Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12313/2143
Title: Automatic wheat ear counting using machine learning based on RGB UAV imagery
Authors: Fernandez‐Gallego, Jose A.
Lootens, Peter
Borra‐Serrano, Irene
Derycke, Veerle
Haesaert, Geert
Roldán‐Ruiz, Isabel
Araus, Jose L.
Kefauver, Shawn C.
Keywords: Aerial platform
Ear counting
Ear density
Field phenotyping
Machine learning
RGB imaging
UAV
Wheat
Issue Date: 5-May-2020
Publisher: Plant Journal
Citation: Fernandez‐Gallego, J.A., Lootens, P., Borra‐Serrano, I., Derycke, V., Haesaert, G., Roldán‐Ruiz, I., Araus, J.L. and Kefauver, S.C. (2020), Automatic wheat ear counting using machine learning based on RGB UAV imagery. Plant J. doi:10.1111/tpj.14799
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.
URI: https://onlinelibrary.wiley.com/doi/abs/10.1111/tpj.14799
ISSN: 0960-7412
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