Automated image acquisition and quantification of leaf lesion from plant fungal pathogen

DOI

This workflow integrates a Bash script to monitor automated image acquisition, a series of Jupyter Notebooks for dataset preparation, a training script for model development, a prediction script using the trained model, and post-processing tools for downstream analysis. The pipeline was designed to phenotype plant disease symptoms through the quantification of necrosis caused by Sclerotinia sclerotiorum on Brassica napus (oilseed rape) leaves using deep learning methods.

Identifier
DOI https://doi.org/10.57745/3IYS6I
Metadata Access https://entrepot.recherche.data.gouv.fr/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.57745/3IYS6I
Provenance
Creator GRIMONPONT, MARGOT; PIRY, SYLVAIN ORCID logo
Publisher Recherche Data Gouv
Contributor GRIMONPONT, MARGOT; PIRY, SYLVAIN; Entrepôt Recherche Data Gouv
Publication Year 2025
Rights etalab 2.0; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/etalab-2.0.html
OpenAccess true
Contact GRIMONPONT, MARGOT (INRAE); PIRY, SYLVAIN (INRAE)
Representation
Resource Type Model; Dataset
Format application/x-ipynb+json; application/pdf; text/x-python
Size 143397; 492630; 3337793; 4266081; 29525868; 493179; 7225059; 7065726; 111631; 8916
Version 2.0
Discipline Agriculture, Forestry, Horticulture; Geosciences; Engineering Sciences; Agricultural Sciences; Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Construction Engineering and Architecture; Earth and Environmental Science; Engineering; Environmental Research; Life Sciences; Natural Sciences