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System And Method For Plant Disease Detection Support - EP3754543

The patent EP3754543 was granted to Basf on Apr 6, 2022. The application was originally filed on May 14, 2020 under application number EP20174784A. The patent is currently recorded with a legal status of "Opposition Rejected".

EP3754543

BASF
Application Number
EP20174784A
Filing Date
May 14, 2020
Status
Opposition Rejected
Mar 28, 2025
Grant Date
Apr 6, 2022
External Links
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BAYERDec 21, 2022BIP PATENTSADMISSIBLE

Non-Patent Literature (NPL) Citations (30) New

NPL citations refer to non-patent references such as research papers, articles, or other publications cited during examination or opposition phases.

Citation PhaseReference Text
DESCRIPTION- AMARA, J.BOUAZIZ, B.ALGERGAWY, A. et al., "A deep learning-based approach for banana leaf diseases classification", BTW (Workshops, (20170000), pages 79 - 88
DESCRIPTION- Computers and Electronics in Agriculture, vol. 138, pages 200 - 209
DESCRIPTION- CRUZ, A.C.EL-KEREAMY, A.AMPATZIDIS, Y., "Vision-based grapevine pierces disease detection system using artificial intelligence", 2018 ASABE Annual International Meeting, American Society of Agricultural and Biological Engineers, (20180000), page 1
DESCRIPTION- "Deep learning based classification for paddy pests & diseases recognition", ALFARISY et al., Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence, ACM, (20180000), pages 21 - 25
DESCRIPTION- "Deep learning for plant diseases: Detection and saliency map visualisation", BRAHIMI et al., Human and Machine Learning, Springer, (20180000), pages 93 - 117
DESCRIPTION- MOHANTY, S.P.HUGHES, D.P.SALATHE, M., "Using deep learning for image based plant disease detection", Frontiers in Plant Science, (20160000), page 7
DESCRIPTION- RUSSAKOVSKY et al., "ImageNet Large Scale Visual Recognition Challenge", International Journal of Computer Vision (IJCV, (20150000), vol. 115, doi:10.1007/s11263-015-0816-y, pages 211 - 252, XP035934552
DESCRIPTION- J.G.A., "A review on the main challenges in automatic plant disease identification based on visible range images", Biosystems engineering, (20160000), vol. 144, doi:10.1016/j.biosystemseng.2016.01.017, pages 52 - 60, XP029467644
DESCRIPTION- LU, J. et al., "An in-field automatic wheat disease diagnosis system", Computers and Electronics in Agriculture, (20170000), vol. 142, doi:10.1016/j.compag.2017.09.012, pages 369 - 379, XP085236320
DESCRIPTION- FERENTINOS, K.P., "Deep learning models for plant disease detection and diagnosis", Computers and Electronics in Agriculture, (20180000), vol. 145, doi:10.1016/j.compag.2018.01.009, pages 311 - 318, XP085343179
DESCRIPTION- HE et al., "Deep residual learning for image recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (20160000), doi:10.1109/CVPR.2016.90, pages 770 - 778, XP055536240
OPPOSITION- Anonymous, "/multiple inputs for cnn images and parameters how to merge", (20180714), URL: https://stackoverflow.com/questions/51341613/multiple- inputs-for-cnn-images-and-parameters-how-to-merge, (20230110), XP093012476
OPPOSITION- Magarey R, Et Al., "Nappfast: an internet system for the weather-based mapping of plant pathogens", Plant Dis, (20070101), vol. 91, XP093012478
OPPOSITION- Mehdi Mirza, Osindero Simon, "Conditional generative adversarial nets", arXiv:1411.1784v1 [cs.LG], (20141106), arXiv:1411.1784v1 [cs.LG], URL: https://arxiv.org/abs/1411.1784v1, (20180821), XP055501175
OPPOSITION- JAYME GARCIA ARNAL BARBEDO et al., "A review on the main challenges in automatic plant disease identification based on visible range images", Biosystems engineering, vol. 144, doi:10.1016/j.biosystemseng.2016.01.017, (20160221), pages 52 - 60, XP029467644
OPPOSITION- JIANG LU et al., "An in-field automatic wheat disease diagnosis system", Computers and Electronics in Agriculture, (20170925), vol. 142, doi:10.1016/j.compag.2017.09.012, pages 369 - 379, XP085236320
