The potential of remote sensing in the Agribusiness Ruben Van De Vijver, Koen Mertens, Peter Lootens, David Nuyttens, Jürgen Vangeyte ILVO ICAReS – Innovations in remote sensing July 14, 2017 – Eastgate Conference Centre, Northfleet, UK
ILVO Overview • ILVO • What is precision agriculture? • Remote sensing & Agriculture Milieu- techniek – Platforms – Sensors • ICAReS agricultural remote sensing cases • Current status & Future perspectives
ILVO Universities Animal Plant Sciences Sciences Social 620 Technology Sciences and Food Science Practice
ILVO Europea ean Agric Agricultu tural l Kn Knowle ledge and d Innovati tion Sys Systems (A (AKIS) IS) tow owards innovatio tion-driven en resea esearch in Sm Smart t Farmin ing Tech Technolo logy. y. Cluster of companies active in innovative precision farming www.smartdigitalfarming.be Inte ternet t of of Foo ood d & & Farm 2020 2020 Large scal Larg cale e pi pilo lots ts
ILVO Overview • ILVO • What is precision agriculture? • Remote sensing & Agriculture Milieu- techniek – Platforms – Sensors • ICAReS agricultural remote sensing cases • Current status & Future perspectives
ILVO 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, ... • Bron: Kempenaar C. Field of onions, 300 m x 75 m
ILVO What is precision agriculture? • How precisely? Pre recision agri riculture 1.0 1.0 Field Grid Pre recision agri riculture 2.0 .0 Plant Pre recision agri riculture 3.0 .0 Leaf
ILVO What is precision agriculture? PA = Site specific application of inputs Uniform field treatment (fertilizers, seeds, plant protection products, irrigation, etc.) Why? SUSTAINABILITY More yi yield ld • Les Less inp nputs • En Environment frie friendly • Bron: : Joh John Deere eere
ILVO What is precision agriculture? Detectio ion MAPPING Inte In telli ligence of variation Straw yield (t/ha) Spring barley Boigneville , 1996 (project IN-SPACE) 150 3.6 COLLECTING DATA Y (m) 3.0 100 Grain yield (t/ha) : spring barley 6.4 ha, Boigneville (24/07/96) 2.6 50 9.00 • harvest 150.00 2.0 8.00 0 50 100 150 200 250 300 7.00 X (m) 100.00 6.00 5.00 50.00 4.00 DATA ANALYSIS 3.00 0.00 50.00 100.00 150.00 200.00 250.00 300.00 Glo lobal positio ionin ing • Crop growth Actu tuator advies • Soil cultivation SITE SPECIFIC APPLICATION Crop protection Remote se sensin ing! Fertilisation Source W. Saeys, KU Leuven
ILVO Overview • ILVO • What is precision agriculture? • Remote sensing & Agriculture Milieu- techniek – Platforms – Sensors • ICAReS agricultural remote sensing cases • Current status & Future perspectives
ILVO 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? far Satellite and aircraft – Unmanned Aerial Vehicles (UAV) – Resolution! Sensors on ground-based machinery & platforms – Sensors in the field – close cl close far Bron: Kempenaar C.
ILVO Remote sensing & Agriculture - platforms • 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 Bron: Kempenaar C.
ILVO Remote sensing & Agriculture - platforms • 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 example: monitoring crop damage Bron: Kempenaar C. www.aureaimaging.com
ILVO Remote sensing & Agriculture - platforms • 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 www.greenseeker.nl Fritzmeier - Isaria Ultra rasoonsensor vo voor spuitboomhoogte http:/ htt ://www.p .pepperl-fuchs.b s.be/ Bron: Kempenaar C.
ILVO Remote sensing & Agriculture - platforms • Sensors in the field - Point measurement, practicability + continuous measurement EasyAg sensor bodemvochtigheid www.sentek.com.au Bron: Kempenaar C.
