Parcourir par Mots clés libres

VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation. Madec Simon, Irfan Kamran, Velumani Kaaviya, Baret Frédéric, David Etienne, Daubige Gaetan, Bernigaud Samatan Lucas, Serouart Mario, Smith Daniel, James Chrisbin, Camacho Fernando, Guo Wei, De Solan Benoit, Chapman Scott, Weiss Marie. 2023. Scientific Data, 10:302, 12 p.

Food security prediction from heterogeneous data combining machine and deep learning methods. Deleglise Hugo, Interdonato Roberto, Bégué Agnès, Maître d'Hôtel Elodie, Teisseire Maguelonne, Roche Mathieu. 2022. Expert Systems with Applications, 190:116189, 11 p.

Trade-off between deep learning for species identification and inference about predator-prey co-occurrence: Reproducible R workflow integrating models in computer vision and ecological statistics. Gimenez Olivier, Kervellec Maëlis, Fanjul Jean-Baptiste, Chaine Anna, Marescot Lucile, Bollet Yoann, Duchamp Christophe. 2022. Computo, 29 p.

AI naturalists might hold the key to unlocking biodiversity data in social media imagery. August Tom A., Pescott Oliver L., Joly Alexis, Bonnet Pierre. 2020. Patterns, 1 (7):100116, 11 p.

A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction. Goeau Hervé, Mora-Fallas Adán, Champ Julien, Rossington Love Natalie L., Mazer Susan J., Mata-Montero Erick, Joly Alexis, Bonnet Pierre. 2020. Applications in Plant Sciences, 8 (6), n.spéc. Machine Learning in Plant Biology: Advances Using Herbarium Specimen Images:e11368, 11 p.