The potential of remote sensing in the Agribusiness Ruben Van De - - PowerPoint PPT Presentation

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The potential of remote sensing in the Agribusiness Ruben Van De - - PowerPoint PPT Presentation

Feb 18, 2023 279 likes •662 views

The potential of remote sensing in the Agribusiness Ruben Van De Vijver, Koen Mertens, Peter Lootens, David Nuyttens, Jrgen Vangeyte ILVO ICAReS Innovations in remote sensing July 14, 2017 Eastgate Conference Centre, Northfleet, UK

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The potential of remote sensing in the Agribusiness

Ruben Van De Vijver, Koen Mertens, Peter Lootens, David Nuyttens, Jürgen Vangeyte

ICAReS – Innovations in remote sensing July 14, 2017 – Eastgate Conference Centre, Northfleet, UK

ILVO

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SLIDE 2

Milieu- techniek

Overview

  • ILVO
  • What is precision agriculture?
  • Remote sensing & Agriculture

–Platforms –Sensors

  • ICAReS agricultural remote sensing cases
  • Current status & Future perspectives

ILVO

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SLIDE 3

Animal Sciences Plant Sciences Social Sciences Technology and Food Science

Universities Practice

620

ILVO

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SLIDE 4

ILVO

Europea ean Agric Agricultu tural l Kn Knowle ledge and d Innovati tion Sys Systems (A (AKIS) IS) tow

  • wards innovatio

tion-driven en resea esearch in Sm Smart t Farmin ing Tech Technolo logy. y. Inte ternet t of

  • f Foo
  • od

d & & Farm 2020 2020 Larg Large scal cale e pi pilo lots ts

Cluster of companies active in innovative precision farming www.smartdigitalfarming.be

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SLIDE 5

Milieu- techniek

Overview

  • ILVO
  • What is precision agriculture?
  • Remote sensing & Agriculture

–Platforms –Sensors

  • ICAReS agricultural remote sensing cases
  • Current status & Future perspectives

ILVO

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SLIDE 6

What is precision agriculture?

  • A type of agriculture where plants and animals, very pre

recisely, both in tim time and sp space, receive the tre treatment they require

  • Why? Vari

riation within one stable, one field, ...

Field of onions, 300 m x 75 m Bron: Kempenaar C.

ILVO

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SLIDE 7

What is precision agriculture?

  • How precisely?

Field Grid Plant Leaf

Pre recision agri riculture 1.0 1.0 Pre recision agri riculture 2.0 .0 Pre recision agri riculture 3.0 .0

ILVO

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What is precision agriculture?

Bron: : Joh John Deere eere

Uniform field treatment PA = Site specific application of inputs (fertilizers, seeds, plant protection products, irrigation, etc.)

Why? SUSTAINABILITY

  • More yi

yield ld

  • Les

Less inp nputs

  • En

Environment frie friendly ILVO

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SLIDE 9

What is precision agriculture?

  • harvest
  • Crop growth
  • Soil cultivation
0.00 50.00 100.00 150.00 200.00 250.00 300.00 50.00 100.00 150.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 Grain yield (t/ha) : spring barley 6.4 ha, Boigneville (24/07/96) 50 100 150 200 250 300 X (m) 50 100 150 Y (m) 2.0 2.6 3.0 3.6

Straw yield (t/ha) Spring barley Boigneville , 1996 (project IN-SPACE)

COLLECTING DATA MAPPING DATA ANALYSIS

advies Fertilisation

SITE SPECIFIC APPLICATION

Detectio ion

  • f variation

In Inte telli ligence Actu tuator Glo lobal positio ionin ing

Source W. Saeys, KU Leuven

Remote se sensin ing!

Crop protection

ILVO

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SLIDE 10

Milieu- techniek

Overview

  • ILVO
  • What is precision agriculture?
  • Remote sensing & Agriculture

–Platforms –Sensors

  • ICAReS agricultural remote sensing cases
  • Current status & Future perspectives

ILVO

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SLIDE 11

Remote sensing & Agriculture - platforms

  • What?

– Quantitative measurement of variations in soil (nutrient status, moisture content, temperature, etc.) and crop characteristics (stress, growth, yield, diseases, weeds, etc.)

  • How?

– Satellite and aircraft – Unmanned Aerial Vehicles (UAV) – Sensors on ground-based machinery & platforms – Sensors in the field

Bron: Kempenaar C.

far close Resolution! cl close far

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SLIDE 12

Bron: Kempenaar C.

