Plant disease monitoring in crowdsourced image streams (Plant Health)
- Starting: September 2018
- Field(s): data sciences, bio-informatics, machine learning, computer vision
- Funding: #DigitAg
- Co-Directors: Pierre Bonnet (UMR AMAP, Cirad), Alexis Joly (Equipe ZENITH, Inria) & Sylvie Blangy (CEFE, CNRS).
- Pl@ntNet project: http://identify.plantnet-project.org [vidéo]
Keywords: Machine Learning, Comptuter Vision, Recognition Image Analysis, Plants
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 candidate will work on the evaluation and experimentation of automated visual data analysis, in the aim to evaluate the potential of automatically recognizing plant diseases in crowdsourcing context.
Contact: pierre.bonnet [AT] cirad.fr – Sue Han LEE : sue-han.lee [AT] cirad.fr / adeline87lee [AT] gmail.com – +33 4 67 61 57 63
Social Networks: ResearchGate
Lee S.H., Goëau H., Bonnet P., Joly A. (2020) Attention-Based Recurrent Neural Network for Plant Disease Classification, Frontiers in Plant Science