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154 results on '"Goëau, Hervé"'

1. Fully automatic extraction of morphological traits from the Web: utopia or reality?

Author

Marcos, Diego, van de Vlasakker, Robert, Athanasiadis, Ioannis N., Bonnet, Pierre, Goeau, Hervé, Joly, Alexis, Kissling, W. Daniel, Leblanc, César, van Proosdij, André S. J., and Panousis, Konstantinos P.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Plant morphological traits, their observable characteristics, are fundamental to understand the role played by each species within their ecosystem. However, compiling trait information for even a moderate number of species is a demanding task that may take experts years to accomplish. At the same time, massive amounts of information about species descriptions is available online in the form of text, although the lack of structure makes this source of data impossible to use at scale. To overcome this, we propose to leverage recent advances in large language models (LLMs) and devise a mechanism for gathering and processing information on plant traits in the form of unstructured textual descriptions, without manual curation. We evaluate our approach by automatically replicating three manually created species-trait matrices. Our method managed to find values for over half of all species-trait pairs, with an F1-score of over 75%. Our results suggest that large-scale creation of structured trait databases from unstructured online text is currently feasible thanks to the information extraction capabilities of LLMs, being limited by the availability of textual descriptions covering all the traits of interest.

Published
2024

2. Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?

Author

Lefort, Tanguy, Affouard, Antoine, Charlier, Benjamin, Lombardo, Jean-Christophe, Chouet, Mathias, Goëau, Hervé, Salmon, Joseph, Bonnet, Pierre, and Joly, Alexis

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

Deep learning models for plant species identification rely on large annotated datasets. The PlantNet system enables global data collection by allowing users to upload and annotate plant observations, leading to noisy labels due to diverse user skills. Achieving consensus is crucial for training, but the vast scale of collected data makes traditional label aggregation strategies challenging. Existing methods either retain all observations, resulting in noisy training data or selectively keep those with sufficient votes, discarding valuable information. Additionally, as many species are rarely observed, user expertise can not be evaluated as an inter-user agreement: otherwise, botanical experts would have a lower weight in the AI training step than the average user. Our proposed label aggregation strategy aims to cooperatively train plant identification AI models. This strategy estimates user expertise as a trust score per user based on their ability to identify plant species from crowdsourced data. The trust score is recursively estimated from correctly identified species given the current estimated labels. This interpretable score exploits botanical experts' knowledge and the heterogeneity of users. Subsequently, our strategy removes unreliable observations but retains those with limited trusted annotations, unlike other approaches. We evaluate PlantNet's strategy on a released large subset of the PlantNet database focused on European flora, comprising over 6M observations and 800K users. We demonstrate that estimating users' skills based on the diversity of their expertise enhances labeling performance. Our findings emphasize the synergy of human annotation and data filtering in improving AI performance for a refined dataset. We explore incorporating AI-based votes alongside human input. This can further enhance human-AI interactions to detect unreliable observations.

Published
2024

3. Overview of LifeCLEF 2024: Challenges on Species Distribution Prediction and Identification

Author

Joly, Alexis, Picek, Lukáš, Kahl, Stefan, Goëau, Hervé, Espitalier, Vincent, Botella, Christophe, Marcos, Diego, Estopinan, Joaquim, Leblanc, Cesar, Larcher, Théo, Šulc, Milan, Hrúz, Marek, Servajean, Maximilien, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Müller, Henning, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goeuriot, Lorraine, editor, Mulhem, Philippe, editor, Quénot, Georges, editor, Schwab, Didier, editor, Di Nunzio, Giorgio Maria, editor, Soulier, Laure, editor, Galuščáková, Petra, editor, García Seco de Herrera, Alba, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2024
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4. LifeCLEF 2024 Teaser: Challenges on Species Distribution Prediction and Identification

Author

Joly, Alexis, Picek, Lukáš, Kahl, Stefan, Goëau, Hervé, Espitalier, Vincent, Botella, Christophe, Deneu, Benjamin, Marcos, Diego, Estopinan, Joaquim, Leblanc, Cesar, Larcher, Théo, Šulc, Milan, Hrúz, Marek, Servajean, Maximilien, Matas, Jiří, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Durso, Andrew M., Eggel, Ivan, Bonnet, Pierre, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Goharian, Nazli, editor, Tonellotto, Nicola, editor, He, Yulan, editor, Lipani, Aldo, editor, McDonald, Graham, editor, Macdonald, Craig, editor, and Ounis, Iadh, editor

