This document discusses using unmanned aerial vehicles and remote sensing to estimate crop loss. It presents several models that have been developed using data collected from UAVs, including models to estimate yield loss from lodging, monitor crop growth patterns using NDVI values, and map genes for foliar disease resistance in wheat. The models need further refinement by testing on larger scales and under different agronomic conditions. Remote sensing tools allow monitoring of crop health and development of predictive models for crop loss estimation that can help determine compensation in crop insurance programs.

1. 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

2. 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
Rain
Water
Logging
Blasts
? Management not
included here

3. 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 93.8mt
Production: 86.5

4. 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
Rain
Water
Logging
Blasts

5. 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 ?

6. Microsatellite data
Thermal 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

7. 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 ?

8. Mini UAV (Drone) for HTP
Ludhiana
Jabalpur
Pusa

9. 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

10. 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)

11. Digital elevation model for lodging
Source: Daljit Singh, KSU

12. 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

13. 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

14. Physiological model for yield estimate
Ei1 x Y1 Ei2 x Y2 Ei3 x Y3
E= Environment (Crop duration)
Solar radiation
Average daily temperature
• >2000 plots
• 3 Locations
• Weather data until booting
Control Block
>100s variety
10 sowing dates
Farmers field
Variety of farmer choice
planted on particular date
= =

15. 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

16. 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 Interval
χ2 for seg
(1:1)
P-value
Recom.
Freq
gwm639 31.87 0 6.876 0.0087
0.0058
NDVI 32.11 0.24 6.946 0.0054 0.0031
gwm1043 32.49 0.38 7.934 0.0049
0.0076
Linkage analysis using NDIV (high, medium and low) as phenotypic
marker and flanking SSR marker
QTL mapping using NDVI

17. 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

18. Acknowledgement
Daljit Singh
Jesse Poland
Suneel Kumar
GWP, CIMMYT and
BISA Team

19. Thank you for
your support!
www.CIMMYT.org