OPPOSITION- KONSTANTINOS P. FERENTINOS, "Deep learning models for plant disease detection and diagnosis", Computers and Electronics in Agriculture, (20180205), vol. 145, doi:10.1016/j.compag.2018.01.009, pages 311 - 318, XP085343179
OPPOSITION- ARTZAI PICON et al., "Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild", Computers and Electronics in Agriculture, vol. 161, doi:10.1016/j.compag.2018.04.002, (20180421), pages 280 - 290, XP055640462
OPPOSITION- ARTZAI PICON et al., "Crop conditional Convolutional Neural networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions", Computers and Electronics in Agriculture, vol. 167, doi:10.1016/j.compag.2019.105093, (20191119), page 105093, XP085931560
OPPOSITION- PICÓN, A. et al., "A statistical recommendation model of mobile services based on contextual evidences", Expert Syst. Appl., (20120000), vol. 39, doi:10.1016/j.eswa.2011.07.056, pages 647 - 653, XP028390581
OPPOSITION- BOUTELL, M. et al., "Beyond pixels: Exploiting camera metadata for photo classification", Pattern Recognition, (20050600), vol. 38, doi:10.1016/j.patcog.2004.11.013, pages 935 - 946, XP004777892
OPPOSITION- BOUTELL M , LUO J, "Bayesian fusion of camera metadata cues in semantic scene classification", PROCEEDINGS OF THE 2004 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 27 JUNE-2 JULY 2004 WASHINGTON, DC, USA, IEEE, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Comput. Soc Los Alamitos, CA, USA, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Comput. Soc Los Alamitos, CA, USA , (20040627), vol. 2, doi:10.1109/CVPR.2004.1315222, ISBN 978-0-7695-2158-9, pages 1 - 8, XP002319660
OPPOSITION- CHEN, D.M. et al., "City-scale landmark identification on mobile devices", CVPR, (20110000), doi:10.1109/CVPR.2011.5995610, pages 737 - 744, XP032038112
OPPOSITION- AHMAD ARIB ALFARISY et al., "Deep learning based classification for paddy pests & diseases recognition", ICMAI '18, Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence, (20180400), doi:10.1145/3208788.3208795, pages 21 - 25, XP055682578
OPPOSITION- Srdjan Sladojevic, Arsenovic Marko, Anderla Andras, Culibrk Dubravko, Stefanovic Darko, "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification", Computational Intelligence and Neuroscience, Hindawi, doi:10.1155/2016/3289801, (20160529), pages 1 - 11, Computational Intelligence and Neuroscience, URL: http://downloads.hindawi.com/journals/cin/2016/3289801.pdf, (20170627), XP055385550
OPPOSITION- Venette Robert C., Kriticos Darren J., Magarey Roger D., Koch Frank H., Baker Richard H. A., Worner Susan P., Gómez Raboteaux Nadilia N., Mckenney Daniel W., Dobesberger Erhard J., Yemshanov Denys, De Barro Paul J., Hutchison William D., Fowler Glenn, Kalaris Tom M., Pedlar John, "Pest Risk Maps for Invasive Alien Species: A Roadmap for Improvement", BIOSCIENCE., BALTIMORE, MD, US, US , (20100501), vol. 60, no. 5, doi:10.1525/bio.2010.60.5.5, ISSN 0006-3568, pages 349 - 362, XP093012477
OPPOSITION- Sharada P. Mohanty, Hughes David P., Salathe Marcel, "Using Deep Learning for Image-Based Plant Disease Detection", Frontiers in Plant Science, doi:10.3389/fpls.2016.01419, pages 1 - 10, Frontiers in Plant Science, URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5032846/pdf/fpls-07-01419.pdf, (20170627), XP055385570
SEARCH- LU JIANG ET AL, "An in-field automatic wheat disease diagnosis system", COMPUTERS AND ELECTRONICS IN AGRICULTURE, (20170925), vol. 142, doi:10.1016/J.COMPAG.2017.09.012, ISSN 0168-1699, pages 369 - 379, XP085236320 [A] 1-15 * the whole document *
SEARCH- FERENTINOS KONSTANTINOS P, "Deep learning models for plant disease detection and diagnosis", COMPUTERS AND ELECTRONICS IN AGRICULTURE, ELSEVIER, AMSTERDAM, NL, (20180205), vol. 145, doi:10.1016/J.COMPAG.2018.01.009, ISSN 0168-1699, pages 311 - 318, XP085343179 [DA] 1-15 * the whole document *
SEARCH- ARTZAI PICON ET AL, "Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild", COMPUTERS AND ELECTRONICS IN AGRICULTURE, AMSTERDAM, NL, (20180421), vol. 161, doi:10.1016/j.compag.2018.04.002, ISSN 0168-1699, pages 280 - 290, XP055640462 [A] 1-15 * the whole document *

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