Published
2024
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6. Overview of LifeCLEF 2023: Evaluation of AI Models for the Identification and Prediction of Birds, Plants, Snakes and Fungi

Author

Joly, Alexis, Botella, Christophe, Picek, Lukáš, Kahl, Stefan, Goëau, Hervé, Deneu, Benjamin, Marcos, Diego, Estopinan, Joaquim, Leblanc, Cesar, Larcher, Théo, Chamidullin, Rail, Šulc, Milan, Hrúz, Marek, Servajean, Maximilien, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Giachanou, Anastasia, editor, Li, Dan, editor, Aliannejadi, Mohammad, editor, Vlachos, Michalis, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2023
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7. LifeCLEF 2023 Teaser: Species Identification and Prediction Challenges

Author

Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Picek, Lukáš, Botella, Christophe, Marcos, Diego, Šulc, Milan, Hrúz, Marek, Lorieul, Titouan, Moussi, Sara Si, Servajean, Maximilien, Kellenberger, Benjamin, Cole, Elijah, Durso, Andrew, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Kamps, Jaap, editor, Goeuriot, Lorraine, editor, Crestani, Fabio, editor, Maistro, Maria, editor, Joho, Hideo, editor, Davis, Brian, editor, Gurrin, Cathal, editor, Kruschwitz, Udo, editor, and Caputo, Annalina, editor

Published
2023
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8. Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus

Author

Love, Natalie LR, Bonnet, Pierre, Goëau, Hervé, Joly, Alexis, and Mazer, Susan J

Subjects
Biological Sciences, regional convolutional neural network, object detection, deep learning, visual data classification, climate change, natural history collections, phenological stage annotation, flowering time, phenological shift, phenology, Agricultural, veterinary and food sciences, Biological sciences
Abstract

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI's were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.

Published
2021

9. Machine Learning Using Digitized Herbarium Specimens to Advance Phenological Research.

Author

Pearson, Katelin D, Nelson, Gil, Aronson, Myla FJ, Bonnet, Pierre, Brenskelle, Laura, Davis, Charles C, Denny, Ellen G, Ellwood, Elizabeth R, Goëau, Hervé, Heberling, J Mason, Joly, Alexis, Lorieul, Titouan, Mazer, Susan J, Meineke, Emily K, Stucky, Brian J, Sweeney, Patrick, White, Alexander E, and Soltis, Pamela S

Subjects
biodiversity, climate change, deep learning, machine learning, phenology, Climate Action, Environmental Sciences, Biological Sciences, Ecology
Abstract

Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens-preserved plant material curated in natural history collections-but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.

Published
2020

10. A new fine-grained method for automated visual analysis of herbarium specimens: A case study for phenological data extraction.

Author

Goëau, Hervé, Mora-Fallas, Adán, Champ, Julien, Love, Natalie L Rossington, Mazer, Susan J, Mata-Montero, Erick, Joly, Alexis, and Bonnet, Pierre

Subjects
automated regional segmentation, deep learning, herbarium data, natural history collections, phenological stage annotation, phenophase, regional convolutional neural network, visual data classification, Crop and Pasture Production
Abstract

Premise:Herbarium specimens represent an outstanding source of material with which to study plant phenological changes in response to climate change. The fine-scale phenological annotation of such specimens is nevertheless highly time consuming and requires substantial human investment and expertise, which are difficult to rapidly mobilize. Methods:We trained and evaluated new deep learning models to automate the detection, segmentation, and classification of four reproductive structures of Streptanthus tortuosus (flower buds, flowers, immature fruits, and mature fruits). We used a training data set of 21 digitized herbarium sheets for which the position and outlines of 1036 reproductive structures were annotated manually. We adjusted the hyperparameters of a mask R-CNN (regional convolutional neural network) to this specific task and evaluated the resulting trained models for their ability to count reproductive structures and estimate their size. Results:The main outcome of our study is that the performance of detection and segmentation can vary significantly with: (i) the type of annotations used for training, (ii) the type of reproductive structures, and (iii) the size of the reproductive structures. In the case of Streptanthus tortuosus, the method can provide quite accurate estimates (77.9% of cases) of the number of reproductive structures, which is better estimated for flowers than for immature fruits and buds. The size estimation results are also encouraging, showing a difference of only a few millimeters between the predicted and actual sizes of buds and flowers. Discussion:This method has great potential for automating the analysis of reproductive structures in high-resolution images of herbarium sheets. Deeper investigations regarding the taxonomic scalability of this approach and its potential improvement will be conducted in future work.

