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1. Deep-Learning-based Counting Methods, Datasets, and Applications in Agriculture -- A Review

Author

Farjon, Guy, Huijun, Liu, and Edan, Yael

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The number of objects is considered an important factor in a variety of tasks in the agricultural domain. Automated counting can improve farmers decisions regarding yield estimation, stress detection, disease prevention, and more. In recent years, deep learning has been increasingly applied to many agriculture-related applications, complementing conventional computer-vision algorithms for counting agricultural objects. This article reviews progress in the past decade and the state of the art for counting methods in agriculture, focusing on deep-learning methods. It presents an overview of counting algorithms, metrics, platforms, and sensors, a list of all publicly available datasets, and an in-depth discussion of various deep-learning methods used for counting. Finally, it discusses open challenges in object counting using deep learning and gives a glimpse into new directions and future perspectives for counting research. The review reveals a major leap forward in object counting in agriculture in the past decade, led by the penetration of deep learning methods into counting platforms.

Published
2023

6. WEST full tungsten operation with an ITER grade divertor

Author

J. Bucalossi, A. Ekedahl, and the WEST Team, J. Achard, K. Afonin, O. Agullo, T. Alarcon, L. Allegretti, F. Almuhisen, H. Ancher, G. Antar, Y. Anquetin, S. Antusch, V. Anzallo, C. Arnas, J.F. Artaud, M.H. Aumeunier, S.G. Baek, X.Y. Bai, M. Balden, C. Balorin, T. Barbui, A. Barbuti, J. Barlerin, J. Barra, V. Basiuk, T. Batal, O. Baulaigue, A. Bec, M. Becoulet, E. Benoit, E. Bernard, J.M. Bernard, M. Bernert, N. Bertelli, E. Bertrand, P. Beyer, J. Bielecki, P. Bienvenu, R. Bisson, B. Bliewert, G. Bodner, S. Bose, C. Bottereau, C. Bouchand, Y. Boumendjel, F. Bouquey, C. Bourdelle, J. Bourg, S. Brezinsek, F. Brochard, C. Brun, V. Bruno, H. Bufferand, A. Bureau, S. Burles, Y. Camenen, B. Cantone, E. Caprin, M. Carole, S. Carpentier-Chouchana, G. Caulier, F. Causa, N. Cazanave, N. Chanet, O. Chellai, Y. Chen, M. Chernyshova, P. Chmielewski, W. Choe, A. Chomiczewska, G. Ciraolo, F. Clairet, J. Coenen, L. Colas, G. Colledani, J. Colnel, P. Coquillat, E. Corbel, Y. Corre, X. Courtois, T. Czarski, A. Da Ros, R. Daniel, J. Daumas, M. De Combarieu, P. De Vries, C. Dechelle, F. Deguara, R. Dejarnac, J.M. Delaplanche, L.F. Delgado-Aparicio, E. Delmas, L. Delpech, C. Desgranges, P. Devynck, J. Denis, S. Di Genova, R. Diab, A. Diallo, M. Diez, G. Dif-Pradalier, M. Dimitrova, R. Ding, T. Dittmar, L. Doceul, M. Domenes, D. Donovan, D. Douai, L. Dubus, N. Dumas, R. Dumont, F. Durand, A. Durif, F. Durodié, D. Elbeze, S. Ertmer, A. Escarguel, F. Escourbiac, B. Esposito, K. Ezato, F. Faisse, J.L. Farjon, N. Faure, N. Fedorczak, P. Fejoz, F. Felici, C. Fenzi-Bonizec, F. Ferlay, L. Ferrand, L. Fevre, M. Firdaouss, L. Fleury, D. Flouquet, T. Fonghetti, A. Gallo, X. Garbet, J. Garcia, J.L. Gardarein, L. Gargiulo, P. Garibaldi, S. Garitta, J. Gaspar, E. Gauthier, S. Gazzotti, F. Gely, J. Gerardin, G. Gervasini, E. Geulin, M. Geynet, P. Ghendrih, I. Giacalone, C. Gil, S. Ginoux, S. Girard, E. Giroux, G. Giruzzi, M. Goniche, V. Gorse, T. Gray, E. Grelier, C. Grisolia, A. Grosjean, A. Grosman, O. Grover, D. Guibert, D. Guilhem, C. Guillemaut, B. Guillermin, R. Guirlet, J.P. Gunn, Y. Gunsu, T. Gyergyek, S. Hacquin, A. Hakola, J. Harris, J.C. Hatchressian, W. Helou, P. Hennequin, C. Hernandez, L. Hijazi, J. Hillairet, T. Hirai, G.T. Hoang, C. Honoré, M. Houry, A. Huart, G. Huijsmans, P. Huynh, M. Iafrati, F. Imbeaux, N. Imbert, I. Ivanova-Stanik, P. Ivanova, R. Jalageas, A. Jamann, C. Jammes, A. Jardin, L. Jaubert, G. Jiolat, E. Joffrin, C. Johnson, A. Jonas, A. Kirschner, C.C. Klepper, M. Komm, M. Koubiti, S. Kosslow, J. Kovacic, M. Kozeiha, K. Krieger, K. Krol, I. Kudashev, B. Lacroix, L. Laguardia, V. Lamaison, V. Lapleigne, H. Laqua, C. Lau, Y. Lausenaz, R. Lé, M. Le Bohec, N. Lefevre, N. Lemoine, E. Lerche, Y. Lesourd, L. Letellier, M. Lewerentz, Y. Li, A. Liang, P. Linczuk, C. Linsmeier, M. Lipa, X. Litaudon, X. Liu, J. Llorens, T. Loarer, A. Loarte, T. Loewenhoff, G. Lombard, J. Lore, P. Lorenzetto, B. Lu, A. Lumsdaine, R. Lunsford, T. Lunt, G. Luo, P. Magaud, P. Maget, J.F. Mahieu, P. Maini, P. Malard, K. Malinowski, P. Manas, L. Manenc, V. Maquet, Y. Marandet, C. Martin, E.J. Martin, P. Martino, M. Mayer, D. Mazon, S. Mazzi, P. Messina, L. Meunier, D. Midou, G. Miglionico, Y. Mineo, M. Missirlian, R. Mitteau, B. Mitu, D. Moiraf, P. Mollard, G. Momparler, V. Moncada, T. Mondiere, C. Monti, J. Morales, M. Moreau, Ph. Moreau, Y. Moudden, G. Moureau, D. Mouyon, M. Muraglia, T. Nakano, E. Nardon, A. Neff, F. Nespoli, J. Nichols, L. Nicolas, S. Nicollet, R. Nouailletas, M. Ono, V. Ostuni, O. Paillat, C. Parish, H. Park, H. Parrat, J.Y. Pascal, B. Pegourie, F.P. Pellissier, Y. Peneliau, M. Peret, E. Pignoly, G. Pintsuk, R. Pitts, C. Pocheau, A. Podolnik, C. Portafaix, M. Poulos, P. Prochet, A. Puig Sitjes, R. Ragona, M. Rasinski, S. Ratynskaia, G. Raup, X. Regal-Mezin, C. Reux, J. Rice, M. Richou, F. Rigollet, N. Rivals, H. Roche, S. Rodrigues, J. Romazanov, G. Ronchi, C. Ruset, R. Sabot, A. Saille, R. Sakamoto, B. Salamon, F. Samaille, A. Santagiustina, B. Santraine, Y. Sarazin, O. Sauter, Y. Savoie-Peysson, L. Schiesko, M. Scholz, J.L. Schwob, E. Serre, H. Shin, S. Shiraiwa, Ja. Signoret, O. Skalli-Fettachi, P. Sogorb, Y. Song, A. Spring, P. Spuig, S. Sridhar, B. Stratton, C. Talatizi, P. Tamain, R. Tatali, Q. Tichit, A. Torre, L. Toulouse, W. Treutterer, E. Tsitrone, E.A. Unterberg, G. Urbanczyk, G. Van Rooij, N. Varadarajan, S. Vartanian, E. Velly, J.M. Verger, L. Vermare, D. Vezinet, N. Vignal, B. Vincent, S. Vives, D. Volpe, G. Wallace, E. Wang, L. Wang, Y. Wang, Y.S. Wang, T. Wauters, D. Weldon, B. Wirth, M. Wirtz, A. Wojenski, M. Xu, Q.X. Yang, H. Yang, B. Zago, R. Zagorski, B. Zhang, X.J. Zhang, X.L. Zou, and the EUROfusion Tokamak Exploitation Team

