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Article: Classification of Sidr honey and detection of sugar adulteration using right angle fluorescence spectroscopy and chemometrics.

Ali, Hina / Rafique, Khalid / Ullah, Rahat / Saleem, M / Ahmad, Iftikhar

European food research and technology = Zeitschrift fur Lebensmittel-Untersuchung und -Forschung. A

2022 Volume 248, Issue 7, Page(s) 1823–1829

Abstract: Sidr honey is vulnerable to adulteration with low-grade honey and sugar syrups, which compromises its nutritional and medicinal value, demanding fast and reliable analytical tools for quality assessment. In this study, fluorescence spectroscopy was ... ...

Abstract	Sidr honey is vulnerable to adulteration with low-grade honey and sugar syrups, which compromises its nutritional and medicinal value, demanding fast and reliable analytical tools for quality assessment. In this study, fluorescence spectroscopy was employed to assess the quality of a honey samples, specifically, Sidr, unifloral (Acacia) and multifloral (Acacia, Carisa and Justicia) honey. Fluorescence spectroscopy revealed characteristic spectral signatures of Sidr honey, compared to Acacia and multifloral honey. In addition, cane sugar syrup was artificially added to Sidr honey at different concentrations. These spectral signatures were exploited for the machine-assisted classification of Sidr, sugar syrup and different concentrations of Sidr-sugar mixture. The bagging classification algorithm generated values of sensitivity and specificity close to unity, indicating its ability for efficient discrimination of the samples. Fluorescence spectroscopy in tandem with chemometrics could potentially be used as a rapid analytical tool to identify Sidr honey and its sugar adulteration. Supplementary information: The online version contains supplementary material available at 10.1007/s00217-022-04008-9.
Language	English
Publishing date	2022-04-09
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1359456-4
ISSN	1438-2377 ; 1431-4630
ISSN	1438-2377 ; 1431-4630
DOI	10.1007/s00217-022-04008-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: One-Shot Learning with Pseudo-Labeling for Cattle Video Segmentation in Smart Livestock Farming

Qiao, Yongliang / Xue, Tengfei / Kong, He / Clark, Cameron / Lomax, Sabrina / Rafique, Khalid / Sukkarieh, Salah

Animals. 2022 Feb. 23, v. 12, no. 5

2022

Abstract: Computer vision-based technologies play a key role in precision livestock farming, and video-based analysis approaches have been advocated as useful tools for automatic animal monitoring, behavior analysis, and efficient welfare measurement management. ... ...

Abstract	Computer vision-based technologies play a key role in precision livestock farming, and video-based analysis approaches have been advocated as useful tools for automatic animal monitoring, behavior analysis, and efficient welfare measurement management. Accurately and efficiently segmenting animals’ contours from their backgrounds is a prerequisite for vision-based technologies. Deep learning-based segmentation methods have shown good performance through training models on a large amount of pixel-labeled images. However, it is challenging and time-consuming to label animal images due to their irregular contours and changing postures. In order to reduce the reliance on the number of labeled images, one-shot learning with a pseudo-labeling approach is proposed using only one labeled image frame to segment animals in videos. The proposed approach is mainly comprised of an Xception-based Fully Convolutional Neural Network (Xception-FCN) module and a pseudo-labeling (PL) module. Xception-FCN utilizes depth-wise separable convolutions to learn different-level visual features and localize dense prediction based on the one single labeled frame. Then, PL leverages the segmentation results of the Xception-FCN model to fine-tune the model, leading to performance boosts in cattle video segmentation. Systematic experiments were conducted on a challenging feedlot cattle video dataset acquired by the authors, and the proposed approach achieved a mean intersection-over-union score of 88.7% and a contour accuracy of 80.8%, outperforming state-of-the-art methods (OSVOS and OSMN). Our proposed one-shot learning approach could serve as an enabling component for livestock farming-related segmentation and detection applications.
Keywords	cattle ; computer vision ; data collection ; feedlots ; neural networks ; prediction
Language	English
Dates of publication	2022-0223
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani12050558
Database	NAL-Catalogue (AGRICOLA)

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Article: Automated aerial animal detection when spatial resolution conditions are varied

Brown, Jasper / Qiao, Yongliang / Clark, Cameron / Lomax, Sabrina / Rafique, Khalid / Sukkarieh, Salah

Computers and electronics in agriculture. 2022 Feb., v. 193

2022

Abstract: Knowing where livestock are located enables optimized management and mustering. However, Australian farms are large meaning that many of Australia’s livestock are unmonitored which impacts farm profit, animal welfare and the environment. Effective animal ...