Published
2020

11. Overview of LifeCLEF 2022: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

Author

Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Picek, Lukáš, Lorieul, Titouan, Cole, Elijah, Deneu, Benjamin, Servajean, Maximilien, Durso, Andrew, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Navine, Amanda, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Šulc, Milan, Hrúz, Marek, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Barrón-Cedeño, Alberto, editor, Da San Martino, Giovanni, editor, Degli Esposti, Mirko, editor, Sebastiani, Fabrizio, editor, Macdonald, Craig, editor, Pasi, Gabriella, editor, Hanbury, Allan, editor, Potthast, Martin, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2022
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12. LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

Author

Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Picek, Lukáš, Lorieul, Titouan, Cole, Elijah, Deneu, Benjamin, Servajean, Maximilien, Durso, Andrew, Bolon, Isabelle, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Müller, Henning, Šulc, Milan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hagen, Matthias, editor, Verberne, Suzan, editor, Macdonald, Craig, editor, Seifert, Christin, editor, Balog, Krisztian, editor, Nørvåg, Kjetil, editor, and Setty, Vinay, editor

Published
2022
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13. Overview of LifeCLEF 2021: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

Author

Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Picek, Lukáš, Lorieul, Titouan, Cole, Elijah, Deneu, Benjamin, Servajean, Maximillien, Durso, Andrew, Bolon, Isabelle, Glotin, Hervé, Planqué, Robert, de Castañeda, Rafael Ruiz, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Candan, K. Selçuk, editor, Ionescu, Bogdan, editor, Goeuriot, Lorraine, editor, Larsen, Birger, editor, Müller, Henning, editor, Joly, Alexis, editor, Maistro, Maria, editor, Piroi, Florina, editor, Faggioli, Guglielmo, editor, and Ferro, Nicola, editor

Published
2021
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14. LifeCLEF 2021 Teaser: Biodiversity Identification and Prediction Challenges

Author

Joly, Alexis, Goëau, Hervé, Cole, Elijah, Kahl, Stefan, Picek, Lukáš, Glotin, Hervé, Deneu, Benjamin, Servajean, Maximilien, Lorieul, Titouan, Vellinga, Willem-Pier, Bonnet, Pierre, Durso, Andrew M., de Castañeda, Rafael Ruiz, Eggel, Ivan, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Hiemstra, Djoerd, editor, Moens, Marie-Francine, editor, Mothe, Josiane, editor, Perego, Raffaele, editor, Potthast, Martin, editor, and Sebastiani, Fabrizio, editor

Published
2021
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15. Overview of LifeCLEF 2020: A System-Oriented Evaluation of Automated Species Identification and Species Distribution Prediction

Author

Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Deneu, Benjamin, Servajean, Maximillien, Cole, Elijah, Picek, Lukáš, Ruiz de Castañeda, Rafael, Bolon, Isabelle, Durso, Andrew, Lorieul, Titouan, Botella, Christophe, Glotin, Hervé, Champ, Julien, Eggel, Ivan, Vellinga, Willem-Pier, Bonnet, Pierre, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Arampatzis, Avi, editor, Kanoulas, Evangelos, editor, Tsikrika, Theodora, editor, Vrochidis, Stefanos, editor, Joho, Hideo, editor, Lioma, Christina, editor, Eickhoff, Carsten, editor, Névéol, Aurélie, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2020
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16. LifeCLEF 2020 Teaser: Biodiversity Identification and Prediction Challenges

