Institution
University of Córdoba (Spain)
Education•Cordova, Spain•
About: University of Córdoba (Spain) is a education organization based out in Cordova, Spain. It is known for research contribution in the topics: Population & Catalysis. The organization has 12006 authors who have published 22998 publications receiving 537842 citations. The organization is also known as: University of Córdoba (Spain) & Universidad de Córdoba.
Topics: Population, Catalysis, Lithium, Extraction (chemistry), Detection limit
Papers published on a yearly basis
Papers
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TL;DR: There are differences in the seminal profiles of individuals exercising in different modalities, and the differences are more marked as intensity and volume of exercise increase, especially for morphology.
141 citations
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TL;DR: Amphiphile-based supramolecular solvents were assessed for the extraction of trace contaminants in liquid foods and the methods developed were applied to the determination of the target compounds in a variety of commercial foods.
141 citations
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TL;DR: In this paper, the synthesis of biomass-derived furanic compounds catalysed by zeolitic materials is discussed, firstly introducing zeolite-catalysed hydrolysis of di-, oligo-and polysaccharides and isomerization reactions of monomeric sugars, and then the catalytic dehydration reactions of hexoses and pentoses to obtain HMF and furfural are reported.
141 citations
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TL;DR: In this paper, the structural characterization of boron-doped LiCoO2 was carried out using low-spin Ni3+probes and showed a distorted tetrahedral coordination.
140 citations
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01 Dec 2015TL;DR: An excellent performance is obtained using very few labelled data complemented with unlabelled data (semi-supervised approach), which motivates the use of weed maps to design site-specific weed control strategies just when farmers implement the early post-emergence weed control.
Abstract: Graphical abstractDisplay Omitted HighlightsThe problem of constructing a weed mapping model via machine learning techniques is assessedThe combination of spectral properties with vegetation indexes and crop rows helps the predictionA semi-supervised classifier has been proved to perform well for the classification problem assessed with very few information provided by the userAn extended experimental design for weed mapping could be performed considering other crops This paper presents a system for weed mapping, using imagery provided by unmanned aerial vehicles (UAVs) Weed control in precision agriculture is based on the design of site-specific control treatments according to weed coverage A key component is precise and timely weed maps, and one of the crucial steps is weed monitoring, by ground sampling or remote detection Traditional remote platforms, such as piloted planes and satellites, are not suitable for early weed mapping, given their low spatial and temporal resolutions Nonetheless, the ultra-high spatial resolution provided by UAVs can be an efficient alternative The proposed method for weed mapping partitions the image and complements the spectral information with other sources of information Apart from the well-known vegetation indexes, which are commonly used in precision agriculture, a method for crop row detection is proposed Given that crops are always organised in rows, this kind of information simplifies the separation between weeds and crops Finally, the system incorporates classification techniques for the characterisation of pixels as crop, soil and weed Different machine learning paradigms are compared to identify the best performing strategies, including unsupervised, semi-supervised and supervised techniques The experiments study the effect of the flight altitude and the sensor used Our results show that an excellent performance is obtained using very few labelled data complemented with unlabelled data (semi-supervised approach), which motivates the use of weed maps to design site-specific weed control strategies just when farmers implement the early post-emergence weed control
140 citations
Authors
Showing all 12089 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jose M. Ordovas | 123 | 1024 | 70978 |
Liang Cheng | 116 | 1779 | 65520 |
Pedro W. Crous | 115 | 809 | 51925 |
Munther A. Khamashta | 109 | 623 | 50205 |
Luis Serrano | 105 | 452 | 42515 |
Raymond Vanholder | 103 | 841 | 40861 |
Carlos Dieguez | 101 | 545 | 36404 |
David G. Bostwick | 99 | 403 | 31638 |
Leon V. Kochian | 95 | 266 | 31301 |
Abhay Ashtekar | 94 | 366 | 37508 |
Néstor Armesto | 93 | 369 | 26848 |
Manuel Hidalgo | 92 | 538 | 41330 |
Rafael de Cabo | 91 | 317 | 35020 |
Harald Mischak | 90 | 445 | 27472 |
Manuel Tena-Sempere | 87 | 351 | 23100 |