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Automatic wheat ear counting using machine learning based on RGB UAV imagery

(2020) PLANT JOURNAL. 103(4). p.1603-1613
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Abstract
In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield‐determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear‐counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017–2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro‐plot images. The relationship between the image‐based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in‐situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in‐situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed.
Keywords
aerial platform, ear counting, ear density, field phenotyping, machine learning, RGB imging, UAV, wheat, DURUM-WHEAT, YIELD COMPONENTS, GRAIN-YIELD, EXTRACTION, FUTURE, FOREST

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MLA
Fernandez‐Gallego, Jose A., et al. “Automatic Wheat Ear Counting Using Machine Learning Based on RGB UAV Imagery.” PLANT JOURNAL, vol. 103, no. 4, 2020, pp. 1603–13, doi:10.1111/tpj.14799.
APA
Fernandez‐Gallego, J. A., Lootens, P., Borra‐Serrano, I., Derycke, V., Haesaert, G., Roldàn-Ruiz, I., … Kefauver, S. C. (2020). Automatic wheat ear counting using machine learning based on RGB UAV imagery. PLANT JOURNAL, 103(4), 1603–1613. https://doi.org/10.1111/tpj.14799
Chicago author-date
Fernandez‐Gallego, Jose A., Peter Lootens, Irene Borra‐Serrano, Veerle Derycke, Geert Haesaert, Isabel Roldàn-Ruiz, Jose L. Araus, and Shawn C. Kefauver. 2020. “Automatic Wheat Ear Counting Using Machine Learning Based on RGB UAV Imagery.” PLANT JOURNAL 103 (4): 1603–13. https://doi.org/10.1111/tpj.14799.
Chicago author-date (all authors)
Fernandez‐Gallego, Jose A., Peter Lootens, Irene Borra‐Serrano, Veerle Derycke, Geert Haesaert, Isabel Roldàn-Ruiz, Jose L. Araus, and Shawn C. Kefauver. 2020. “Automatic Wheat Ear Counting Using Machine Learning Based on RGB UAV Imagery.” PLANT JOURNAL 103 (4): 1603–1613. doi:10.1111/tpj.14799.
Vancouver
1.
Fernandez‐Gallego JA, Lootens P, Borra‐Serrano I, Derycke V, Haesaert G, Roldàn-Ruiz I, et al. Automatic wheat ear counting using machine learning based on RGB UAV imagery. PLANT JOURNAL. 2020;103(4):1603–13.
IEEE
[1]
J. A. Fernandez‐Gallego et al., “Automatic wheat ear counting using machine learning based on RGB UAV imagery,” PLANT JOURNAL, vol. 103, no. 4, pp. 1603–1613, 2020.
@article{8671928,
  abstract     = {In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield‐determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear‐counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017–2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro‐plot images. The relationship between the image‐based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in‐situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in‐situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed.},
  author       = {Fernandez‐Gallego, Jose A. and Lootens, Peter and Borra‐Serrano, Irene and Derycke, Veerle and Haesaert, Geert and Roldàn-Ruiz, Isabel and Araus, Jose L. and Kefauver, Shawn C.},
  issn         = {0960-7412},
  journal      = {PLANT JOURNAL},
  keywords     = {aerial platform,ear counting,ear density,field phenotyping,machine learning,RGB imging,UAV,wheat,DURUM-WHEAT,YIELD COMPONENTS,GRAIN-YIELD,EXTRACTION,FUTURE,FOREST},
  language     = {eng},
  number       = {4},
  pages        = {1603--1613},
  title        = {Automatic wheat ear counting using machine learning based on RGB UAV imagery},
  url          = {http://dx.doi.org/10.1111/tpj.14799},
  volume       = {103},
  year         = {2020},
}

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