  • Satellite & aircraft

+ Always operational (satellites) + Great coverage

  • Cloud cover
  • Off-line detection: number of days between detection and action
  • Low frequency (some days)
  • Resolution satellite: ± 10 m, aircraft: ± 1 m
  • Sentinel-2 satellite (launched 2015)
  • Multispectral camera: 13 spectral bands VIS  NIR - SWIR
  • Every 5 days full earth coverage

www www.s .sate telli lietb tbeeld ld.n .nl

Remote sensing & Agriculture - platforms

ILVO

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  • UAV/Drones

– Fixed wing or multi-rotor + Flies under the clouds + higher resolution (cm-mm) + Coverage: up to 1000 ha/day (fixed wing) – Price: some tens of €per ha per flight – Legal permission + flight certificate

Bron: Kempenaar C. www.aureaimaging.com

example: monitoring crop damage

Remote sensing & Agriculture - platforms

ILVO

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  • Sensors on ground-based machinery & platforms

+ Precision: cm/mm + Direct coupling with actuator is possible

  • Difficult to integrate ≠ data sources
  • Robustness, dust, vibration, limited field zone monitored

Bron: Kempenaar C.

Ultra rasoonsensor vo voor spuitboomhoogte htt http:/ ://www.p .pepperl-fuchs.b s.be/

www.greenseeker.nl Fritzmeier - Isaria

Remote sensing & Agriculture - platforms

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  • Sensors in the field
  • Point measurement, practicability

+ continuous measurement

Bron: Kempenaar C. EasyAg sensor bodemvochtigheid www.sentek.com.au

Remote sensing & Agriculture - platforms

ILVO

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Standard digitale camera + image processing

+ cheap, easy to use

  • Only sees what human eye can i.e. visible light (380-780 nm)

Spectrum

100s of Bands Band 1 .45-.52 Band 2 .52-.60 Band 3 .63-.69 Band 4 .79-.90 Band 5 1.55-1.75 Band 7 2.08-2.35 Band 6 10.4-12.4 Visible Light SWIR Infra Red LWIR

Remote sensing & Agriculture - sensors

ILVO

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  • Hyper/multispectral cameras (also measure ‘invisible light’)

Bron: Kempenaar C. Source W. Saeys, KU Leuven

Spectrum

100s of Bands Band 1 .45-.52 Band 2 .52-.60 Band 3 .63-.69 Band 4 .79-.90 Band 5 1.55-1.75 Band 7 2.08-2.35 Band 6 10.4-12.4

Hyperspectral Broadband to Multispectral

Visible Light SWIR Infra Red LWIR

Spectrum

  • Spectral “signature” for each

pixel of the image

  • More information from one

image

– Plant characteristics – Species recognition

ILVO

Remote sensing & Agriculture - sensors

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  • Hyperspectral camera’s measure reflectance

– Biomass – Water content – Nitrogen status – Weed detection – Disease detecation

Bron: Kempenaar C. Source www.agbusiness.ca

NDVI = (R800 - R670)/(R800 + R670)

Healthy plants:

  • Low reflection red (R670)
  • High reflection NIR (R800)

Ratio: NDVI (biomass)

Remote sensing & Agriculture - sensors

ILVO

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SLIDE 19

Bron: Kempenaar C.

Mahlein et al. (2013) Remote Sensing of Environment, 128: 21-30

  • Hyperspectral cameras

– Disease detection at leaf level

Remote sensing & Agriculture - sensors

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  • Thermal cameras

– Detection of crop stress (drought, disease, etc.), soil water content, etc.

  • Laser scanners (e.g. Lidar)

– Scans the with a pulsed laser beam and the reflection time of the signal from the object back to the detector is measured – Applications: crop height measurement, tree characterization,…

Remote sensing & Agriculture - sensors

Olive tree height measurement Escola et al., 2015

ILVO

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Milieu- techniek

Overview

  • ILVO
  • What is precision agriculture?
  • Remote sensing & Agriculture

–Platforms –Sensors

  • ICAReS agricultural remote sensing cases
  • Current status & Future perspectives

ILVO

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SLIDE 22

ICARES agric. remote sensing cases

ILVO platform + cameras Thermaal nog toevoegen?

ILVO

ICAReS cases

– Emphasis on high temporal and spatial resolution – Using new state of the art IMEC hyperspectral snapshot cameras

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SLIDE 23

Bron: Kempenaar C.

