The potential of remote sensing in the Agribusiness

Transcription

1 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

2 Overview ILVO What is precision agriculture? Remote sensing & Agriculture Platforms Sensors Milieutechniek ICAReS agricultural remote sensing cases Current status & Future perspectives

3 Universities 620 Animal Sciences Social Sciences Plant Sciences Technology and Food Science Practice

4 European Agricultural Knowledge and Innovation Systems (AKIS) towards innovation-driven research in Smart Farming Technology. Cluster of companies active in innovative precision farming Internet of Food & Farm 2020 Large scale pilots

5 Overview ILVO What is precision agriculture? Remote sensing & Agriculture Platforms Sensors Milieutechniek ICAReS agricultural remote sensing cases Current status & Future perspectives

6 What is precision agriculture? A type of agriculture where plants and animals, very precisely, both in time and space, receive the treatment they require Why? Variation within one stable, one field,... Bron: Kempenaar C. Field of onions, 300 m x 75 m

7 What is precision agriculture? How precisely? Field Precision agriculture 1.0 Grid Precision agriculture 2.0 Plant Leaf Precision agriculture 3.0

8 What is precision agriculture? Uniform field treatment PA = Site specific application of inputs (fertilizers, seeds, plant protection products, irrigation, etc.) Why? SUSTAINABILITY More yield Less inputs Environment friendly Bron: John Deere

9 What is precision agriculture? Detection of variation COLLECTING DATA Y (m) MAPPING Straw yield (t/ha) Spring barley Boigneville, 1996 (project IN-SPACE) Grain yield (t/ha) : spring barley 6.4 ha, Boigneville (24/07/96) Intelligence harvest Crop growth X (m) Global positioning DATA ANALYSIS Soil cultivation Actuator SITE SPECIFIC APPLICATION advies Remote sensing! Crop protection Fertilisation Source W. Saeys, KU Leuven

10 Overview ILVO What is precision agriculture? Remote sensing & Agriculture Platforms Sensors Milieutechniek ICAReS agricultural remote sensing cases Current status & Future perspectives

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 far close Resolution! close far Bron: Kempenaar C.

12 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 Bron: Kempenaar C.

13 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.

14 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 Fritzmeier - Isaria Ultrasoonsensor voor spuitboomhoogte Bron: Kempenaar C.

15 Remote sensing & Agriculture - platforms Sensors in the field - Point measurement, practicability + continuous measurement EasyAg sensor bodemvochtigheid Bron: Kempenaar C.

16 Remote sensing & Agriculture - sensors Standard digitale camera + image processing + cheap, easy to use - Only sees what human eye can i.e. visible light ( nm) Spectrum Visible Light SWIR Infra Red LWIR Band Band Band Band Band Band Band s of Bands

17 Remote sensing & Agriculture - sensors Hyper/multispectral cameras (also measure invisible light ) Spectrum Visible Light SWIR Infra Red LWIR Spectrum Band Band Band Band Band Band Band Broadband to Multispectral 100s of Bands Hyperspectral Spectral signature for each pixel of the image More information from one image Plant characteristics Species recognition Source W. Saeys, Bron: KU Leuven Kempenaar C.

18 Remote sensing & Agriculture - sensors Hyperspectral camera s measure reflectance Biomass Water content Nitrogen status Weed detection Disease detecation Healthy plants: - Low reflection red (R670) - High reflection NIR (R800) Ratio: NDVI (biomass) NDVI = (R800 - R670)/(R800 + R670) Source Bron: Kempenaar C.

19 Remote sensing & Agriculture - sensors Hyperspectral cameras Disease detection at leaf level Mahlein et al. (2013) Remote Sensing of Environment, 128: Bron: Kempenaar C.

20 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

21 Overview ILVO What is precision agriculture? Remote sensing & Agriculture Platforms Sensors Milieutechniek ICAReS agricultural remote sensing cases Current status & Future perspectives

22 ICARES agric. remote sensing cases ILVO platform + cameras ICAReS cases Thermaal nog toevoegen? Emphasis on high temporal and spatial resolution Using new state of the art IMEC hyperspectral snapshot cameras

23 Exported potatoes (US$ million) ILVO ICARES agric. remote sensing cases Early disease detection in potatoes Annual production: 4 million tons Netherlands Belgium US Canada France Frozen potatoes Raw potatoes Bron: Kempenaar C.

24 ICARES agric. remote sensing cases Early disease detection in potatoes Why? Phytophthora infestans Alternaria solani Verticillium dahliae Pectobacterium carotovorum PVY Bron: Kempenaar C.

25 ICARES agric. remote sensing cases Early disease detection in potatoes Crop protection products (Coulier, 2008)

26 ICARES agric. remote sensing cases High value crop What?

27 ICARES agric. remote sensing cases Early disease detection in potatoes Hyperspectral sensors Cover to exclude sunlight Light source

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

29 ICARES agric. remote sensing cases Early disease detection in potatoes Legend: 1 Verticilium dahlia 2 Colletotrichum coccodes 3 PVY 4 Pectobacterium carotovorum 5 Globodera spp. 6 Phytophtora infestans 7 Alternaria solani 8 control

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

31 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 Thorn apple Toxic! black Nightshade toxic! Nut grass Dockweed Objectives More sustainable PPP use (site specific spraying, early detection, etc.) Safe food Better yields

32 ICARES agric. remote sensing cases Weed detection in grasland, maize and vegetables

33 Overview ILVO What is precision agriculture? Remote sensing & Agriculture Platforms Sensors Milieutechniek ICARES agricultural remote sensing cases Current status & Future perspectives

34 Current status & Future perspectives Drones are already used in Belgium to: Map soil colour and translate it to organic matter Emergence differences for evaluation of potatoes Height model (DEM-DTM) of the fields DEM=> shadow zones calculation and variable planting Biomass and nitrogen determination and adjustment of fertilization strategy Variable crop desiccation (potato variable spraying) Detect heterogeneity and problem patterns faster in order to perform more field visits in an intentional way Source : Jacob Van Den Borne -

35 Current status & Future perspectives 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.

36 Current status & Future perspectives Gaps & challenges Safety: Sense and avoid Battery time Law? Harmonisation! Transport? Spraying? Agriculture : applications not ready or extensively validated, management models? Transfer Knowledge & innovation to practice

37 Thank you for your attention! Contacts: Institute for Agricultural and Fisheries Research Burg. Van Gansberghelaan Merelbeke België T + 32 (0) F +32 (0)