Author

Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Botella, Christophe, Ruiz De Castaneda, Rafael, Glotin, Hervé, Cole, Elijah, Champ, Julien, Deneu, Benjamin, Servajean, Maximillien, Lorieul, Titouan, Vellinga, Willem-Pier, Stöter, Fabian-Robert, Durso, Andrew, Bonnet, Pierre, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jose, Joemon M., editor, Yilmaz, Emine, editor, Magalhães, João, editor, Castells, Pablo, editor, Ferro, Nicola, editor, Silva, Mário J., editor, and Martins, Flávio, editor

Published
2020
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17. Biodiversity Information Retrieval Through Large Scale Content-Based Identification: A Long-Term Evaluation

Author

Joly, Alexis, Goëau, Hervé, Glotin, Hervé, Spampinato, Concetto, Bonnet, Pierre, Vellinga, Willem-Pier, Lombardo, Jean-Christophe, Planqué, Robert, Palazzo, Simone, Müller, Henning, Zhai, ChengXiang, Series Editor, de Rijke, Maarten, Series Editor, Belkin, Nicholas J., Editorial Board Member, Clarke, Charles, Editorial Board Member, Kelly, Diane, Editorial Board Member, Sebastiani, Fabrizio, Editorial Board Member, Ferro, Nicola, editor, and Peters, Carol, editor

Published
2019
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18. Overview of LifeCLEF 2019: Identification of Amazonian Plants, South & North American Birds, and Niche Prediction

Author

Joly, Alexis, Goëau, Hervé, Botella, Christophe, Kahl, Stefan, Servajean, Maximillien, Glotin, Hervé, Bonnet, Pierre, Planqué, Robert, Robert-Stöter, Fabian, Vellinga, Willem-Pier, Müller, Henning, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Crestani, Fabio, editor, Braschler, Martin, editor, Savoy, Jacques, editor, Rauber, Andreas, editor, Müller, Henning, editor, Losada, David E., editor, Heinatz Bürki, Gundula, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2019
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19. LifeCLEF 2019: Biodiversity Identification and Prediction Challenges

Author

Joly, Alexis, Goëau, Hervé, Botella, Christophe, Kahl, Stefan, Poupard, Marion, Servajean, Maximillien, Glotin, Hervé, Bonnet, Pierre, Vellinga, Willem-Pier, Planqué, Robert, Schlüter, Jan, Stöter, Fabian-Robert, Müller, Henning, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Azzopardi, Leif, editor, Stein, Benno, editor, Fuhr, Norbert, editor, Mayr, Philipp, editor, Hauff, Claudia, editor, and Hiemstra, Djoerd, editor

Published
2019
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21. Plant Identification: Experts vs. Machines in the Era of Deep Learning : Deep Learning Techniques Challenge Flora Experts

Author

Bonnet, Pierre, Goëau, Hervé, Hang, Siang Thye, Lasseck, Mario, Šulc, Milan, Malécot, Valéry, Jauzein, Philippe, Melet, Jean-Claude, You, Christian, Joly, Alexis, Furht, Borko, Series Editor, Joly, Alexis, editor, Vrochidis, Stefanos, editor, Karatzas, Kostas, editor, Karppinen, Ari, editor, and Bonnet, Pierre, editor

Published
2018
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23. Overview of LifeCLEF 2018: A Large-Scale Evaluation of Species Identification and Recommendation Algorithms in the Era of AI

Author

Joly, Alexis, Goëau, Hervé, Botella, Christophe, Glotin, Hervé, Bonnet, Pierre, Vellinga, Willem-Pier, Planqué, Robert, Müller, Henning, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Bellot, Patrice, editor, Trabelsi, Chiraz, editor, Mothe, Josiane, editor, Murtagh, Fionn, editor, Nie, Jian Yun, editor, Soulier, Laure, editor, SanJuan, Eric, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2018
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24. Plant Classification Based on Gated Recurrent Unit

Author

Lee, Sue Han, Chang, Yang Loong, Chan, Chee Seng, Alexis, Joly, Bonnet, Pierre, Goeau, Herve, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Bellot, Patrice, editor, Trabelsi, Chiraz, editor, Mothe, Josiane, editor, Murtagh, Fionn, editor, Nie, Jian Yun, editor, Soulier, Laure, editor, SanJuan, Eric, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2018
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26. LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges

