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