Lecture by Xurxo Gago
•
0 j'aime•608 vues
Plant water assessment through aerial thermography A multicopter practical case in a vineyard
Recommandé
Recommandé
Contenu connexe
Similaire à Lecture by Xurxo Gago
Similaire à Lecture by Xurxo Gago (20)
Plus de Salvatore Manfreda
Plus de Salvatore Manfreda (20)
Dernier
Dernier (20)
Lecture by Xurxo Gago
- 1. Plant water assessment through aerial thermography A mul4-‐copter prac4cal case in a vineyard Xurxo Gago
- 3. SUMMER
- 4. «More crop per drop» 1-‐Agricultures consumes most of the world water resources (70%) 2-‐Another industries compite for the water 3-‐45% of food supply is produced in irrigated fields 4-‐Irrigated fields just cover 18% of total agriculture lands Doll & Siebert, 2002; Gilbert et al., 2012
- 5. Photosynthesis Stomatal conductance 0 2 1 Two main “ways” to improve Water Use Efficiency (WUE) M a x i m i z i n g photosynthesis G e n e t i c improvement & Biotechnology Regulated deficit irrigation Soil and crop management Irriga4on control , the quickiest way to improve on farm WUE
- 6. WHY gs? gs is a good indicator of plan water status and permit characterize the degree of stress Stomatal conductance (mmol H2O m-2 s-1) 0 100 200 300 400 AN(µmolCO2m -2 s-1 ) 0 2 4 6 8 10 12 14 16 18 Without water stress Moderate water stress Severe water stress gs > 150 mmol m-2s-1 50 < gs < 150mmol m-2s-1 gs < 50mmol m-2s-1 Medrano et al. 2002. Ann Bot. 89,895-‐905 Data of 10 years of measurements in pot and field plants of Manto negro and Tempranillo and in 22 cvs in pots
- 7. How to scale-‐up levels?
- 8. Engines MK3638 Li-‐Po BaFery 8Amp 30C Camera mount servo-‐stabilized carbon-‐fiber UAVEurope ® Mul4-‐copter 6 engines equipped with a thermal camera : Main parts Thermal camera GOBI384 Xenics®! Ubiquiwifi , on line wi-‐fi data streaming (Ubiqui[ Networks®) Propellers (APC 12x3,8 inc) Frame (carbon fiber Air-‐Sci UAVEurope®) Electronic systems: Mikrokopter®
- 9. Advantages and limita4ons of the different types for plant ecophysiology Parameter Wing-‐span planes Helicopters Mul4-‐copters Camera resolu4on Lower, + al4tude Higher, -‐ al4tude Higher, -‐ al4tude No hovering Hovering Hovering Mapping Wider Reduced Reduced Logis4cs Land-‐off requirements No requirements No requirements Exper4se ++ technical knowledge +++ technical knowledge Plug´n´play Flight ++ technical knowledge +++ technical knowledge User friendly
- 10. How to detect water stress from an UAV?
- 11. Reference UAVs type Crop Thermal Index gs (R2) Poten4al hydric (R2) Baluja et al. 2012 Wing-‐span fixed wing Grapevine Tcanopy – Tair 0.69 0.5 CWSI 0.68 0.5 IG 0.72 0.5 I3 0.5 0.42 Zarco-‐ Tejada et al. 2012 Wing-‐span fixed wing Citrus sinensis Temperature 0.78 0.34 González-‐ Dugo et al. 2013 Wingspan fixed-‐wing Almond Tc-‐Ta 0.67 Peach 0.92 Lemon 0.48 Orange 0.27 Apricot 0.64 Aerial thermography for water stress detec4on in crops Berni et al. 2009 González-‐Dugo et al. 2012
- 12. Thermal indexes… an aqempt to normalize the environment (Idso et al., 1980; Jones, 1999) CWSI = T canopy – Twet / Tdry – Twet IG = T dry – Tcanopy / Tcanopy – Twet I3= T canopy – Twet / Tdry – Tcanopy And the leaf energy balance: 𝑇↓𝑙 − 𝑇↓𝑎 = [ 𝑟↓𝐻𝑅 ( 𝑟↓𝑎𝑤 + 𝑟↓𝑠 )𝛾 𝑅↓𝑛𝑖 − 𝑝 𝑐↓𝑝 𝑟↓𝐻𝑅 𝐷]⁄{𝑝 𝑐↓𝑝 [𝛾( 𝑟↓𝑎𝑤 + 𝑟↓𝑠 ) + 𝑠 𝑟↓𝐻𝑅 ]} 𝑟↓𝑠 = − 𝑝 𝑐↓𝑝 𝑟↓𝐻𝑅 [𝑠( 𝑇↓𝑙 − 𝑇↓𝑎 )+ 𝐷] ⁄{𝛾 [( 𝑇↓𝑙 − 𝑇↓𝑎 )𝑝 𝑐↓𝑝 − 𝑟↓𝐻𝑅 𝑅↓𝑛𝑖 ]}− 𝑟↓𝑎𝑤 How to detect the drought from an UAV?