Subjects
nuclear fusion, magnetic confinement, tokamak, divertor, WEST, ITER, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The mission of WEST (tungsten-W Environment in Steady-state Tokamak) is to explore long pulse operation in a full tungsten (W) environment for preparing next-step fusion devices (ITER and DEMO) with a focus on testing the ITER actively cooled W divertor in tokamak conditions. Following the successful completion of phase 1 (2016-2021), phase 2 started in December 2022 with the lower divertor made entirely of actively cooled ITER-grade tungsten mono-blocks. A boronization prior the first plasma attempt allowed for a smooth startup with the new divertor. Despite the reduced operating window due to tungsten, rapid progress has been made in long pulse operation, resulting in discharges with a pulse length of 100 s and an injected energy of around 300 MJ per discharge. Plasma startup studies were carried out with equatorial boron nitride limiters to compare them with tungsten limiters, while Ion Cyclotron Resonance Heating assisted startup was attempted. High fluence operation in attached regime, which was the main thrust of the first campaigns, already showed the progressive build up of deposits and appearance of dust, impacting the plasma operation as the plasma fluence increased. In total, the cumulated injected energy during the first campaigns reached 43 GJ and the cumulated plasma time exceeded 5 h. Demonstration of controlled X-Point Radiator regime is also reported, opening a promising route for investigating plasma exhaust and plasma-wall interaction issues in more detached regime. This paper summarises the lessons learned from the manufacturing and the first operation of the ITER-grade divertor, describing the progress achieved in optimising operation in a full W environment with a focus on long pulse operation and plasma wall interaction.