Author

Joly, Alexis, Goëau, Hervé, Glotin, Hervé, Spampinato, Concetto, Bonnet, Pierre, Vellinga, Willem-Pier, Lombardo, Jean-Christophe, Planqué, Robert, Palazzo, Simone, Müller, Henning, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Jones, Gareth J.F., editor, Lawless, Séamus, editor, Gonzalo, Julio, editor, Kelly, Liadh, editor, Goeuriot, Lorraine, editor, Mandl, Thomas, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2017
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27. Synergizing Digital, Biological, and Participatory Sciences for Global Plant Species Identification: Enabling access to a worldwide identification service

Author

Bonnet, Pierre, primary, Affouard, Antoine, additional, Lombardo, Jean-Christophe, additional, Chouet, Mathias, additional, Gresse, Hugo, additional, Hequet, Vanessa, additional, Palard, Remi, additional, Fromholtz, Maxime, additional, Espitalier, Vincent, additional, Goëau, Hervé, additional, Deneu, Benjamin, additional, Botella, Christophe, additional, Estopinan, Joaquim, additional, Leblanc, César, additional, Servajean, Maximilien, additional, Munoz, François, additional, and Joly, Alexis, additional

Published
2023
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28. LifeCLEF 2016: Multimedia Life Species Identification Challenges

Author

Joly, Alexis, Goëau, Hervé, Glotin, Hervé, Spampinato, Concetto, Bonnet, Pierre, Vellinga, Willem-Pier, Champ, Julien, Planqué, Robert, Palazzo, Simone, Müller, Henning, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Fuhr, Norbert, editor, Quaresma, Paulo, editor, Gonçalves, Teresa, editor, Larsen, Birger, editor, Balog, Krisztian, editor, Macdonald, Craig, editor, Cappellato, Linda, editor, and Ferro, Nicola, editor

Published
2016
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30. LifeCLEF 2015: Multimedia Life Species Identification Challenges

Author

Joly, Alexis, Goëau, Hervé, Glotin, Hervé, Spampinato, Concetto, Bonnet, Pierre, Vellinga, Willem-Pier, Planqué, Robert, Rauber, Andreas, Palazzo, Simone, Fisher, Bob, Müller, Henning, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Mothe, Josanne, editor, Savoy, Jacques, editor, Kamps, Jaap, editor, Pinel-Sauvagnat, Karen, editor, Jones, Gareth, editor, San Juan, Eric, editor, Capellato, Linda, editor, and Ferro, Nicola, editor

Published
2015
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31. LifeCLEF 2014: Multimedia Life Species Identification Challenges

Author

Joly, Alexis, Goëau, Hervé, Glotin, Hervé, Spampinato, Concetto, Bonnet, Pierre, Vellinga, Willem-Pier, Planque, Robert, Rauber, Andreas, Fisher, Robert, Müller, Henning, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Kanoulas, Evangelos, editor, Lupu, Mihai, editor, Clough, Paul, editor, Sanderson, Mark, editor, Hall, Mark, editor, Hanbury, Allan, editor, and Toms, Elaine, editor

Published
2014
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33. ImageCLEF 2013: The Vision, the Data and the Open Challenges

Author

Caputo, Barbara, Muller, Henning, Thomee, Bart, Villegas, Mauricio, Paredes, Roberto, Zellhofer, David, Goeau, Herve, Joly, Alexis, Bonnet, Pierre, Martinez Gomez, Jesus, Varea, Ismael Garcia, Cazorla, Miguel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Forner, Pamela, editor, Müller, Henning, editor, Paredes, Roberto, editor, Rosso, Paolo, editor, and Stein, Benno, editor

Published
2013
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35. Overview of BirdCLEF 2022: Endangered bird species recognition in soundscape recordings

Author

Kahl, Stefan, Navine, Amanda, Denton, Tom, Klinck, Holger, Hart, Patrick, Glotin, Hervé, Goëau, Hervé, Vellinga, Willem-Pier, Planqué, Robert, Joly, Alexis, Cornell University [New York], University of Hawai'i [Hilo], Google LLC, Cornell Laboratory of Ornithology, Université de Toulon (UTLN), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Xeno-canto foundation, Scientific Data Management (ZENITH), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
bird, evaluation, call, species, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, passive acoustic monitoring, bioacoustics, benchmark, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, audio, collection, identification, song, LifeCLEF, fine-grained classification, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, retrieval
Abstract

