camera

for bulk sorting applications.

Contact: rick.vandezedde@wur.nl

slide-17
SLIDE 17

28-9-2014 17

Hyperspectral NIR camera

  • Near InfraRed linescan camera (940 – 1790 nm)
  • Xenics XEVA-343 xc104 - Specim N17E30 μm slit.
  • Spectral resolution: 256 pixels - 3.3 nm/ pixel.
  • Spatial resolution: 320 pixels.
  • 100 frames (lines)/ second.

NB: multispectral RGBi camera’s have colour channels from 400-700 nm and a near-infrared (NIR) channel at 750-900nm.

Contact: rick.vandezedde@wur.nl

Hyperspectral imaging

  • Consider hyperspectral imaging when

product and defects have:

  • No density difference (no x-ray)
  • No clear colour differences (no RGB)
  • Quantitative measurements (ie. moisture content, fat content,

inner decay)

  • Warnings:
  • Expensive hardware - InGaAs sensor instead of CCD/CMOS.
  • Sensitive hardware - calibration/ humidity/ damage.
  • Training set should cover all ‘real-life’ occurrences. NB:
  • Seasonal differences will change the product
  • Robust for several varieties of the product.
  • Sensitive for the relevant range within product?

Contact: rick.vandezedde@wur.nl

slide-18
SLIDE 18

28-9-2014 18

Outline

Food inspection: from farm to table

  • Farm/ breeding/ phenotyping
  • Post-harvest quality inspection
  • Packaging and robotics
  • Retail
  • Future perspective of R&D

Contact: rick.vandezedde@wur.nl

  • Food processing factories have to be flexible:
  • Large number of products and packaging variations
  • Small batches
  • Retailers place theirs orders late

Food processing industry

Contact: rick.vandezedde@wur.nl

slide-19
SLIDE 19

28-9-2014 19

  • To meet increasing demands:
  • Enormous amount of manual labour
  • People are flexible
  • Robots/machines are not flexible (yet)

Food processing industry

Contact: rick.vandezedde@wur.nl

EU-project - PicknPack

  • Large-scale EU funded

research project

  • Coordinator:

Wageningen UR (Erik Pekkeriet)

  • Budget: 14 M€
  • Consortium:

14 universities, research institutes, companies incl. retail. www.picknpack.eu

Reducing manual labour in quality assessment and packaging of food products.

Contact: rick.vandezedde@wur.nl

slide-20
SLIDE 20

28-9-2014 20 Flexible robotic systems for automated adaptive packaging of fresh and processed food products

Sensing module Robot module Packaging module

PicknPack – demonstrator

  • PicknPack demonstrator
  • Pick-and-place demo
  • f vine tomatoes
  • Dedicated gripper
slide-21
SLIDE 21

28-9-2014 21

Outline

Food inspection: from farm to table

  • Farm/ breeding/ phenotyping
  • Post-harvest quality inspection
  • Packaging and robotics
  • Retail
  • Future perspective of R&D

EasyFlow – automated check-out

Concept: Supermarket with no barcodes. Fully automated check-out to identify 30,000 different products. Solution Integration of machine learning based on multiple sensors using: computer vision, NIR technology, weight and statistics. Continuous self learning – shared with other Easyflow check-outs Result Hitrate 99%, better than a human cashier. Benefits: less personnel costs, less fraud, better and faster service for supermarket customers.

slide-22
SLIDE 22

28-9-2014 22

ITAB – Match X project

Nicole Koenderink (PL), Don Willems, Roeland vBatenburg, ea.

Outline

Food inspection: from farm to table

  • Farm/ breeding/ phenotyping
  • Post-harvest quality inspection
  • Packaging and robotics
  • Retail
  • Future perspective of R&D
slide-23
SLIDE 23

28-9-2014 23

Future perspective of R&D

  • Focus on automation of plant phenotyping activities
  • Ultra high-throughput phenotyping machines
  • Complex plant features, ie. 3D shape, # leaves, etc.
  • Robotics to replace manual labour
  • Big data hardware/ techniques to analyse results.
  • Increase of machine learning in agrifood for
  • Quality grading of food products based on complex

combinations of features (sensor fusion).

  • To optimize device parameters – not all operators get the

‘max’ out of their machine.

Future perspective of R&D

  • An increase in vision guided robotics developments in agrifood

industry, driven by :

  • Only alternative for Europe/ North-America to compete

as a ‘production location’.

  • Price of hardware is decreasing - quicker ROI .
  • Need for objective and stable product quality
  • Need for more production capacity and flexibility
  • Lack of skilled and motivated manual labour
slide-24
SLIDE 24

28-9-2014 24

Acknowledgement

  • Colleagues at Wageningen UR:
  • Erik Pekkeriet
  • Eldert van Henten
  • Gerrit Polder
  • Gerwoud Otten
  • Franck Golbach
  • Nicole Koenderink
  • Sanja Damjanovic
  • Mari Wigham
  • Gert Kootstra

Contact: rick.vandezedde@wur.nl

Plus industrial partners Lacquey (R. vd Linde): Optiserve (A. v Kasteren): Enza (M. Klooster): IsoGroup (P . Oomen): WPK (E. vd Arend): ITAB (J. Möller):

EU projects:

Thank you for your attention! Questions? More info: http://greenvision.wur.nl/

Contact: rick.vandezedde@wur.nl