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One of the major difficulty encountered in plant disease epidemiology is the lack of occurrence data. Large-scale and sustainable monitoring efforts are penalized by the lack of experts and the difficulty of diagnosing plant diseases for non-experts. In this context, crowdsourcing plant observation tools (such as Pl@ntNet) could serve as a brave new monitoring methodology. Even if non-healthy plants remain a relatively rare event in such high-throughput image data stream, the number of occurrences might be sufficiently high for several monitoring scenarios. Now, automatically recognizing plant diseases in such crowdsourced image streams is a challenging computer vision problem because of the scarcity of the training data, the low inter-class variability and the rarity of the events. The original approach that we propose to solve these issues is to rely on transfer learning and pro-active learning solutions as a way to set up an innovative and participatory citizen sciences program.

The research work will be implemented by a post-doctoral fellow (18 months) who will be hosted 3 days a week in AMAP unit and 2 days a week in LIRMM.

Project Number : 1604-019

Year : 2016

Type of funding : AAP INTERLABEX

Project type : AAP

Start date :
08 Jan 2018

End date :
15 Jul 2019

Flagship project :

Project leader :
Pierre Bonnet Alexis Joly Sylvie Blangy

Project leader's institution :

Project leader's RU :

Budget allocated :
90000 €

Total budget allocated ( including co-financing) :
90000 €

Funding :