ILVO Remote sensing & Agriculture - sensors Standard digitale camera + image processing + cheap, easy to use - Only sees what human eye can i.e. visible light (380-780 nm) LWIR Visible Light Spectrum SWIR Infra Red Band 1 Band 2 Band 3 Band 4 Band 5 Band 7 Band 6 .45-.52 .52-.60 .63-.69 .79-.90 1.55-1.75 2.08-2.35 10.4-12.4 100s of Bands
ILVO Remote sensing & Agriculture - sensors • Hyper/multispectral cameras (also measure ‘ invisible light’) LWIR Visible Light Spectrum SWIR Infra Red Spectrum Broadband to Band 1 Band 2 Band 3 Band 4 Band 5 Band 7 Band 6 .79-.90 1.55-1.75 2.08-2.35 .45-.52 .52-.60 .63-.69 10.4-12.4 Multispectral Hyperspectral 100s of Bands Spectral “signature” for each • pixel of the image More information from one • image Plant characteristics – Species recognition – Source W. Saeys, KU Leuven Bron: Kempenaar C.
ILVO Remote sensing & Agriculture - sensors • Hyperspectral camera’s measure reflectance Healthy plants: – Biomass - Low reflection red (R670) - High reflection NIR (R800) – Water content – Nitrogen status Ratio: NDVI (biomass) – Weed detection – Disease detecation NDVI = (R800 - R670)/(R800 + R670) Bron: Kempenaar C. Source www.agbusiness.ca
ILVO Remote sensing & Agriculture - sensors • Hyperspectral cameras – Disease detection at leaf level Bron: Kempenaar C. Mahlein et al. (2013) Remote Sensing of Environment, 128: 21-30
ILVO Remote sensing & Agriculture - sensors • 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,… Olive tree height measurement Escola et al., 2015
ILVO Overview • ILVO • What is precision agriculture? • Remote sensing & Agriculture Milieu- techniek – Platforms – Sensors • ICAReS agricultural remote sensing cases • Current status & Future perspectives
ILVO ICARES agric. remote sensing cases ILVO platform + cameras Thermaal nog ICAReS cases toevoegen? Emphasis on high temporal – and spatial resolution Using new state of the art – IMEC hyperspectral snapshot cameras
ILVO ICARES agric. remote sensing cases • Early disease detection in potatoes 2500 2000 Exported potatoes (US$ million) Annual production: 4 million tons 1500 1000 500 0 Netherlands Belgium US Canada France Frozen potatoes Raw potatoes Bron: Kempenaar C.
ILVO ICARES agric. remote sensing cases • Early disease detection in potatoes Why? Phytophthora infestans Alternaria solani Verticillium dahliae Pectobacterium carotovorum Bron: Kempenaar C. PVY
ILVO ICARES agric. remote sensing cases • Early disease detection in potatoes Cr Crop pro rotectio ion pro roducts (Coulier, 2008)
ILVO ICARES agric. remote sensing cases What? Hig igh val alue cro crop
ILVO ICARES agric. remote sensing cases • Early disease detection in potatoes Hyperspectral sensors Cover Light to exclude source sunlight
ILVO ICARES agric. remote sensing cases • Early disease detection in potatoes
ILVO ICARES agric. remote sensing cases • Early disease detection in potatoes 7 4 8 3 2 1 5 6 5 3 6 2 8 4 1 7 8 6 1 4 7 5 3 2 2 1 7 5 3 6 4 8 Legend: 1 – Verticilium dahlia 5 – Globodera spp. 2 – Colletotrichum coccodes 6 – Phytophtora infestans 3 – PVY 7 – Alternaria solani 4 – Pectobacterium carotovorum 8 – control
ILVO ICARES agric. remote sensing cases • Early disease detection in potatoes
ILVO ICARES agric. remote sensing cases • Weed detection in grasland, maize and vegetables – Early detection of economically important problem weeds in grassland, maize and vegetables – Selected weeds Thistle Bindweed Jimson black Nut grass Dockweed Thorn apple Nightshade Toxic! toxic! Objectives – More sustainable PPP use (site specific spraying, early detection, etc.) – Safe food – Better yields
ILVO ICARES agric. remote sensing cases • Weed detection in grasland, maize and vegetables 16-07-2016 26-07-2016 01-08-2016 09-08-2016
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