Parcourir par Mots clés libres

From prototype to inference: A pipeline to apply deep learning in sorghum panicle detection. James Chrisbin, Gu Yanyang, Potgieter Andries, David Etienne, Madec Simon, Guo Wei, Baret Frédéric, Eriksson Anders, Chapman Scott. 2023. Plant Phenomics, 5:0017, 16 p.

High-throughput 2D+t root system architecture reconstruction and modelling from time-lapse phenotyping data. Fernandez Romain, Crabos Amandine, Maillard Morgan, Nacry Philippe, Pradal Christophe. 2023. In : Book of abstracts the 10th International Conference on Functional-Structural Plant Models (FSPM2023). Tsu-Wei Chen (ed.), Andreas Fricke (ed.), Katrin Kahlen (ed.), Susann Müller (ed.), Hartmut Stützel (ed.). Hannover : Institute of Horticultural Production Systems, 62-63. International Conference on Functional-Structural Plant Models (FSPM2023). 10, Berlin, Allemagne, 27 Mars 2023/31 Mars 2023.

Pl@ntNet Crops: Merging citizen science observations and structured survey data to improve crop recognition for agri-food-environment applications. van der Velde M., Goeau Hervé, Bonnet Pierre, d'Andrimont Raphaël, Yordanov M., Affouard Antoine, Claverie M., Czucz B., Elvekjaer N., Martinez-Sanchez L., Rotllan-Puig X., Sima A., Verhegghen Astrid, Joly Alexis. 2023. Environmental Research Letters, 18:025005, 12 p.

PhenoTrack3D: An automatic high-throughput phenotyping pipeline to track maize organs over time. Daviet Benoît, Fernandez Romain, Cabrera-Bosquet Llorenç, Pradal Christophe, Fournier Christian. 2022. Plant Methods, 18 (1):130, 14 p.

Characterization of RTB product colour change during time. High-throughput phenotyping protocols (HTPP), WP3. Cornet Denis, Meghar Karima (collab.), Davrieux Fabrice (collab.). 2021. Montpellier : RTBfoods Project-CIRAD, 16 p.

Characterization of RTB starch grain size and shape through imaging. High-throughput phenotyping protocols (HTPP), WP3. Cornet Denis, Desfontaines Lucienne, Meghar Karima (collab.), Davrieux Fabrice (collab.). 2021. Montpellier : RTBfoods Project-CIRAD, 14 p.

Hierarchical classification of very small objects: Application to the detection of arthropod species. Tresson Paul, Carval Dominique, Tixier Philippe, Puech William. 2021. IEEE Access, 9 : 63925-63932.

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.

Basics of automated plant identification. Bonnet Pierre, Frame Dawn. 2020. In : Automated forest restoration: Could robots revive rain forests?. Elliott,S. (ed.), Gale G. (ed.), Robertson M. (ed.). Chiang Mai : FORRU-CMU, 158-167. Workshop "Automated Forest Restoration: Could Robots Revive Rain Forests?", Chiang Mai, Thaïlande, Octobre 2015.

Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo. Mora-Fallas Adán, Goeau Hervé, Joly Alexis, Bonnet Pierre, Mata-Montero Erick. 2020. Tecnología en Marcha, 33, n.spéc. Contribuciones a la Conferencia 6th Latin America High Performance Computing Conference (CARLA) : 13-17. Latin America High Performance Computing Conference. 6, Turrialba, Costa Rica, 25 Septembre 2019/27 Septembre 2019.

A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Brichet Nicolas, Fournier Christian, Turc Olivier, Strauss Olivier, Artzet Simon, Pradal Christophe, Welcker Claude, Tardieu François, Cabrera-Bosquet Llorenç. 2017. Plant Methods, 13:96, 12 p.