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Unmanned Aerial Vehicles:Aerial Survey based modelling for crop
loss estimation
Uttam Kumar
CIMMYT/BISA
Ludhiana
Workshop on “Opportunities in the New Pradhan Mantri Fasal Bima Yojana (PMFBY) Indian Agricultural Insurance Program” December 21st 2016 New Delhi
Expected causes of crop failure/loss
Weather
Disease & pests
Timing/Growth stage of crop• Before grain filling
Yield• After grain filling
Quality• Grain• Seed
Other damages
Assumption: Planting and initial crop stand is normal
Blights
Rusts
Aphids or other insects
Post harvest
Temperature
Rain + Wind
Lodging
RainWater
Logging
Blasts
? Management not included here
Temp. during wheat season (Ludh)
10
15
20
25
30
35
40
45
Nov Dec Jan-1/2 Jan-2/2 Feb Mar Apr May
T-Max 13-14 T-Max 14-15 T-Max 15-16
0
5
10
15
20
25
Nov Dec Jan-1/2 Jan-2/2 Feb Mar Apr May
T-Min 13-14 T-Min 14-15 T-Min 15-16
Higher Temp
Low temp
Temp shock
Estimated 93.8mtProduction: 86.5
Expected causes of crop failure/loss
Weather
Disease & pests
Timing/Growth stage of crop• Before grain filling
Yield• After grain filling
Quality• Grain• Seed
? Management not included here
Other damages
Assumption: Planting and initial crop stand is normal
Blights
Rusts
Aphids or other insects
Post harvest
Temperature
Rain + Wind
Lodging
RainWater
Logging
Blasts
Why management is important?
Farmer A
Crop failure/loss
Farming is not of much concernedFarmer BInnovative and
hardworking
Crop Insurance
How to compensate
proportionatelyLogical to have photo based
insurance
Is there other way also?
Proper care in inputs
Let the crop go itself
Beyond farmer control
Satellite data
Production history
Not a cause of crop loss per se but important component to
be considered ?
Microsatellite dataThermal image of the CIMMYT-Obregon station• Resolution: 2 Meter• Date: 14 Feb 2013• Well-watered (cooler) plots: Blue• Water-stressed (warmer) plots: Green and Red. • Roads and bare soil areas (higher temperature): Yellow
Photo: Katelyn Roett, CIMMYT
Still predictive model for crop loss estimation need to refine
Production estimation-Sowing dates-Weather and on ground data
Surveillance
Why management is important?
Farmer A
Crop failure/loss
Farming is not of much concernedFarmer BInnovative and
hardworking
Crop Insurance
How to compensate
proportionatelyLogical to have photo based
insurance
Is there other way also?
Proper care in inputs
Let the crop go itself
Beyond farmer control
Satellite data
Production history
Not a cause of crop loss per se but important component to
be considered ?
Mini UAV (Drone) for HTP
Ludhiana
Jabalpur
Pusa
Data we collect through UAV
UAVNDVI Visual
Biomass at different growth stages
Biotic stresses 3D image of Plots
Abiotic stress or other damage
Foliar disease Rusts
Lodging
Plant Height
Yield loss estimation
Correlate with yield
Genetic mapping
Crop growth stages• Heading• Anthesis• Maturity
NDVI as a crop growth pattern
20
30
40
50
60
70
80
90
NDVI 2015-16
15
25
35
45
55
65
75
85
95
NDVI 2014-15
Ludhiana Jabalpur Pusa
Ludhiana Jabalpur Pusa
Max NDVI 66 DAS
Max NDVI 86 DAS
Max NDVI 64 DAS
Max NDVI 96 DAS Max NDVI 63 DAS Max NDVI 79 DAS
Average max NDVI Ludhiana (83.3) Jabalpur (71.0) Pusa (78.8)
Digital elevation model for lodging
Source: Daljit Singh, KSU
Regression equation for yield loss due to lodging
• Data recording: Using UAV
• Plot : 50 sqm, 2 Rep
• Lodging %: 0, 30, 50, 70, 100
• Stage of lodging: GFD
• Type of lodging: Root lodging
• Location: Jabalpur
• Year: 2015-16
• Date of recording: 05 Mar 2016
Further refinement is needed• Time of lodging:
• After anthesis• Before grain filling• After grain filling
• Larger samples• Different agronomy• Different genotypes
Role of breeding intervention
Genotype x Environment interaction– Planting of best genotype in unfavourable
environment• Early planting of Late sown variety• Late planting of early sown• Salt sensitive variety in saline soil• Sensitive to water logging
• Planting of highly susceptible variety (e.g. PBW343)
• Blights• Blast• Nematode etc
Physiological model for yield estimate
Ei1 x Y1 Ei2 x Y2 Ei3 x Y3
E= Environment (Crop duration)Solar radiationAverage daily temperature
• >2000 plots• 3 Locations• Weather data until booting
Control Block>100s variety
10 sowing dates
Farmers fieldVariety of farmer choice
planted on particular date
= =
Case study for HTP
Mapping of genes for Foliar disease in wheat
– Germplasm lines tested for leaf blight at Pusa (2 yrs)
– Used hand held NDVI sensor
– Data recorded 3-4 times at different growth stages
– Genotyping of mapping population (Microsatellite markers)
– QTL/Gene mapping and linkage map using IciMapping software
Results
Growth stage % DS at GS69 % DS at GS77
2013/4
NDVI (GS69) -0.89 (P < 0.01) -0.70 (P < 0.01)
NDVI (GS77) -0.73 (P < 0.01) -0.87 (P < 0.01)
2014/15
NDVI (GS69) -0.84 (P < 0.01) -0.67 (P < 0.01)
NDVI (GS77) -0.71 (P < 0.01) -0.91 (P < 0.01)
Trait LOD PVE (%) W-test P-value
2013-14
NDVI 24.9 41.3 0.92 ≤ 0.01
DS (%) 25.7 42.0 0.80 ≤ 0.01
2014-15
NDVI 30.1 48.8 0.93 ≤ 0.01
DS (%) 32.4 49.3 0.79 ≤ 0.01
Mapping using % DS and NDVI value based on data
taken at GS77
Correlation coefficients % DS and NDVI value at GS69 &
GS77
Marker Pos Intervalχ2 for seg
(1:1)P-value
Recom.
Freq
gwm639 31.87 0 6.876 0.00870.0058
NDVI 32.11 0.24 6.946 0.0054 0.0031
gwm1043 32.49 0.38 7.934 0.00490.0076
Linkage analysis using NDIV (high, medium and low) as phenotypic
marker and flanking SSR markerQTL mapping using NDVI
Conclusion
• Multiple factors may cause crop failure
• Several tools/models are available to estimate crop loss
• The mini UAV has been used to develop a model for crop loss estimation
• The models need to refine and test at larger scale
• NDVI can be used effectively to monitor crop health and unusual changes in growth pattern
• Not only yield loss estimation and crop health monitoring, the HTP tools can be used in basic research
Acknowledgement
Daljit Singh
Jesse Poland
Suneel Kumar
GWP, CIMMYT and
BISA Team
Thank you for
your support!
www.CIMMYT.org