International audience; As the "extinction capital of the world", Hawai'i has lost 68% of its native bird species, the consequences of which can harm entire ecosystems. With physical monitoring difficult, scientists have turned to sound recordings, as this approach could provide a passive, low labor, and cost-effective strategy for monitoring endangered bird populations. Current methods for processing large bioacoustic datasets involve manual review of each recording. This requires specialized training and prohibitively large amounts of time. Recent advances in machine learning have made it possible to automatically identify bird songs for common species with ample training data. However, it remains challenging to develop such tools for rare and endangered species. The main goal of the 2022 edition of BirdCLEF was to advance automated detection of rare and endangered bird species that lack large amounts of training data. The competition challenged participants to develop reliable analysis frameworks to detect and identify the vocalizations of rare bird species in continuous Hawaiian soundscapes utilizing limited training data.

Published
2022

38. LifeCLEF 2022 Teaser: An Evaluation of Machine-Learning Based Species Identification and Species Distribution Prediction

Author

Hagen, Matthias, Verberne, Suzan, Macdonald, Craig, Seifert, Christin, Balog, Krisztian, Nørvåg, Kjetil, Setty, Vinay, Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Picek, Lukáš, Lorieul, Titouan, Cole, Elijah, Deneu, Benjamin, Servajean, Maximilien, Durso, Andrew, Bolon, Isabelle, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Müller, Henning, Šulc, Milan, Hagen, Matthias, Verberne, Suzan, Macdonald, Craig, Seifert, Christin, Balog, Krisztian, Nørvåg, Kjetil, Setty, Vinay, Joly, Alexis, Goëau, Hervé, Kahl, Stefan, Picek, Lukáš, Lorieul, Titouan, Cole, Elijah, Deneu, Benjamin, Servajean, Maximilien, Durso, Andrew, Bolon, Isabelle, Glotin, Hervé, Planqué, Robert, Vellinga, Willem-Pier, Klinck, Holger, Denton, Tom, Eggel, Ivan, Bonnet, Pierre, Müller, Henning, and Šulc, Milan

Abstract

Building accurate knowledge of the identity, the geographic distribution and the evolution of species is essential for the sustainable development of humanity, as well as for biodiversity conservation. However, the difficulty of identifying plants, animals and fungi is hindering the aggregation of new data and knowledge. Identifying and naming living organisms is almost impossible for the general public and is often difficult even for professionals and naturalists. Bridging this gap is a key step towards enabling effective biodiversity monitoring systems. The LifeCLEF campaign, presented in this paper, has been promoting and evaluating advances in this domain since 2011. The 2022 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) PlantCLEF: very large-scale plant identification, (ii) BirdCLEF: bird species recognition in audio soundscapes, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) SnakeCLEF: Snake Species Identification in Medically Important scenarios, and (v) FungiCLEF: Fungi recognition from images and metadata.

Published
2022

40. Herbarium records of Streptanthus tortuosus scored for reproductive structures by human observers and machine learning

Author

Love, Natalie, Bonnet, Pierre, Goëau, Hervé, Joly, Alexis, and Susan J. Mazer

Subjects
visual data classification, climate change, natural history collections, deep learning, object detection, flowering time, regional convolutional neural network, phenology, phenological stage annotation
Abstract

This dataset represents 709 herbarium specimens ofStreptanthus tortuosus,a widespread herb in California, which were scored for phenology. Reproductive structures on each sheet were counted by both human observers (manual) and by machine learning (mask R-CNN). These counts were used to calculate a quantitative metic of phenological status (the phenological index or PI).Collection date and relevant climate data (spring maximum temperature, degrees C; winter precipitation, mm)at the location and during the year of specimen collection (YOC) are also included in the dataset.&nbsp

Published
2021
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41. WeedElec: a robotic research platform for individual weed detection and selective electrical weeding

Author

Abdelghafour, Florent, Rançon, Florian, Liu, Shiyu, Champ, Julien, De Rudnicki, Vincent, Guizard, Christophe, Goëau, Hervé, Doussan, Claude, Joly, Alexis, Bonnet, Pierre, Rabatel, Gilles, Information – Technologies – Analyse Environnementale – Procédés Agricoles (UMR ITAP), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and ANR-17-ROSE-0003,WeedElec2017,' Robot de désherbage localisé par procédé électrique haute tension combiné avec une gestion prédictive par vision hyper-spectrale et post-évaluation par drone'(2017)