Published
2024
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17. Gut bacteria are essential for normal cuticle development in herbivorous turtle ants

Author

Christophe Duplais, Vincent Sarou-Kanian, Dominique Massiot, Alia Hassan, Barbara Perrone, Yannick Estevez, John T. Wertz, Estelle Martineau, Jonathan Farjon, Patrick Giraudeau, and Corrie S. Moreau

Subjects
Science
Abstract

Microbial symbionts can help their hosts metabolise diverse diets. A study on herbivorous turtle ants identifies the cuticular components which are nitrogen-enriched by gut bacteria, highlighting the role of symbionts in insect evolution.

Published
2021
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32. Leaf Counting: Fusing Network Components for Improved Accuracy

Author

Guy Farjon, Yotam Itzhaky, Faina Khoroshevsky, and Aharon Bar-Hillel

Subjects
image-based plant phenotyping, leaf counting, counting with convolutional neural networks, fusing network components for counting, growth rate estimation, Plant culture, SB1-1110
Abstract

Leaf counting in potted plants is an important building block for estimating their health status and growth rate and has obtained increasing attention from the visual phenotyping community in recent years. Two novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study. The first method performs counting via direct regression but using multiple image representation resolutions to attend leaves of multiple scales. The leaf count from multiple resolutions is fused using a novel technique to get the final count. The second method is detection with a regression model that counts the leaves after locating leaf center points and aggregating them. The algorithms are evaluated on the Leaf Counting Challenge (LCC) dataset of the Computer Vision Problems in Plant Phenotyping (CVPPP) conference 2017, and a new larger dataset of banana leaves. Experimental results show that both methods outperform previous CVPPP LCC challenge winners, based on the challenge evaluation metrics, and place this study as the state of the art in leaf counting. The detection with regression method is found to be preferable for larger datasets when the center-dot annotation is available, and it also enables leaf center localization with a 0.94 average precision. When such annotations are not available, the multiple scale regression model is a good option.

Published
2021
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40. Non‐uniform sampling to enhance the performance of compact NMR for characterizing new psychoactive substances

Author

Castaing‐Cordier, Thomas, primary, Crasnier, Sélina, additional, Dubois, Damien, additional, Ladroue, Virginie, additional, Buleté, Audrey, additional, Prudhomme, Cédric, additional, Charvoz, Céline, additional, Besacier, Fabrice, additional, Jacquemin, Denis, additional, Giraudeau, Patrick, additional, and Farjon, Jonathan, additional

Published
2023
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44. Non‐uniform sampling to enhance the performance of compact NMR for characterizing new psychoactive substances.

Author

Castaing‐Cordier, Thomas, Crasnier, Sélina, Dubois, Damien, Ladroue, Virginie, Buleté, Audrey, Prudhomme, Cédric, Charvoz, Céline, Besacier, Fabrice, Jacquemin, Denis, Giraudeau, Patrick, and Farjon, Jonathan

Subjects
NMR spectrometers, SPECTRAL sensitivity, NUCLEAR magnetic resonance spectroscopy, CRIME laboratories, MOLECULAR structure, MAINTENANCE costs
Abstract

Efficient and robust analytical methods are needed to improve the identification and subsequent regulation of new psychoactive substances (NPS). NMR spectroscopy is a unique method able to determine the structure of small molecules such as NPS even in mixtures. However, high‐field NMR analysis is associated with expensive purchase and maintenance costs. For more than a decade, compact NMR spectrometers have changed this paradigm. It was recently shown that a dedicated analytical workflow combining compact NMR and databases could identify the molecular structure of NPS, in spite of the lower spectral dispersion and sensitivity of compact spectrometers. This approach relies on 1H‐13C HSQC to both recognize NPS and elucidate the structure of unknown substances. Still, its performance is limited by the need to compromise between resolution and experiment time. Here, we show that this strategy can be significantly improved by implementing non‐uniform sampling (NUS) to improve spectral resolution in the 13C dimension of HSQC at no cost in terms of experiment time. Gains in the range of 3 to 4 in resolution are achieved for pure NPS and for a mixture. Finally, 2D HSQC with NUS was applied to improve the identification of NPS with the assistance of databases. The resulting method appears as a useful tool for the characterization of NPS in mixtures, which is essential for forensic laboratories. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Synthesis of ribavirin 2’-Me-C-nucleoside analogues

Author

Fanny Cosson, Aline Faroux, Jean-Pierre Baltaze, Jonathan Farjon, Régis Guillot, Jacques Uziel, and Nadège Lubin-Germain

Subjects
alkynylation, antiviral, cancer, C-nucleosides, ribavirin, Science, Organic chemistry, QD241-441
Abstract

An efficient synthetic pathway leading to two carbonated analogues of ribavirin is described. The key-steps in the synthesis of these ribosyltriazoles bearing a quaternary carbon atom in the 2’-position are an indium-mediated alkynylation and a 1,3-dipolar cyclization.

Published
2017
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