Résumé:
In recent years, the field of agriculture has been generating a considerable volume of data, par-
ticularly in the area of crop disease monitoring. However, the efficient utilization of this massive
data requires accurate and automated annotation tools.
The main objective of this research is to design a solution capable of automatically extrac-
ting and annotating relevant information on wheat diseases from the data collected in grain wa-
rehouses. To achieve this goal, we propose an approach based on semantic web techniques and
image processing algorithms.
Firstly, we gather domain information to develop an ontology that enables the structuring and
representation of knowledge. Next, we propose a process for extracting and annotating various
symptoms of wheat diseases.
Simultaneously, we design a user-friendly interface that allows users to manually annotate un-
labeled data to improve the accuracy and quality of results. This interface will also provide visua-
lization and management features for annotated data, facilitating collaboration between agricul-
tural experts and data scientists
