Timothée Wintz

Automated computer vision tasks are a key factor to provide high-throughput phenotyping of plants in the field, and could lead to a more efficient and sustainable agriculture. This requires analysis of data acquired in the field and development of both plant models and new computer vision methods to extract useful traits from crops, across space and time. Machine learning techniques can also help us find useful information in the data and help the farmers in their decision-making process. The roughness of in-field conditions compared to the lab's perfectly controlled environment, as well as the diversity of crops in small market farms make for a great challenge, especially when we want to keep the costs of tools and sensors accessible to everyone. Making farmers part of these experiments is key to the approach's success, and this is why I think it is important to develop free and open source software, and to keep the data open.

Plant Models / Computer Vision / Machine Learning



Selected Publications

Wintz T., Colliaux D., Hanappe P. Automated extraction of phyllotactic traits from Arabidopsis thaliana (To be submitted soon to CVPPP 2018)

Colliaux D., Wintz T., Hanappe P. Bringing phenotyping to the field: An evaluation of 3d reconstruction of plants in outdoor enironment, Venice 2017 ICCV’17 workshop on computer vision problems in plant phenotyping.


I studied mathematics at École Normale Supérieure de Rennes, before completing a Master's degree in mathematics applied to machine learning and computer vision at École Normale Supérieure de Cachan. During my PhD, I studied mathematical models of wave based methods for medical imaging applications. I defended my PhD thesis from PSL University entitled "super-resolution in wave imaging" in June 2017. I joined the sustainability team of Sony CSL Paris in September 2017.


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