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REMOTE SENSING OF IPM:

REMOTE SENSING OF IPM: Reflectance Measurements of Aphid Infestation and Density Estimation in Wheat Growing under Field Conditions. Mustafa Mirik, Gerald J. Michels Jr., Norman C. Elliott, Sabina Kassymzhanova, Roxanne Bowling, Bob Villarreal, Vasile Catana, Timothy D. Johnson.

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REMOTE SENSING OF IPM:

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  1. REMOTE SENSING OF IPM: Reflectance Measurements of Aphid Infestation and Density Estimation in Wheat Growing under Field Conditions. Mustafa Mirik, Gerald J. Michels Jr., Norman C. Elliott, Sabina Kassymzhanova, Roxanne Bowling, Bob Villarreal, Vasile Catana, Timothy D. Johnson

  2. How this fits with AWPM for Wheat As a part of the participation with AWPM for Wheat, the Entomology Program in Amarillo is using an airborne hyperspectral spectrometer for detecting aphid infestations. The work is conducted as a part of the Precision Agriculture Initiative at Texas A & M in cooperation with Oklahoma State University and the USDA-ARS.

  3. Remote sensing helps detect greenbug infestations in wheat fields and helps demonstrate alternatives to costly spraying. We hope to detect infestations before wheat fields would require insecticide application to protect crops from economic losses. It can be used to generate spatial, up-to-date information over time and space in combination with statistical tools such as GIS

  4. Remote sensing is the art & science of collecting information about the earth’s surface using some portions of the electromagnetic spectrum from ground, air and space platforms without physical contact with the objects under surveillance To the right is the equipment that they use. Above right: Cessna 172; Right: airborne aerial spectometer.

  5. Methodology Collected aphid density and spectral reflectance data from Texas, Oklahoma and Colorado • Aphid density included greenbug, bird-cherry oat, & Russian wheat aphids • Data collected in & over stressed and non-stressed 0.25m2 wheat plots • Reflectance data gathered by hyperspectral ground spectrometer over aphid infested wheat and non-infested wheat nearby

  6. Methodology • Subsequently, at least 30 tillers were cut at ground level and transported to laboratory to count the number of aphids per 0.25m2 sample plot • Remaining tillers in each plot were tallied in the fields to estimate aphid density for each sample plot • Sometimes, aphid density was determined in the fields by counting all aphids within plots during the early growing season or clipping all plants and counting aphids in the laboratory during the late growing season as time permits

  7. Methodology • All in all, aphid density was determined at 0.25m2 level for each sample • The methodology was applied to all sites and information given during the following slides

  8. Distractive sampling to count aphids in the lab when wheat was at mid- and late growth stages.

  9. TAMU employees at work counting Russian wheat aphids in the lab Russian wheat aphid population in a 0.25m2 wheat plot

  10. Survey team from the USDA-ARS, Stillwater, and TAMU collecting aphid and remote sensing data in the field

  11. The plot at the top represents the greenbug (GB) and bird-cherry oat aphid (BCOA) (mainly GB), combination of aphid and abiotic stress, and no-stress on volunteer wheat. The data were collected over infested wheat near Dumas, Texas in late fall 2003.

  12. The plot above is the Russian wheat aphid (RWA) and abiotic stress, no-stress on winter wheat, and exposed soil collected in a field near Amarillo, Texas in mid-April 2004.

  13. Spectral reflectance of GB and RWA data were plotted across the Visible and Near Infrared (NIR) wavelengths. These plots clearly indicates that hot spots of GB and RWA can be accurately detected and discriminated from the soil, abiotic stress and non-stressed wheat in fields with air- and space-borne remote sensing platforms at an appropriate scale

  14. The graph and table below depicts sampling done on wheat under 3 levels of stress: healthy plants, plants stressed by greenbug alone, and plants stressed by a combination of greenbug and abiotic factors (bar plot on the left) and RWA stress (table on the right).