Subjects
robotic, [SPI]Engineering Sciences [physics], electrical weeding, selective weeding, deep learning
Abstract

International audience; The project ‘WeedElec’ aims at developing a robotic intra-row weeding solution based on a high-voltage selective electrocution. It performs the following tasks: weed detection and location, positioning of the weeding probe according to location results and electrical destruction of weeds. The detection is achieved using an RGB camera associated with the deep-learning solution of the ‘Pl@ntNet’ team. Discrimination results between crops and weeds attained 76% accuracy for maize and 62% for beans (field trials). Mortality rates weeds ranged from 75% (Polygonaceae) to 13% (Brassicaceae) for plants less than one month old (laboratory conditions). Survivors were nevertheless impacted with a severe limitation of their growth.

Published
2021
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44. Attention-Based Recurrent Neural Network for Plant Disease Classification

Author

Lee, Sue Han, Goëau, Hervé, Bonnet, Pierre, Joly, Alexis, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Swinburne University of Technology Sarawak Campus, Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ANR-10-LABX-0004,CeMEB,Mediterranean Center for Environment and Biodiversity(2010), ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), ANR-10-LABX-0001,AGRO,Agricultural Sciences for sustainable Development(2010), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
Identification, Pathologie végétale, Plant Science, Agent pathogène, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], smart farming, Maladie des plantes, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Surveillance des cultures, Methods, crops monitoring, MESH: Plant disease classification, Deep learning,recurrent neural network, Automated visual crops analysis ,Precision agriculture technologies, H20 - Maladies des plantes, precision agriculture technologies, fungi, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], food and beverages, deep learning, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Classification, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [SDE.ES]Environmental Sciences/Environmental and Society, automated visual crops analysis, Mesure phytosanitaire, H50 - Troubles divers des plantes, recurrent neural network, U30 - Méthodes de recherche, Réseau de neurones, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, plant disease classification, pests analysis
Abstract

International audience; Plant diseases have a significant impact on global food security and the world's agricultural economy. Their early detection and classification increase the chances of setting up effective control measures, which is why the search for automatic systems that allow this is of major interest to our society. Several recent studies have reported promising results in the classification of plant diseases from RGB images on the basis of Convolutional Neural Networks (CNN). These studies have been successfully experimented on a large number of crops and symptoms, and they have shown significant advantages in the support of human expertise. However, the CNN models still have limitations. In particular, CNN models do not necessarily focus on the visible parts affected by a plant disease to allow their classification, and they can sometimes take into account irrelevant backgrounds or healthy plant parts. In this paper, we therefore develop a new technique based on a Recurrent Neural Network (RNN) to automatically locate infected regions and extract relevant features for disease classification. We show experimentally that our RNN-based approach is more robust and has a greater ability to generalize to unseen infected crop species as well as to different plant disease domain images compared to classical CNN approaches. We also analyze the focus of attention as learned by our RNN and show that our approach is capable of accurately locating infectious diseases in plants. Our approach, which has been tested on a large number of plant species, should thus contribute to the development of more effective means of detecting and classifying crop pathogens in the near future.

Published
2020
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45. Contribution citoyenne au suivi de la flore d'un parc national français, un exemple remarquable à l'échelle du Parc national des Cévennes

Author

Bonnet, Pierre, Goëau, Hervé, Hopkins, Frantz, Véla, Errol, Sahl, Amandine, Affouard, Antoine, Champ, Julien, Gresse, Hugo, Joly, Alexis, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Parc national des Cévennes (PnC), Ministère de l'Écologie, de l'Energie, du Développement durable et de l'Aménagement du territoire, Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
PlantNet, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Identification botanique, Pl@ntNet, Science citoyenne, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Parc des Cévennes
Abstract