- 13. 𝑇↓𝑙 − 𝑇↓𝑎 = [ 𝑟↓𝐻𝑅 ( 𝑟↓𝑎𝑤 + 𝑟↓𝑠 )𝛾 𝑅↓𝑛𝑖 − 𝑝 𝑐↓𝑝 𝑟↓𝐻𝑅 𝐷]⁄{𝑝 𝑐↓𝑝 [𝛾( 𝑟↓𝑎𝑤 + 𝑟↓𝑠 ) + 𝑠 𝑟↓𝐻𝑅 ]} 𝑟↓𝑠 = − 𝑝 𝑐↓𝑝 𝑟↓𝐻𝑅 [𝑠( 𝑇↓𝑙 − 𝑇↓𝑎 )+ 𝐷] ⁄{𝛾 [( 𝑇↓𝑙 − 𝑇↓𝑎 )𝑝 𝑐↓𝑝 − 𝑟↓𝐻𝑅 𝑅↓𝑛𝑖 ]}− 𝑟↓𝑎𝑤 Leaf energy balance (Jones, 1992) + meteorological data = gs es4ma4on How to detect drought from an UAV?
- 14. More thermal arial results with an hexakopter DEM model by Agisov Photoscan -‐Flying al4tude: 30 m -‐Resolu4on : 1,4 cm/px -‐RMSE : 1,1 cm -‐2 cv under 3 irriga4on treatments Gago et al. 2013 a & b Gago et al. 2013 a & b Tempranillo Grenache WW D C C WW D
- 15. Plant truth-‐data at leaf level -‐Leaf water poten4al -‐Leaf gas-‐exchange measurements
- 16. More integra4ve plant truth-‐ data at stem-‐plant level -‐Stem Sap flow -‐Whole-‐plant chamber
- 17. Grenache, several days of campaign: thermal indices vs gs at leaf level CWSI IG Gs (mol H2O m-‐2 s-‐1) Date 1 Date 2 Date 3 D WW C
- 18. Grenache, several days of campaign: Tc-‐Ta and Canopy conductance vs gs at leaf level Tc-‐Ta (ºC) Gc (mol H2O m-‐2 s-‐1) Gs (mol H2O m-‐2 s-‐1) Date 1 Date 2 Date 3 D WW C
- 19. Tc-‐Ta and Canopy conductance vs midday water poten4al at leaf level in both cv Tc-‐Ta (ºC) Midday water poten4al (MPa) Tc-‐Ta (ºC) Tempranillo Grenache Date 1 Date 4
- 20. Grenache, several days of campaign: Tc-‐Ta and Canopy conductance vs sap flow at stem level Date 1 Date 2 Date 3 Tc-‐Ta (ºC) Gc (mol H2O m-‐2 s-‐1) Sapflow (l/h) Sapflow (l/h) Drought Watered
- 21. So… we have an es4ma4on of the transpira4on… but we also need to know the foliar area to can calculate the needed irriga4on WW D C Tempranillo Grenache WW D C R² = 0,9301 P<0.05 0 2 4 6 8 10 12 14 16 0 2 4 6 8 Canopy es4mated (m2) Plant-‐truth foliar area (m2 /plant) R² = 0,9497 P<0.05 0 2 4 6 8 Plant-‐truth foliar area (m2/plant) W D C Tempranillo Grenache
- 22. Environmental factors sensi[vity of the leaf energy balance model
- 23. Meteodruino : a micro-‐open-‐hardware meteorological sta[on for mul[-‐copters: -‐Piranometer Apogee SP110 Sensirion SHT 75 (Temp + HR (%) Collec4ng micro-‐ meteorological data close to the plants
- 24. : a micro-‐open-‐hardware meteorological sta[on for mul[-‐copters: Meteodruino calibra4on
- 25. : sampling around vines
- 26. -‐UAVs can assess crop water status through termography -‐Improve spa4al and temporal resolu4on than aircravs and satellites but cover minor areas -‐S4ll, all the process must be more «user-‐friendly» and automated to can be generalized for agriculture
- 27. Thanks And Good Fligths!!
- 28. Micro-DRONES for low-cost high-throughput phenotyping? Micro-drones for low-cost high-through-put phenotyping?
- 29. Micro-DRONES for low-cost high-throughput phenotyping? Micro-drones for low-cost high-through-put phenotyping?
- 30. TOSHI PLANTS