Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes Academic Article uri icon


  • 2018 Elsevier B.V. Recent advances in molecular breeding and bioinformatics have greatly accelerated the screening of large sets of genotypes. Development of field-level phenotyping, however, still lags behind and is considered by many as the main bottleneck to improved efficiency in breeding programs. Unmanned Aerial System (UAS) and sensor technology available today enables collection of data at high spatial and temporal scales, previously unobtainable using traditional airborne remote sensing technologies. Here, we propose an UAS-assisted high throughput phenotyping framework for cotton (Gossypium hirsutum L.) genotype selection. UAS data collected on July 24, 2015 were used to calculate canopy cover, and UAS data collected on August 5, 2015 were used to extract open boll related phenotypic features including number of open bolls, average area of open bolls, average diameter of open bolls, perimeter of open bolls, perimeter to area ratio. Using the extracted features, a sequential selection procedure was performed on a population of 144 entries. Entries selected from the proposed framework were compared to the highest yielding entries determined by mechanical harvest results. Experimental results indicated that the selection process increased minimum and average lint yield of the remaining population by 7.4 and 10%, respectively, and UAS-selected entries and genotypes matched 80 and 73%, respectively, the same lists ranked by actual field harvest measurements.

published proceedings


altmetric score

  • 0.25

author list (cited authors)

  • Jung, J., Maeda, M., Chang, A., Landivar, J., Yeom, J., & McGinty, J.

citation count

  • 25

complete list of authors

  • Jung, Jinha||Maeda, Murilo||Chang, Anjin||Landivar, Juan||Yeom, Junho||McGinty, Joshua

publication date

  • January 2018