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Disease monitoring in wheat through remotely sensed data

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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)

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Disease monitoring in wheat through remotely sensed data

  1. 1. Disease monitoring in wheat through remotely sensed data Perla Chávez-Dulanto1 Pawan K. Singh1 Christian Yarlequé2 Matthew P. Reynolds1 1CIMMYT, Mexico 2CIP, Peru p.chavez@cgiar.org
  2. 2. Disease monitoring through remotely sensed data Justification  Conventional visual field monitoring: stress is detected after significant damage has occurred and yield reduced.  Pests and diseases pressure increased due to climate change.
  3. 3. Early disease monitoring through remotely sensed data Approach and advantage 3) Space-borne assessment 2) Air-borne assessment  Reducing environmental risks and footprint of farming by reducing use of agrichemicals.  Target application (local and extend) of pesticides.  Breeding purposes: identification of resilient genotypes.  Non-destructive and large-scale applicable approach. 1) Ground-based assessment Stresses can be detected before symptoms development  Boosting competitiveness through more efficient practices (e.g. improved management of inputs).
  4. 4. Early disease monitoring through hyperspectral remotely sensed data SVI NDVI SRa NPQI PRI WI1 WI2 Name Normalized difference vegetation index Simple ratio Normalized pheophytinization index Photochemical reflectance index Water index 1 Water index 2 Calculation (RNIR-Rred)/(RNIR+Rred) R800/R680 (R415-R435)/(R415+R435) (R531-R570)/(R531+R570) R900/R970 R900/R950 Spectral vegetation indices (SVIs) calculated from the hyperspectral reflectance data of the wheat genotypes under study did not show reliable results
  5. 5. Method 1 : Physiology and Statistics (Chavez et al., 2009) 100 ∫ b 60 f(x) dx a 40 20 Wavelenght (nm) Blue Green Red NIR 988 952 916 879 843 806 769 732 695 658 620 582 544 506 468 429 0 390 Reflectance (%) Reflectance (%) 80
  6. 6. Method 2 : Physics, Physiology and Statistics (Chavez et al., 2010) Fractal dimensions and formalism of the time series hyperspectral data
  7. 7. Decision tree for classification of fungal disease severity of wheat with hyperspectral time series data
  8. 8. Three main diseases evaluated: • Septoria tritici blotch → Toluca • Tan spot → El Batán • Spot blotch → Agua Fria
  9. 9. 1 2 0.8 m wide 3
  10. 10. SEPTORIA TRITICI BLOTCH - Toluca Genotype AUDPC Index3m PhysioStat Met1+Met2 PhyStat AUDPC Index3m Met1 Met2 Rank Rank CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN I 131.07 I 0.47 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 10 2 r wi th AUDPC SPOT BLOTCH - AguaFria Genotype 1.00 0.95 AUDPC 0.35 Index3m Met1+Met2 0.99 0.52 Phys i oSta t Met1 PhySta t Met2 AUDPC Ra nk Index3m Ra nk MURGA 58.23 J 0.62 CHIRYA.3 60.08 I 0.62 CATBIRD 124.69 F 0.65 CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN 61.73 H 0.66 KACHU #1 113.99 G 0.68 I I H G F 23817 21145 27001 25151 27988 DE F AB CD A 2.59 3.01 3.00 3.44 3.19 C G F J I 1 2 5 3 4 1 2 3 4 5 FINSI/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 r 2 wi th AUDPC TAN SPOT - El Batan Genotype 1.00 AUDPC 0.85 Index3m Met1+Met2 CROC_1/AE.SQUARROSA (205)//BORL95/3/2*MILAN 115.23 J 0.66 MURGA 122.43 I 0.67 6B662 270.78 H 0.68 CATBIRD 292.39 G 0.69 HUIRIVIS #1 325.51 F 0.70 FINSI/METSO 328.40 E 0.71 KACHU #1 335.60 D 0.70 ERIK 371.60 C 0.71 GLENLEA 427.78 B 0.76 ND-495 488.27 A 0.81 r 2 wi th AUDPC 1.00 0.53 0.89 Phys i oSta t Met1 H G F E D C D C B A 34824 35934 28178 37926 30195 34093 31912 27127 23147 20738 0.77 PhySta t Met2 C B G A F D E H I J 2.75 2.57 3.29 2.27 2.67 3.17 2.73 2.73 3.03 3.09 0.37 10 0.96 0.46 AUDPC Ra nk E H A I G B F F D C Index3m Ra nk 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 7 6 8 9 10 0.99 Same level of sensitivity like AUDPC to discriminate susceptible/ resistant genotypes
  11. 11. Early disease detection through hyperspectral remotely sensed data : Yellow rust pilot trial (isolines) – Chavez P., Yahyaoui A., Singh P.K. et al. 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 among genotypes.
  12. 12. Fusarium Head Bligth (FHB) : El Batan (in progress) Images from El Batan, 2013
  13. 13. Thanks for your attention

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