Using Unmanned Aerial Vehicles To Monitor Crop Nutrient and Water Stress at the Field Level

Transcription

1 Using Unmanned Aerial Vehicles To Monitor Crop Nutrient and Water Stress at the Field Level Bruno Basso and Ryan Nagelkirk Department of Geological Sciences and W.K. Kellogg Biological Station

2 Outline of the presentation Pro and Cons of UAV Available UAV Technologies Applications of UAVs in Precision Agriculture How to link UAV with crop modeling

3 Unmanned Aerial Vehicles According to AUVSI, following the FAA integration of UAVs into national airspace by Sept. 2015, UAVs will have $13.6 Billion impact on US economy (growth in precision agriculture) UAVs have many advantages but there is still work to do on them before they can be used directly by farmers

4 Pros and Cons of UAV in Agriculture UAVs Pros: - Response time - Precision - Resolution - User Control Cons: - Stability ( if windy) - Costs (hardware) - Regulations (COA) - Training Satellite Pros: - Processing time - Established - Availability - Coverage Cons: - Cloud cover - Lacks user control - Return time - Costs (images)

5 Published Applications of UAVs in Agriculture Detection of crop/tree chlorosis (Zarco-Tejada & Berni, 2008) Detection of water spills in irrigated orchards (Zarco-Tejada & Berni, 2008) Estimating leaf carotenoid content in vineyards (Zarco-Tejada et al., 2013) Leaf Area Index (Berni & Zarco-Tejada, 2009; Zarco-Tejada & Berni, 2008) Water stress (Berni & Zarco-Tejada, 2009; Suárez, 2008; Suárez, 2009; Zarco-Tejada & Berni, 2008; Zarco-Tejada et al., 2012) Chlorophyll content (Berni & Zarco, 2009; Zarco-Tejada & Berni, 2008; Uto et al., 2013) Canopy temperature (Zarco-Tejada & Berni, 2008) Remote sensing of N stress levels (Teoh & Hassan, 2012; Tremblay et al., 2011) Weed Management (Torres-Sánchez et al. 2013)

6 Two Two Common UAV Airframes Fixed-wing Rotorcraft Vertical Takeoff and Landing (VTOL) Grenzdörffer et al., 2008 Nebiker et al., 2008 Berni & Zarco-Tejada, 2009 Nebiker et al., 2008

7 Size Range Flight Time Altitude Payload Pictured: Large 500 km 2 days 3-20 km 11,000 kg NASA Ikhana Medium 500 km 10 hours 4 km 50 kg NASA SIERRA Small / mini 10 km 2 hours 1 km 5-30 kg Quest UAV Micro / nano 10 km 1 hour 250 m < 5 kg Microdrone Adapted from Anderson & Gaston, 2013

8 UAV model: mdr microdrones.com md is equipped with RGB digital video-camera, thermal camera, hyperspectral, laser scanner. Spatial resolution < 1 in (1-7cm) Payload 3 lbs, Flying time minute with 1 battery

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10 Sensors Visible Multispectral Tetracam Sony, microdrones microdrones Headwall Photonics Thermal (IR) Hyperspectral

11 Laser scanner imagery from UAV (microdrone md-1000)

12 Multispectral broad-band vegetation indices Mulla 2013

13 Hyperspectral narrow-band vegetation indices Mulla 2013

14 Barnes, et al., 2000; Fritzgerald et al., 2008; Cammarano, Basso et al., 2011

15 The chlorophyll vegetation indices Chlorophyll Indices Formula NDRE1 (NIR Combined - RE) / (NIR Chlorophyll + RE) Indices NDRE2 (RE - Red) / (RE NDRE1 + Red) / NDVI CRM NIR / RE - 1 CGM NIR / Green - 1 CVI MTCI CARI TCARI MCARI MCARI2 NDRE2 / NDVI NDRE1 / GC (NIR / Green) *(Red / Green) (NIR - RE) / (RE - Red) NDRE2 / GC TCARI / OSAVI (RE - Red) * (RE Green) MCARI / OSAVI 3 * ((RE - Red) * (RE - Green) * (RE / Red)) MCARI / MTVI ((RE - Red) * (RE - Green)) * (RE / Red) 1.5*(2.5*(NIR-Red) *(NIR-Green)) / ((2 * NIR +1)² -(6*NIR - 5*Red^0.5 - MCARI / MTVI2 0.5))^0.5 Structural Indices NDVI OSAVI MTVI MTVI2 GC Formula (NIR - Red) / (NIR + Red) (1+0,16)*(NIR-Red)/(NIR+RED+0,16) 1,5*(1,2*(NIR-Green)-2,5*(Red-Green))/((2*(2*NIR+1)^0.5-(6*NIR-5*(Red)^0.5)-0,5)) (1.5 * (1.2 * (NIR - Green) * (Red - Green))) / ( (2 * NIR + 1)^2 - (6 * NIR - 5 * (Red^0.5)) - 0.5)^0.5 Ground cover calculated with RapidEye method

16 The 2012 drought provided an opportunity to study spatial variability of rooting depth and yield NDWI-2 Airborne image taken early August 2012 Spatial resolution 15 cm Normalized Difference Water Index 2 NDWI2 =(Green-NIR)/(Green+NIR) Vegetation Index used to detect water content in plants 2012 Grain yield maps (bu/ac) Spatial resolution about 5 m2 Yield Mapping for different crops

17 Green Chlorophyll Index- GCI =(NIR/GREEN)-1 Vegetation Index used to estimate chlorophyll content NDWI2 =(Green-NIR)/(Green+NIR) Vegetation Index used to detect water content in plants

18 Thermal images over corn four different time of day Berni and Zarco-Tejada 2009

19 Photchemical reflectance index over corn PRI before irrigation in the morning (a) and at midday (b) Suárez et al., 2009 PRI after irrigation in the morning (c) and at midday (d)

20 Strategic and tactical N management using spatially explicit crop modeling Net Revenue ($ ha -1) High Yield Zone kg N ha -1 Medium Yield Zone Low Yield Zone 60 kg N ha kg N ha Nitrate Leaching (kg N ha -1 ) Dual criteria optimization through tested model determines the N rate that minimizes nitrate leaching and increases net revenues for farmers (Basso et al., 2011; Eur J. Agron 35: )

21 Conclusions UAVs will revolutionize data collection in agriculture and significantly improve the efficiency of input applications at the field/farm scale UAV need to be able to deliver maps that can be used by farmers to implement changes in their managament practices over space and time The integration of UAV with crop modeling is the key to understand a complex systems like crop production in space and time.