disease monitoring in wheat through remotely sensed data
DESCRIPTION
Remote sensing –Beyond images Mexico 14-15 December 2013 The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)TRANSCRIPT
Disease monitoring in wheatthrough remotely sensed data
Perla Chávez-Dulanto1
Pawan K. Singh1
Christian Yarlequé2
Matthew P. Reynolds1
1CIMMYT, Mexico2CIP, Peru
Justification
Conventional visual fieldmonitoring: stress is detectedafter significant damage hasoccurred and yield reduced.
Pests and diseases pressureincreased due to climatechange.
Disease monitoring through remotely sensed data
Stresses can be detected before symptomsdevelopment
Early disease monitoring through remotely sensed data
Approach and advantage
Reducing environmental risks andfootprint of farming by reducing useof agrichemicals.
Target application (local and extend)of pesticides.
Breeding purposes: identification ofresilient genotypes.
Non-destructive and large-scaleapplicable approach.
Boosting competitiveness throughmore efficient practices (e.g.improved management of inputs).
1) Ground-based assessment
2) Air-borne assessment
3) Space-borne assessment
SVI Name CalculationNDVI Normalized difference vegetation index (RNIR-Rred)/(RNIR+Rred)SRa Simple ratio R800/R680
NPQI Normalized pheophytinization index (R415-R435)/(R415+R435)PRI Photochemical reflectance index (R531-R570)/(R531+R570)WI1 Water index 1 R900/R970
WI2 Water index 2 R900/R950
Spectral vegetation indices (SVIs) calculated from the hyperspectral reflectance data of thewheat genotypes under study did not show reliable results
Early disease monitoring through hyperspectral remotely sensed data
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Wavelenght (nm)
Ref
lect
ance
(%)
Blue Green Red NIR
Method 1 : Physiology and Statistics (Chavez et al., 2009)
Ref
lect
ance
(%) ∫f(x) dx
a
b
Method 2 : Physics, Physiology and Statistics (Chavez et al., 2010)
Fractal dimensions and formalism of the time series hyperspectral data
Decision tree for classification of fungal disease severity of wheat with hyperspectral timeseries data
Three main diseases evaluated:
• Spot blotch → Agua Fria
• Tan spot → El Batán
• Septoria tritici blotch → Toluca
0.8 m wide
1 2 3
SEPTORIA TRITICI BLOTCH - TolucaGenotype AUDPC Index3m PhysioStat PhyStat AUDPC Index3m
Met1+Met2 Met1 Met2 Rank Rank
CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN131.07 I 0.47 I 25588 A 0.955 G 1 1
MURGA 139.71 H 0.49 H 30022 A 0.708 A 2 2
FINSI/METSO 218.93 G 0.54 G 23896 A 0.919 F 3 3
6B662 237.66 F 0.55 F 28227 A 0.858 D 4 4
GLENLEA 250.62 E 0.56 E 31360 A 0.748 B 5 5
CATBIRD 259.26 D 0.58 D 20345 A 1.024 H 6 6
ERIK 381.69 C 0.66 C 29257 A 0.87 E 7 8
ND-495 393.21 B 0.66 C 30350 A 0.83 C 8 7
HUIRIVIS #1 445.06 A 0.71 B 19691 A 1.048 I 9 9
KACHU #1 445.06 A 0.83 A 20036 A 1.187 J 10 10r 2 wi th AUDPC 1.00 0.95 0.35 0.52 0.99
SPOT BLOTCH - AguaFriaGenotype AUDPC Index3m Phys ioStat PhyStat AUDPC Index3m
Met1+Met2 Met1 Met2 Rank RankMURGA 58.23 J 0.62 I 23817 DE 2.59 C 1 1CHIRYA.3 60.08 I 0.62 I 21145 F 3.01 G 2 2CATBIRD 124.69 F 0.65 H 27001 AB 3.00 F 5 3CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN61.73 H 0.66 G 25151 CD 3.44 J 3 4KACHU #1 113.99 G 0.68 F 27988 A 3.19 I 4 5FINSI/METSO 133.54 E 0.69 E 22261 EF 2.67 D 6 6
HUIRIVIS #1 158.64 D 0.72 D 23880 DE 2.58 B 7 7
FRANCOLIN #1 231.69 C 0.73 C 26642 ABC 3.16 H 8 8
CIANO T 79 391.77 B 0.75 B 25325 BCD 2.91 E 9 9
SONALIKA 643.83 A 0.78 A 28389 A 2.42 A 10 10r 2 with AUDPC 1.00 0.85 0.53 0.46 0.96
Same level ofsensitivity like
AUDPC todiscriminatesusceptible/
resistantgenotypes
TAN SPOT - El BatanGenotype AUDPC Index3m Phys ioStat PhyStat AUDPC Index3m
Met1+Met2 Met1 Met2 Rank RankCROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN115.23 J 0.66 H 34824 C 2.75 E 1 1MURGA 122.43 I 0.67 G 35934 B 2.57 H 2 26B662 270.78 H 0.68 F 28178 G 3.29 A 3 3CATBIRD 292.39 G 0.69 E 37926 A 2.27 I 4 4HUIRIVIS #1 325.51 F 0.70 D 30195 F 2.67 G 5 5FINSI/METSO 328.40 E 0.71 C 34093 D 3.17 B 6 7KACHU #1 335.60 D 0.70 D 31912 E 2.73 F 7 6ERIK 371.60 C 0.71 C 27127 H 2.73 F 8 8GLENLEA 427.78 B 0.76 B 23147 I 3.03 D 9 9ND-495 488.27 A 0.81 A 20738 J 3.09 C 10 10r 2 with AUDPC 1.00 0.89 0.77 0.37 0.99
Pictures from CIMMYT Toluca 06/09/2012
Healthy Diseased
Yellow Rust Detection:
Merging both methods: 94% matching with conventional visual monitoring.
Discrimination between susceptible and resistant cultivars: Resilience level amonggenotypes.
Early disease detection through hyperspectral remotely sensed data :Yellow rust pilot trial (isolines) – Chavez P., Yahyaoui A., Singh P.K. et al.
Fusarium Head Bligth (FHB) : El Batan (in progress)
Images from El Batan, 2013
Thanks for your attention