Abstract	Knowing where livestock are located enables optimized management and mustering. However, Australian farms are large meaning that many of Australia’s livestock are unmonitored which impacts farm profit, animal welfare and the environment. Effective animal localisation and counting by analysing satellite imagery overcomes this management hurdle however, high resolution satellite imagery is expensive. Thus, to minimise cost the lowest spatial resolution data that enables accurate livestock detection should be selected. In our work, we determine the association between object detector performance and spatial degradation for cattle, sheep and dogs. Accurate ground truth was established using high resolution drone images which were then downsampled to various ground sample distances (GSDs). Both circular and cassegrain aperture optics were simulated to generate point spread functions (PSFs) corresponding to various optical qualities. By simulating the PSF, rather than approximating it as a Gaussian, the images were accurately degraded to match the spatial resolution and blurring structure of satellite imagery. Two existing datasets were combined and used to train and test a YoloV5 object detection network. Detector performance was found to drop steeply around a GSD of 0.5 m/px and was associated with PSF matrix structure within this GSD region. Detector mAP performance fell by 52% when a cassegrain, rather than circular, aperture was used at a 0.5 m/px GSD. Overall blurring magnitude also had a small impact when matched to GSD, as did the internal network resolution. Our results here inform the selection of remote sensing data requirements for animal detection tasks, allowing farmers and ecologists to use more accessible medium resolution imagery with confidence.
Keywords	agriculture ; animal welfare ; automation ; cattle ; data collection ; electronics ; farm profitability ; optics ; remote sensing ; sheep ; Australia
Language	English
Dates of publication	2022-02
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	395514-x
ISSN	0168-1699
ISSN	0168-1699
DOI	10.1016/j.compag.2022.106689
Database	NAL-Catalogue (AGRICOLA)

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Article: Classification of Sidr honey and detection of sugar adulteration using right angle fluorescence spectroscopy and chemometrics

Ali, Hina / Rafique, Khalid / Ullah, Rahat / Saleem, M. / Ahmad, Iftikhar

European food research & technology. 2022 July, v. 248, no. 7

2022

Abstract	Sidr honey is vulnerable to adulteration with low-grade honey and sugar syrups, which compromises its nutritional and medicinal value, demanding fast and reliable analytical tools for quality assessment. In this study, fluorescence spectroscopy was employed to assess the quality of a honey samples, specifically, Sidr, unifloral (Acacia) and multifloral (Acacia, Carisa and Justicia) honey. Fluorescence spectroscopy revealed characteristic spectral signatures of Sidr honey, compared to Acacia and multifloral honey. In addition, cane sugar syrup was artificially added to Sidr honey at different concentrations. These spectral signatures were exploited for the machine-assisted classification of Sidr, sugar syrup and different concentrations of Sidr–sugar mixture. The bagging classification algorithm generated values of sensitivity and specificity close to unity, indicating its ability for efficient discrimination of the samples. Fluorescence spectroscopy in tandem with chemometrics could potentially be used as a rapid analytical tool to identify Sidr honey and its sugar adulteration.
Keywords	Acacia ; Justicia ; adulterated products ; algorithms ; cane syrup ; chemometrics ; fluorescence emission spectroscopy ; food research ; honey ; sugars ; technology
Language	English
Dates of publication	2022-07
Size	p. 1823-1829.
Publishing place	Springer Berlin Heidelberg
Document type	Article
ZDB-ID	1359456-4
ISSN	1431-4630 ; 1438-2377
ISSN	1431-4630 ; 1438-2377
DOI	10.1007/s00217-022-04008-9
Database	NAL-Catalogue (AGRICOLA)

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In stock of ZB MED Bonn / Germany

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Article: One-Shot Learning with Pseudo-Labeling for Cattle Video Segmentation in Smart Livestock Farming.

Qiao, Yongliang / Xue, Tengfei / Kong, He / Clark, Cameron / Lomax, Sabrina / Rafique, Khalid / Sukkarieh, Salah

