Characterisation of fungal diseases on winter wheat crop using proximal and remote multispectral imaging

In: Precision agriculture '19
Authors:
R. Bebronne Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030, Gembloux, Belgium.

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A. Michez Forest Management, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030, Gembloux, Belgium.

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V. Leemans Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030, Gembloux, Belgium.

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P. Vermeulen Walloon Agricultural Research Centre, Valorisation of Agricultural Products Department, Food and Feed Quality Unit, Chaussée de Namur, 24, 5030, Gembloux, Belgium.

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B. Dumont Plant Sciences, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030, Gembloux, Belgium.

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B. Mercatoris Biosystems Dynamics and Exchanges, TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Passage des Déportés, 2, 5030, Gembloux, Belgium.

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Winter wheat fungal diseases, responsible for high yield losses, can be assessed by computer vision to increase phenotyping performance. This study aims to compare multispectral imagery based on remote and proximal sensing for disease detection. Wavelength selection was achieved by ANOVA and stepwise regression. Prediction of disease severity was performed by means of an artificial neural network based on proximal sensing data. Septoria tritici blotch (STB) requires proximal measurements, but stripe and brown rusts can be detected from UAVs and from the ground. Prediction results obtained gave R2 of 0.55 and 0.57 for STB and stripe rust respectively.

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