8 June 2016 Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry
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Abstract
Digital and image analysis technologies in greenhouses have become commonplace in plant science research and started to move into the plant breeding industry. However, the core of plant breeding work takes place in fields. We will present successive technological developments that have allowed the migration and application of remote sensing approaches at large into the field of crop genetics and physiology research, with a number of projects that have taken place in France. These projects have allowed us to develop combined sensor plus vector systems, from tractor mounted and UAV (unmanned aerial vehicle) mounted spectroradiometry to autonomous vehicle mounted spectroradiometry, RGB (red-green-blue) imagery and Lidar. We have tested these systems for deciphering the genetics of complex plant improvement targets such as the robustness to nitrogen and water deficiency of wheat and maize. Our results from wheat experiments indicate that these systems can be used both to screen genetic diversity for nitrogen stress tolerance and to decipher the genetics behind this diversity. We will present our view on the next critical steps in terms of technology and data analysis that will be required to reach cost effective implementation in industrial plant breeding programs. If this can be achieved, these technologies will largely contribute to resolving the equation of increasing food supply in the resource limited world that lies ahead.
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David Gouache, David Gouache, Katia Beauchêne, Katia Beauchêne, Agathe Mini, Agathe Mini, Antoine Fournier, Antoine Fournier, Benoit de Solan, Benoit de Solan, Fred Baret, Fred Baret, Alexis Comar, Alexis Comar, } "Applying remote sensing expertise to crop improvement: progress and challenges to scale up high throughput field phenotyping from research to industry", Proc. SPIE 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping, 986604 (8 June 2016); doi: 10.1117/12.2229389; https://doi.org/10.1117/12.2229389
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