12 December 2022, 14:00-15:30.
ENS, Hauts du DI.
Intégration de données textuelles pour la détection des risques naturels en agriculture
Agriculture is entering the digital age through data (which opens up precision agriculture) or knowledge (which opens up new decision support tools). Modern technologies and IoT devices have been applied to improve agricultural processes. One application scenario is plant monitoring using sensors and data analysis techniques. However, most existing solutions based on specific devices and imaging technologies require a financial investment, which is inaccessible to small farmers. Furthermore, the lack of farmer input into data collection and decision-making in these solutions raises trust issues between farmers and smart farming technologies. On the other hand, textual data in agriculture, e.g. exchanges among farmers on social networks, can be a source of knowledge. This knowledge has great value when it is formalized, contextualized and integrated with other data. Crowdsensing is a sensing paradigm that allows ordinary people to contribute with data that their mobile devices equipped with sensors collect or generate. Farmers’ observations reflect their knowledge and experience in plant health monitoring. Driven by the increasing connectivity of farmers and the emergence of online farming communities, this thesis proposes:
(1) to use Twitter as an open crowdsensing platform to acquire people’s perceptions of crop health so that we can include farmer participation in agricultural knowledge reconstruction.
(2) to use pre-trained language models as an implicit and domain-specific knowledge base that integrates heterogeneous texts and supports information extraction from text.