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Fernandez-Gallego JA, Lootens P, Borra-Serrano I, Derycke V, Haesaert G, Roldán-Ruiz I, Araus JL, Kefauver SC. Automatic wheat ear counting using machine learning based on RGB UAV imagery. Plant J 2020; 103:1603-1613. [PMID: 32369641 DOI: 10.1111/tpj.14799] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 04/15/2020] [Accepted: 04/23/2020] [Indexed: 06/11/2023]
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.
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Affiliation(s)
- Jose A Fernandez-Gallego
- Plant Physiology Section, Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Diagonal 643, Barcelona, 08028, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, Lleida, 25198, Spain
- Programa de Ingeniería Electrónica, Facultad de Ingeniería, Universidad de Ibagué, Carrera 22 Calle 67, Ibagué, 730001, Colombia
| | - Peter Lootens
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Caritasstraat 39, Melle, 9090, Belgium
| | - Irene Borra-Serrano
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Caritasstraat 39, Melle, 9090, Belgium
- Division of Forest, Nature and Landscape, KU Leuven, Celestijnenlaan 200E, Leuven, 3001, Belgium
| | - Veerle Derycke
- Department Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Valentin Vaerwyckweg 1, Ghent, 9000, Belgium
| | - Geert Haesaert
- Department Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Valentin Vaerwyckweg 1, Ghent, 9000, Belgium
| | - Isabel Roldán-Ruiz
- Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Caritasstraat 39, Melle, 9090, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Technologiepark 71, Ghent, 9052, Belgium
| | - Jose L Araus
- Plant Physiology Section, Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Diagonal 643, Barcelona, 08028, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, Lleida, 25198, Spain
| | - Shawn C Kefauver
- Plant Physiology Section, Department of Evolutionary Biology, Ecology and Environmental Sciences, Faculty of Biology, University of Barcelona, Diagonal 643, Barcelona, 08028, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, Lleida, 25198, Spain
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