Zobrazeno 1 - 10
of 107
pro vyhledávání: '"Lars Petersson"'
HairNet2: deep learning to quantify cotton leaf hairiness, a complex genetic and environmental trait
Autor:
Moshiur Farazi, Warren C. Conaty, Lucy Egan, Susan P. J. Thompson, Iain W. Wilson, Shiming Liu, Warwick N. Stiller, Lars Petersson, Vivien Rolland
Publikováno v:
Plant Methods, Vol 20, Iss 1, Pp 1-19 (2024)
Abstract Background Cotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genot
Externí odkaz:
https://doaj.org/article/307372dac3e741bca13f82732c72ebb2
Autor:
David Ahmedt-Aristizabal, Daniel Smith, Muhammad Rizwan Khokher, Xun Li, Adam L. Smith, Lars Petersson, Vivien Rolland, Everard J. Edwards
Publikováno v:
IEEE Access, Vol 12, Pp 102146-102166 (2024)
Accurately predicting grape yield in vineyards is essential for strategic decision-making in the wine industry. Current methods are labour-intensive, costly, and lack spatial coverage, reducing accuracy and cost-efficiency. Efforts to automate and en
Externí odkaz:
https://doaj.org/article/a82a25051d6c4cca825f7fc4824d39a8
Publikováno v:
Signals, Vol 3, Iss 2, Pp 296-312 (2022)
Deep learning in the last decade has been very successful in computer vision and machine learning applications. Deep learning networks provide state-of-the-art performance in almost all of the applications where they have been employed. In this revie
Externí odkaz:
https://doaj.org/article/ffacf33117a44cc4809d042dbd640206
Autor:
Vivien Rolland, Moshiur R. Farazi, Warren C. Conaty, Deon Cameron, Shiming Liu, Lars Petersson, Warwick N. Stiller
Publikováno v:
Plant Methods, Vol 18, Iss 1, Pp 1-19 (2022)
Abstract Background Leaf hairiness (pubescence) is an important plant phenotype which regulates leaf transpiration, affects sunlight penetration, and provides increased resistance or susceptibility against certain insects. Cotton accounts for 80% of
Externí odkaz:
https://doaj.org/article/9da9a171dbed4ff3aabdf437af11f481
Autor:
Fazlul Karim, Mohammed Ali Armin, David Ahmedt-Aristizabal, Lachlan Tychsen-Smith, Lars Petersson
Publikováno v:
Water, Vol 15, Iss 3, p 566 (2023)
Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep
Externí odkaz:
https://doaj.org/article/eff6ff94cd7d4b36b9988b427692db6d
Autor:
Junlin Han, Mehrdad Shoeiby, Tim Malthus, Elizabeth Botha, Janet Anstee, Saeed Anwar, Ran Wei, Mohammad Ali Armin, Hongdong Li, Lars Petersson
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 4297 (2022)
Underwater image restoration is of significant importance in unveiling the underwater world. Numerous techniques and algorithms have been developed in recent decades. However, due to fundamental difficulties associated with imaging/sensing, lighting,
Externí odkaz:
https://doaj.org/article/4a1f5d1d64154d428dca47eedc49210b
Publikováno v:
Sensors, Vol 21, Iss 14, p 4758 (2021)
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare
Externí odkaz:
https://doaj.org/article/50ce0de4895c4d96819fdd55cd3a0c9b
Publikováno v:
Signals; Volume 3; Issue 2; Pages: 296-312
Deep learning in the last decade has been very successful in computer vision and machine learning applications. Deep learning networks provide state-of-the-art performance in almost all of the applications where they have been employed. In this revie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d714285a3d44783764e7e1f4846b964
https://hdl.handle.net/10453/167913
https://hdl.handle.net/10453/167913
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74dd4c097e65cb42fa956e00b7fc2613
https://doi.org/10.1109/wacv56688.2023.00425
https://doi.org/10.1109/wacv56688.2023.00425
Autor:
Nariman Habili, Ernest Kwan, Weihao Li, Christfried Webers, Jeremy Oorloff, Mohammad Ali Armin, Lars Petersson
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250811
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e3892e3f339a3915c9cb03b8c712beb
https://doi.org/10.1007/978-3-031-25082-8_17
https://doi.org/10.1007/978-3-031-25082-8_17