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Quantifying Stripe Rust Reaction in Wheat Using Remote Sensing Based Hand-held NDVI Sensor

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Apoorva Arora

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Quantifying Stripe Rust Reaction in Wheat Using Remote Sensing Based Hand-held NDVI Sensor

  1. 1. Quantifying stripe rust reaction in wheat using remote sensing based hand held NDVI sensor Apoorva Arora Directorate of Wheat Research, Karnal
  2. 2. Normalized  difference  vegeta2on   index  (NDVI)         Ø  Func2on  of  incident  and  reflected  light                                                NDVI=      NIR  –  Red    ,                                                                                NIR  +  Red                      NIR  750-­‐1300  nm                                          Red  600-­‐700  nm     Ø  Foliar  pigments  dominate  reflectance  measurements      0<  NDVI<  1  
  3. 3. Stripe  Rust  and  NDVI     Ø  Breakdown  of  foliar  pigments   Ø  Foliar  physiological  ac2vity  decreases   Ø  Less  reflactance  of  infrared  by  healthy   vegeta2on  and  vice  versa   Ø  Decrease  in  value  of  NDVI     NASA  Earth  Observatory  (Illustra2on  by  Robert  Simmon)  
  4. 4. Why  NDVI   Ø Most  sensors  provide  measurements  in  NIR  &  Red   por2on  of  spectrum     Ø Addi2ve  gene  governed  rust  resistance  geZng  more   a[en2on  than  ever  before     Ø Minor  varia2ons  can  be  captured  by  quan2ta2ve   varia2on  
  5. 5. High  Throughput  Screening  Gap     Phenotyping   Genotyping   Phenotyping  in  field  condi1ons…??  
  6. 6. Rust  Scoring  Methodology  
  7. 7. Satellite  based  Remote  sensing:   Limita2ons   Ø Atmospheric  condi2ons   Ø Satellite  geometry  &  calibera2on   Ø Soil  backgrounds   Ø Crop  canopy   Ø Angle  of  solar  radia2on  incidence   Ø Small  plots  can’t  be  used  for  measurements          
  8. 8. Objec2ve   .          Quantifying stripe rust reaction in wheat using remote sensing based hand held NDVI sensor  
  9. 9. Research  Methodology    
  10. 10. Study  Material   322  genotypes   Pedigree   analysis   Diversity   analysis   120     genotypes     Released  varie1es,   Local  land  races,Elite   wheat     genotypes         Represen1ng     each  group  
  11. 11. Field  Experiment   LaZce  Design    
  12. 12. Plan2ng  Method  
  13. 13. Epiphyto2c  Condi2ons     Ø Epiphyto2c  condi2ons  created   by  plan2ng  suscep2ble  check   on  either  sides  of  plot   Ø Inoculated  with  Yr27  virulent   race  78S84  of  Puccinia   striiformis  
  14. 14.   Equipment     Ø  Recorded  data  using  handheld  ac2ve  op2cal  GreenSeeker   sensor  (Trimble  Industeries,  Inc.)     Ø  NDVI  computed  from  reflectance  measurements  in  red   (~660nm)&  near  infrared  (around  780nm)por2on  of  spectrum     Ø  Display  value  in  range  of  0.00  to  0.99  
  15. 15. RESULTS  
  16. 16. AUDPC  wise  distribu2on  of  genotypes   Ø  AUDPC  computed  varied  from  0   to  2077   Ø  Equal  number  of  genotypes  in  all   categories  except  in  AUDPC  range   of  1-­‐100  
  17. 17. NDVI     Ø  Recorded  when  crop  showed  symptoms  of      maximum  infec2on     Ø  Value  of  NDVI  varied  from  0.46    to  0.69     Ø  Mean  values  across  different  AUDPC  range:  0.58  to  0.69   Ø  Value  reduced  with  increase  in  incidence  of  disease  
  18. 18. NDVI  vs  AUDPC   Ø Regression  equa2on  for   NDVI  using  AUDPC  :            NDVI=0.663+-­‐6.165E-­‐5(AUDPC)t     Ø Significant  correla2on   depicted  by  r2  value  of  0.63     NDVI AUDPC
  19. 19. Effect  of  Spot  Blotch   Ø  Spectral  quality  of    reflected  light   from  leaves  manifested  in  leaf   color     Ø   NDVI  values  also  got  affected  by   the  presence  of  spot  blotch   Ø  Quan2fied  value  of  NDVI  due  to   blotch  alone  was  added  (if   required)    
  20. 20. Ø  Mean  NDVI  values  amer  correc2on:   0.64  to  0.76     Ø  Regression  equa2on  for      Corrected  NDVI  vs  AUDPC:      NDVI=0.738  +-­‐7.061E-­‐5(AUDPC)t     Ø  Correla2on  (r2  )  value  improved  to   0.69           NDVI AUDPC
  21. 21. Plant  physiological  factors   AUDPC     Ø  Correla2on  coefficient  showed   significant  values   Ø  Correla2on  value  improved  as   categories  shimed  from   predominantly  resistant  to              suscep2ble  types   AUDPC   Range   Coeff.  of   determina2on  (r2)     Correla2on   coefficient   0-­‐200   0.20     -­‐0.45   >200   0.72     -­‐0.85                
  22. 22. Plant  Height     Plant  Height   (in  cm)   No.  of   genotypes   Coeff.  of   determina1on  (r2)   Correla1on  coefficient   65-­‐74   3   0.76   -­‐0.87   75-­‐84   15   0.62   -­‐0.79   85-­‐94   50   0.62   -­‐0.79   95-­‐104   35   0.73   -­‐0.85   105-­‐114   14   0.57   -­‐0.76   115-­‐124   2   1.00   -­‐1   Ø  Value  of  correla2on  coefficient  increased  as  taller  types  were                    more  suscep2ble   Ø  Range  of  coefficient  :  0.76  to  1.00        
  23. 23.   Ø Similar  value  of  correla2on  coefficient  obtained   Ø No  significant  difference  between  categories            was  no2ced       Waxiness  and  Early  Growth  Habit    
  24. 24. Conclusion   Ø  With  increasing  a[en2on  towards  quan2ta2ve  rust  resistance   studies,  innova2ve  tools  &  techniques  are  needed   Ø  NDVI  sensor  technique  provides  mean  value  of  several  images   captured  from  the  plot  as  against  single  frame  observa2on  by   human  eye   Ø  High  correla2on  value  indicates  suitability  of    the  instrument   as  an  useful  tool  for  accurate  rust  data  recording     Ø  Accuracy  of  this  method  improves  when  catagoriza2on  of   genotypes  accrued  to  predominance  of    susep2bility  
  25. 25. Inspiration behind work…
  26. 26. Dr.  Indu  Sharma   Project  Director   Directorate  of  Wheat  Research    Karnal   Dr.  M.  S.  Saharan   Principal  Scien1st   Dr.  R.K.    Sharma   Principal  Scien1st   Dr.  K.  Venkatesh   Scien1st   Davender  Sharma   Sr.  Research   Fellow  
  27. 27. Thank You

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