International audience; The diversity of habitats in the Cévennes national Park is home to a rich flora, comprising more than 2400 species (angiosperms, gymnosperms, and ferns). A good knowledge of this flora is essential for the development of adapted management strategies. However, human resources for this purpose are limited, and the support of residents and visitors can help to increase the capacity to identify this biodiversity. As the rapid and correct identification of a large number of plant species by non-botanists is difficult, the Cévennes national Park has invested in the development of a new method based on automatic visual approaches to increase citizen participation. We report this experience here, based on the citizen scientific platform Pl@ntNet. This study underlines both the interest of civil society in this approach, the results it brings, and lays the foundations to facilitate its deployment in other French reserves and parks; La diversité des habitats du Parc national des Cévennes héberge une flore riche, composée de plus de 2400 espèces (angiospermes, gymnospermes et fougères). Une bonne connaissance de cette flore est essentielle pour le développement de stratégies de gestion adaptées. Les ressources humaines étant cependant limitées, l’appui des résidents et visiteurs, peut contribuer à multiplier les capacités de recensement de cette biodiversité. L’identification rapide et correcte d’un grand nombre d’espèces végétales étant difficile par des non-spécialistes de cette flore, le Parc national des Cévennes s’est investi dans le développement d’une nouvelle méthode basée sur des approches visuelles automatiques pour accroître la participation des citoyens. Nous rapportons ici cette expérience, s’appuyant sur la plateforme scientifique citoyenne Pl@ntNet. Cette étude souligne à la fois l’intérêt de la société civile pour cette approche, évalue les résultats qu’elle apporte, et pose les bases pour faciliter son déploiement dans d’autres réserves naturelles et parcs français.

Published
2020
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46. Biodiversity Information Science and Standards 4: e58933 Pl@ntNet Services, a Contribution to the Monitoring and Sharing of Information on the World Flora

Author

Bonnet, Pierre, Champ, Julien, Goëau, Hervé, Stöter, Fabian-Robert, Deneu, Benjamin, Servajean, Maximilien, Affouard, Antoine, Lombardo, Jean-Christophe, Levchenko, Oleksandra, Gresse, Hugo, Joly, Alexis, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), ADVanced Analytics for data SciencE (ADVANSE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), Scientific Data Management (ZENITH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Agropolis Fondation, and #DigitAG., European Project: 863463,Cos4Cloud project, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)

Subjects
Artificial intelligence, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Plant diversity, Pl@ntNet, Multimedia biodiversity data, Citizen science, Plant identification, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Biodiversity informatics
Abstract

International audience; Pl@ntNet is a scientific and citizen platform based on artificial intelligence techniques to help participants more easily identify plants with their smartphones. The identification of plant species is indeed an important step for many scientific, educational and land management activities (for natural or cultivated spaces). This step, which is integrated into various biology training courses, is difficult to develop on a large scale, even for professionals. This difficulty in naming plants by a very large part of the society limits the positive interactions between humans and their environment, and thus reduces awareness of the contribution of plants to the well-being of humanity. It is therefore essential to attempt to solve this problem on a large geographical, taxonomic and sociological scale in order to develop more responsible and environmentally precautionary societies.

Published
2020
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47. Segmentación de instancias para detección automática de malezas y cultivos en campos de cultivo

Author

Bonnet, Pierre, Mata Montero, Erick, Mora Fallas, Adán, Goëau, Hervé, Joly, Alexis, Bonnet, Pierre, Mata Montero, Erick, Mora Fallas, Adán, Goëau, Hervé, and Joly, Alexis

Abstract

Based on recent successful applications of Deep Learning techniques in classification, detection and segmentation of plants, we propose an instance segmentation approach that uses a Mask R-CNN model for weeds and crops detection on farmlands. We evaluated our model performance with the MSCOCO average precision metric, contrasting the use of data augmentation techniques. Results obtained show how the model fits very well in this context, opening new opportunities to automated weed control solutions, at larger scales, Con base en las recientes aplicaciones exitosas de técnicas de Aprendizaje Profundo en la clasificación, detección y segmentación de plantas, proponemos un enfoque de segmentación de instancias utilizando un modelo Mask R-CNN para la detección de malezas y cultivos en tierras de cultivo. Evaluamos el rendimiento de nuestro modelo con la métrica de precisión promedio de MSCOCO, contrastando el uso de técnicas de aumento de datos. Los resultados obtenidos muestran cómo el modelo se adapta muy bien en este contexto, abriendo nuevas oportunidades para soluciones automatizadas de control de malezas a gran escala

Published
2020

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