  15. We found there were statistically significant differences in the reflectance from each of these wheat conditions. A similar comparison of wheat stressed by Russian wheat aphid versus healthy plants also showed significant differences in reflected light These data substantiates the trend seen in the two previous graphs; it suggests we can use air- and space-borne imageries to detect aphid stress.

  16. Healthy wheat Abiotic and Aphid Stressed Wheat Aphid Stressed Wheat

  17. These are the sample pictures of aphid stress, combination of aphid and abiotic stress, and no-stress on volunteer wheat taken in the same field near Dumas, Texas. Aphid and combination of aphid and abiotic stresses are visually assessable in these pictures. We found eight spots heavily damaged by aphid, both GB and BCOA; we also found 22 spots stressed by abiotic and biotic factors. These eight pictures were analyzed using ASSESS, Image Analysis Software for Plant Disease Quantification, to determine percent damage caused by aphid feeding on wheat, (see next slide). Then total aphid (GB + BCOA), GB, and BCOA densities were regressed against percent aphid damage to estimate aphid density. See slides 20, 21, & 22 for these results.

  18. This picture shows how the percent aphid damage was assessed by masking either healthy or unhealthy parts of the canopy. Aphid damage was outlined, and percent damage was estimated on wheat leaves.

  19. We found strong correlations between percent aphid damage and density of total aphid, GB, and BCOA (R2 = 0.85 for total aphid, GB, and BCOA Densities). The next three slides present slides that illustrate this.

  20. This graph shows strong correlation found between percent aphid damage and total aphid density, greenbug and bird-cherry oat aphid. R2 = 0.85

  21. This graph shows percent damage and the correlation with greenbug density (R2 = 0.85)

  22. This graph shows percent damage and its correlation with bird-cherry oat aphid density (R2 = 0.85)

  23. Reflectance data were analyzed by calculating 25 existing spectral vegetation indices and regressing them against density of total aphid, GB, and BCOA for 30 samples situated near Dumas. Of which, the Carotenoid Reflectance Index (CRI) was best correlated with density of aphids.

  24. This graph shows the correlation between CRI and total aphid (GB & BCOA) number.

  25. This graph shows the correlation between CRI and greenbug density.

  26. This graph shows the correlation between CRI and bird-cherry oat aphid density.

  27. Spectral reflectance data gathered in Oklahoma winter wheat fields exhibited similar trends to the data collected near Dumas for aphid density estimation. This graph shows the relationship between NDVI (Normalized Difference Reflectance Index) and total aphid (GB & BCOA) density.

  28. The graph shows the relationship between NDVI and greenbug density.

  29. The graph shows the relationship between green NDVI and bird-cherry oat aphid density.

  30. Strong linear relationship were found between RWA density and spectral vegetation indices for both Texas (slide 31) and Colorado (slide 32) winter wheat fields. Correlations were 97% for Texas and 77% for the Colorado wheat fields.

  31. Texas This slide shows the correlation between CR (Chlorophyll Ratio) and RWA density

  32. Colorado This slide shows the correlation between NDVI and RWA density Colorado

  33. We have also worked on estimating and comparing wet and dry biomass of stressed by RWA and non-stressed wheat near Amarillo. The next slide shows that there were significant differences between wet and dry biomass of stressed and non-stressed wheat. The next slide gives a slide illustrating this.

  34. This slide shows dry biomass estimation for RWA infested and non-infested wheat. High R2 values (0.80 and 0.69 for infested and non-infested wheat, respectively) indicate the usefulness of remote sensing technology and techniques to predict wheat biomass regardless of aphid infestation.

  35. Last year, we collected baseline data to correlate observed aphid density and damage in wheat to ground-based remote sensing data. These preliminary results showing established correlations strongly force us to move to forward. In addition to these, remote sensing technologies and techniques are highly promising to detect aphid stress in other field crops.

  36. In the next year, we plan to move from the ground-based remote sensing to air-borne hyperspectral and/or satellite multispectral remote sensing. We expect to use hyperspectral or multispectral imageries to detect aphid-induced stress in wheat and sorghum, and possibly other crops at larger scales if the conditions permit. Click here to return to AWPM for Wheat site

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