Person:
Haghighattalab, A.

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Haghighattalab
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Haghighattalab, A.
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0000-0003-3707-5551

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2016 - 201922020 - 20213
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    Improving wheat yield prediction using secondary traits and high-density phenotyping under heat-stressed environments
    (Frontiers, 2021) Rahman, M.M.; Crain, J.; Haghighattalab, A.; Singh, R.P.; Poland, J.
    Publication
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    Aerial high‐throughput phenotyping enabling indirect selection for grain yield at the early‐generation seed‐limited stages in breeding programs
    (CSSA, 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
    Publication
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    Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs
    (Crop Science Society of America (CSSA), 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
    Publication
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    Utilizing genomics and phenomics in CIMMYT wheat breeding
    (CIMMYT, 2016) Mondal, S.; Poland, J.; Haghighattalab, A.; Singh, D.; Rahmanm, M.; Sorrells, M.E.; Jin Sun; Singh, R.P.; Crossa, J.; Dreisigacker, S.; Kumar, U.; Imtiaz, M.; Juliana, P.
    Publication
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    Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries
    (BioMed Central, 2016) Haghighattalab, A.; González Pérez, L.; Mondal, S.; Singh, D.; Schinstock, D.; Rutkoski, J.; Ortiz-Monasterio, I.; Singh, R.P.; Goodin, D.; Poland, J.
    Background: Low cost unmanned aerial systems (UAS) have great potential for rapid proximal measurements of plants in agriculture. In the context of plant breeding and genetics, current approaches for phenotyping a large number of breeding lines under field conditions require substantial investments in time, cost, and labor. For fieldbased high-throughput phenotyping (HTP), UAS platforms can provide high-resolution measurements for small plot research, while enabling the rapid assessment of tens-of-thousands of field plots. The objective of this study was to complete a baseline assessment of the utility of UAS in assessment field trials as commonly implemented in wheat breeding programs. We developed a semi-automated image-processing pipeline to extract plot level data from UAS imagery. The image dataset was processed using a photogrammetric pipeline based on image orientation and radiometric calibration to produce orthomosaic images. We also examined the relationships between vegetation indices (VIs) extracted from high spatial resolution multispectral imagery collected with two different UAS systems (eBee Ag carrying MultiSpec 4C camera, and IRIS+ quadcopter carrying modified NIR Canon S100) and ground truth spectral data from hand-held spectroradiometer. Results: We found good correlation between the VIs obtained from UAS platforms and ground-truth measurements and observed high broad-sense heritability for VIs. We determined radiometric calibration methods developed for satellite imagery significantly improved the precision of VIs from the UAS. We observed VIs extracted from calibrated images of Canon S100 had a significantly higher correlation to the spectroradiometer (r = 0.76) than VIs from the MultiSpec 4C camera (r = 0.64). Their correlation to spectroradiometer readings was as high as or higher than repeated measurements with the spectroradiometer per se. Conclusion: The approaches described here for UAS imaging and extraction of proximal sensing data enable collection of HTP measurements on the scale and with the precision needed for powerful selection tools in plant breeding. Low-cost UAS platforms have great potential for use as a selection tool in plant breeding programs. In the scope of tools development, the pipeline developed in this study can be effectively employed for other UAS and also other crops planted in breeding nurseries.
    Publication