  • Early disease detection in potatoes

ICARES agric. remote sensing cases

ILVO 500 1000 1500 2000 2500 Netherlands Belgium US Canada France Exported potatoes (US$ million) Frozen potatoes Raw potatoes

Annual production: 4 million tons

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Bron: Kempenaar C.

  • Early disease detection in potatoes

ICARES agric. remote sensing cases

ILVO

Why?

Verticillium dahliae PVY Pectobacterium carotovorum Alternaria solani Phytophthora infestans

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  • Early disease detection in potatoes

ICARES agric. remote sensing cases

ILVO

(Coulier, 2008)

Cr Crop pro rotectio ion pro roducts

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ICARES agric. remote sensing cases

ILVO

Hig igh val alue cro crop

What?

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Light source Hyperspectral sensors Cover to exclude sunlight

ICARES agric. remote sensing cases

ILVO

  • Early disease detection in potatoes
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ICARES agric. remote sensing cases

ILVO

  • Early disease detection in potatoes
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7 5 8 2 4 3 6 1 8 6 1 7 3 2 4 5 2 8 7 3 1 4 5 6 5 1 3 4 6 7 2 8 Legend: 1 – Verticilium dahlia 2 – Colletotrichum coccodes 3 – PVY 4 – Pectobacterium carotovorum 5 – Globodera spp. 6 – Phytophtora infestans 7 – Alternaria solani 8 – control

ICARES agric. remote sensing cases

ILVO

  • Early disease detection in potatoes
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ICARES agric. remote sensing cases

ILVO

  • Early disease detection in potatoes
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ICARES agric. remote sensing cases

ILVO

  • Weed detection in grasland, maize and vegetables

– Early detection of economically important problem weeds in grassland, maize and vegetables – Selected weeds

Thistle Bindweed Jimson Thorn apple Toxic! black Nightshade toxic! Nut grass Dockweed

Objectives – More sustainable PPP use (site specific spraying, early detection, etc.) – Safe food – Better yields

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16-07-2016 26-07-2016 01-08-2016 09-08-2016

ICARES agric. remote sensing cases

ILVO

  • Weed detection in grasland, maize and vegetables
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Milieu- techniek

Overview

  • ILVO
  • What is precision agriculture?
  • Remote sensing & Agriculture

–Platforms –Sensors

  • ICARES agricultural remote sensing cases
  • Current status & Future perspectives

ILVO

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SLIDE 34

Drones are already used in Belgium to:

  • Map soi

soil col colour and translate it to organic matter

  • Em

Emergence differences for evaluation of potatoes

  • Hei

eight model (DEM-DTM) of the fields

  • DEM=> shadow zones calculation and va

vari riable le plantin ing

  • Biomass and ni

nitrogen determination and adjustment of fertilization strategy

  • Vari

riable le cro crop des esic iccatio ion (potato variable spraying)

  • Detect he

heterogeneit ity and pro roble lem patterns faster in order to perform more field visits in an intentional way

Source : Jacob Van Den Borne - http://www.vandenborneaardappelen.com/

Current status & Future perspectives

ILVO

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Precision agriculture 3.0: future?

  • = plant specific treatment (0,1 m² and smaller)
  • Most cost saving opportunities
  • Treatment of individual plants only possible with

‘robots’? (smartbots)

  • R&D needed

– Sensor technology – Decision models – Drone observations -> how to use the data?

Bron: Kempenaar C.

Current status & Future perspectives

ILVO

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SLIDE 36

Gaps & challenges

  • Safety: Sense and avoid
  • Battery time
  • Law? Harmonisation!

Transport? Spraying?

  • Agriculture : applications not

ready or extensively validated, management models?

  • Transfer Knowledge & in

innovatio ion to practice

Current status & Future perspectives

ILVO

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Thank you for your attention!

Contacts: ruben.vandevijver@ilvo.vlaanderen.be, jurgen.vangeyte@ilvo.vlaanderen.be, peter.lootens@ilvo.vlaanderen.be, david.nuyttens@ilvo.vlaanderen.be, koen.mertens@ilvo.vlaanderen.be

Institute for Agricultural and Fisheries Research

  • Burg. Van Gansberghelaan 115

9820 Merelbeke – België T + 32 (0)9 272 28 00 F +32 (0)9 272 28 01 www.ilvo.vlaanderen.be

ILVO