Animals : an open access journal from MDPI

2022 Volume 12, Issue 5

Abstract	Computer vision-based technologies play a key role in precision livestock farming, and video-based analysis approaches have been advocated as useful tools for automatic animal monitoring, behavior analysis, and efficient welfare measurement management. Accurately and efficiently segmenting animals' contours from their backgrounds is a prerequisite for vision-based technologies. Deep learning-based segmentation methods have shown good performance through training models on a large amount of pixel-labeled images. However, it is challenging and time-consuming to label animal images due to their irregular contours and changing postures. In order to reduce the reliance on the number of labeled images, one-shot learning with a pseudo-labeling approach is proposed using only one labeled image frame to segment animals in videos. The proposed approach is mainly comprised of an Xception-based Fully Convolutional Neural Network (Xception-FCN) module and a pseudo-labeling (PL) module. Xception-FCN utilizes depth-wise separable convolutions to learn different-level visual features and localize dense prediction based on the one single labeled frame. Then, PL leverages the segmentation results of the Xception-FCN model to fine-tune the model, leading to performance boosts in cattle video segmentation. Systematic experiments were conducted on a challenging feedlot cattle video dataset acquired by the authors, and the proposed approach achieved a mean intersection-over-union score of 88.7% and a contour accuracy of 80.8%, outperforming state-of-the-art methods (OSVOS and OSMN). Our proposed one-shot learning approach could serve as an enabling component for livestock farming-related segmentation and detection applications.
Language	English
Publishing date	2022-02-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606558-7
ISSN	2076-2615
ISSN	2076-2615
DOI	10.3390/ani12050558
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Manipulating UAV Imagery for Satellite Model Training, Calibration and Testing

Brown, Jasper / Clark, Cameron / Lomax, Sabrina / Rafique, Khalid / Sukkarieh, Salah

2022

Abstract: Modern livestock farming is increasingly data driven and frequently relies on efficient remote sensing to gather data over wide areas. High resolution satellite imagery is one such data source, which is becoming more accessible for farmers as coverage ... ...

Abstract	Modern livestock farming is increasingly data driven and frequently relies on efficient remote sensing to gather data over wide areas. High resolution satellite imagery is one such data source, which is becoming more accessible for farmers as coverage increases and cost falls. Such images can be used to detect and track animals, monitor pasture changes, and understand land use. Many of the data driven models being applied to these tasks require ground truthing at resolutions higher than satellites can provide. Simultaneously, there is a lack of available aerial imagery focused on farmland changes that occur over days or weeks, such as herd movement. With this goal in mind, we present a new multi-temporal dataset of high resolution UAV imagery which is artificially degraded to match satellite data quality. An empirical blurring metric is used to calibrate the degradation process against actual satellite imagery of the area. UAV surveys were flown repeatedly over several weeks, for specific farm locations. This 5cm/pixel data is sufficiently high resolution to accurately ground truth cattle locations, and other factors such as grass cover. From 33 wide area UAV surveys, 1869 patches were extracted and artificially degraded using an accurate satellite optical model to simulate satellite data. Geographic patches from multiple time periods are aligned and presented as sets, providing a multi-temporal dataset that can be used for detecting changes on farms. The geo-referenced images and 27,853 manually annotated cattle labels are made publicly available. Comment: 16 pages, 7 figures, 2 tables
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing
Publishing date	2022-03-21
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Automated Aerial Animal Detection When Spatial Resolution Conditions Are Varied

Brown, Jasper / Qiao, Yongliang / Clark, Cameron / Lomax, Sabrina / Rafique, Khalid / Sukkarieh, Salah

2021

Abstract: Knowing where livestock are located enables optimized management and mustering. However, Australian farms are large meaning that many of Australia's livestock are unmonitored which impacts farm profit, animal welfare and the environment. Effective animal ...

Abstract	Knowing where livestock are located enables optimized management and mustering. However, Australian farms are large meaning that many of Australia's livestock are unmonitored which impacts farm profit, animal welfare and the environment. Effective animal localisation and counting by analysing satellite imagery overcomes this management hurdle however, high resolution satellite imagery is expensive. Thus, to minimise cost the lowest spatial resolution data that enables accurate livestock detection should be selected. In our work, we determine the association between object detector performance and spatial degradation for cattle, sheep and dogs. Accurate ground truth was established using high resolution drone images which were then downsampled to various ground sample distances (GSDs). Both circular and cassegrain aperture optics were simulated to generate point spread functions (PSFs) corresponding to various optical qualities. By simulating the PSF, rather than approximating it as a Gaussian, the images were accurately degraded to match the spatial resolution and blurring structure of satellite imagery. Two existing datasets were combined and used to train and test a YoloV5 object detection network. Detector performance was found to drop steeply around a GSD of 0.5m/px and was associated with PSF matrix structure within this GSD region. Detector mAP performance fell by 52 percent when a cassegrain, rather than circular, aperture was used at a 0.5m/px GSD. Overall blurring magnitude also had a small impact when matched to GSD, as did the internal network resolution. Our results here inform the selection of remote sensing data requirements for animal detection tasks, allowing farmers and ecologists to use more accessible medium resolution imagery with confidence. Comment: 20 pages, 9 figures, 4 tables in appendix
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing
Subject code	006
Publishing date	2021-10-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Screening of multiclass pesticide residues in honey by SPE-GC/MSD: a pilot study.

