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Precision Agriculture

, Volume 19, Issue 1, pp 161–177 | Cite as

Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images

  • Ruizhi Chen
  • Tianxing ChuEmail author
  • Juan A. Landivar
  • Chenghai Yang
  • Murilo M. Maeda
Article

Abstract

Examination of seed germination rate is of great importance for growers early in the season to determine the necessity for replanting their fields. The objective of this study was to explore the potential of using unmanned aircraft system (UAS)-based visible-band images to monitor and quantify the cotton germination process. A light-weight UAS platform was used, which carried a consumer-grade red, green, and blue camera stabilized by a built-in gimbal system. In order to obtain ultrahigh image resolution during the germination stage, the UAS platform was flown at an altitude of approximately 15–20 m above ground. By applying the structure-from-motion (SfM) algorithm, the images were rectified and orthographically mosaicked with a ground sampling distance of approximately 6–9 mm/pixel. A novel solution was then developed for calculating the average plant size and the number of germinated cotton plants according to the leaf polygons extracted from the orthomosaic images. By using the estimated number of germinated cotton plants, the plant density and the cumulative germination rate can also be estimated in a straightforward manner using field-specific parameters. An assessment of the proposed solution was conducted by comparing the estimated number of the germinated cotton plants against ground observation data collected from six cotton row segments. The results demonstrated that the average estimation accuracy achieved 88.6% in terms of identifying the number of the germinated cotton plants. The accuracy may be further improved if images with near infrared band are employed.

Keywords

Cotton germination Unmanned aircraft system Image processing Orthomosaics Ultrahigh spatial resolution Leaf polygon 

Notes

Acknowledgements

This research work was co-funded by the Cotton Incorporated (Project 15-669TX) and the National Science Foundation (Award Nr. 1 429 518). The authors would like to thank Dr. Carlos Fernandez from the Texas A&M AgriLife Research and Extension Center at Corpus Christi for providing the meteorological data of the test field.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Ruizhi Chen
    • 1
  • Tianxing Chu
    • 2
    Email author
  • Juan A. Landivar
    • 3
  • Chenghai Yang
    • 4
  • Murilo M. Maeda
    • 3
  1. 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina
  2. 2.Conrad Blucher Institute for Surveying and ScienceTexas A&M University Corpus ChristiCorpus ChristiUSA
  3. 3.Texas A&M AgriLife Research and Extension CenterCorpus ChristiUSA
  4. 4.USDA-Agricultural Research ServiceCollege StationUSA

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