Rafique, Nazia / Nazir, Sehrish / Akram, Sumaira / Ahad, Karam / Gohar, Afshan / Abbasi, Surriya Tariq / Ahmed, Ijaz / Rafique, Khalid

Environmental monitoring and assessment

2018 Volume 190, Issue 11, Page(s) 666

Abstract: Analytical method for the monitoring of residues of multiclass pesticides (variable chemical structure and chromatographic behavior) in honey has been optimized and in-house validated in the present study. Chemical confirmation of 35 selected pesticides ( ...

Abstract	Analytical method for the monitoring of residues of multiclass pesticides (variable chemical structure and chromatographic behavior) in honey has been optimized and in-house validated in the present study. Chemical confirmation of 35 selected pesticides (in-hive-treated pesticides and pesticides applied for agricultural practices in vicinity of apiaries) has been successfully achieved with the acetonitrile extraction/partitioning and cleanup by modified US EPA solid-phase extraction (SPE) protocol following analysis on the GC/MS DRS Pesticide Screener. The applied extraction procedure has given acceptable recoveries with an associated precision (RSD) for selected pesticides within the range as suggested by SANTE at MQL of 10 μg kg
MeSH term(s)	Environmental Monitoring/methods ; Gas Chromatography-Mass Spectrometry/methods ; Honey/analysis ; Pakistan ; Pesticide Residues/analysis ; Pesticides/analysis ; Pilot Projects ; Solid Phase Extraction
Chemical Substances	Pesticide Residues ; Pesticides
Language	English
Publishing date	2018-10-22
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	782621-7
ISSN	1573-2959 ; 0167-6369
ISSN (online)	1573-2959
ISSN	0167-6369
DOI	10.1007/s10661-018-7041-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Oral clonidine for attenuation of haemodynamic response to laryngoscopy and endotracheal intubation in known hypertensive patients.

Mujahid-ul-Islam / Ahmad, Imtiaz / Rafique, Khalid / Kamal, Rehana / Abbas, Faahad

Journal of Ayub Medical College, Abbottabad : JAMC

2012 Volume 24, Issue 3-4, Page(s) 174–176

Abstract: Background: Sympathetic response associated with laryngoscopy and endotracheal intubation is recognised as a potential cause for a number of complications especially in hypertensive patients. Various methods have been used to attenuate these ... ...

Abstract	Background: Sympathetic response associated with laryngoscopy and endotracheal intubation is recognised as a potential cause for a number of complications especially in hypertensive patients. Various methods have been used to attenuate these haemodynamic responses; however most of the studies are in normotensive patients. The aim of our study was to compare the effect of oral clonidine and 1/V fentanyl with oral placebo and I/V fentanyl in attenuating the haemodynamic responses to laryngoscopy and intubation in known hypertensive patients. Method: In a double blind randomised controlled trial. 60 hypertensive patients, taking antihypertensive drugs and with systolic blood pressure below 160 (mmHg and diastolic blood pressure below 100 mmHg scheduled for elective surgeries, requiring oral endotracheal intubation and age ranging from 40-65 years were included in this study and randomly divided into Group A (clonidine 0.2 mg + fentanyl 2 microg/Kg) and Group B (Placebo + fentanyl 2 microg/Kg). Results: Demographic data were comparable in both groups. There were no statistically significant differences between the two groups in the duration of laryngoscopy and intubation. There was statistically significant attenuation in heart rate in both groups (p = 0.020). The trends of attenuation of systolic blood pressure, diastolic blood pressure and mean arterial pressure in Group A compared to Group B, were statistically significant (p = 0.034, 0.011, 0.011 respectively). Conclusion: Clonidine, under the present study design attenuates the haemodynamic response to laryngoscopy and endotracheal intubation in known hypertensive patients.
MeSH term(s)	Administration, Oral ; Adult ; Aged ; Antihypertensive Agents/administration & dosage ; Blood Pressure/drug effects ; Clonidine/administration & dosage ; Female ; Heart Rate/drug effects ; Hemodynamics/drug effects ; Humans ; Hypertension/drug therapy ; Intubation, Intratracheal ; Laryngoscopy ; Male ; Middle Aged
Chemical Substances	Antihypertensive Agents ; Clonidine (MN3L5RMN02)
Language	English
Publishing date	2012-07
Publishing country	Pakistan
Document type	Journal Article ; Randomized Controlled Trial
ZDB-ID	2192473-9
ISSN	1025-9589
ISSN	1025-9589
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Book: Digital Simulation of viscous incompressible flow around a solid sphere

Rafique, Khalid

1971

Author's details	Khalid Rafique
Language	Undetermined
Size	3 ME
Document type	Book
Note	London, Univ., Imperial College, Ph.D.Thesis 1971
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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