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  • 1.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    From classical methods to animal biometrics: A review on cattle identification and tracking2016In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 123, p. 423-435Article in journal (Refereed)
    Abstract [en]

    Cattle, buffalo and cow, identification has recently played an influential role towards understanding disease trajectory, vaccination and production management, animal traceability, and animal ownership assignment. Cattle identification and tracking refers to the process of accurately recognizing individual cattle and their products via a unique identifier or marker. Classical cattle identification and tracking methods such as ear tags, branding, tattooing, and electrical methods have long been in use; however, their performance is limited due to their vulnerability to losses, duplications, fraud, and security challenges. Owing to their uniqueness, immutability, and low costs, biometric traits mapped into animal identification systems have emerged as a promising trend. Biometric identifiers for beef animals include muzzle print images, iris patterns, and retinal vascular patterns. Although using biometric identifiers has replaced human experts with computerized systems, it raises additional challenges in terms of identifier capturing, identification accuracy, processing time, and overall system operability. This article reviews the evolution in cattle identification and tracking from classical methods to animal biometrics. It reports on traditional animal identification methods and their advantages and problems. Moreover, this article describes the deployment of biometric identifiers for effectively identifying beef animals. The article presents recent research findings in animal biometrics, with a strong focus on cattle biometric identifiers such as muzzle prints, iris patterns, and retinal vascular patterns. A discussion of current challenges involved in the biometric-based identification systems appears in the conclusions, which may drive future research directions.

  • 2.
    Berglund, Anders
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Broman, Olof
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Grönlund, Anders
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Fredriksson, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Improved log rotation using information from a computed tomography scanner2013In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 90, p. 152-158Article in journal (Refereed)
    Abstract [en]

    The development of an industrial computed tomography scanner for the sawmilling industry raises the question of how to find a production strategy that uses a computed tomography scanner in the sawmill production line to its full potential. This study was focused on a Scandinavian sawmill processing Scots pine (Pinus sylvestris) and Norway spruce (Picea abies). The potential value increase when allowing an alternative log rotation other than the horns down position was investigated using a log breakdown simulation. The resulting data was analysed with respect to the size of the log rotational step, an introduced rotational error of the sawing machine and different price differences between the quality grades. It was also of interest to define the outer log properties that characterise the logs sawn for the greatest profit return close to the horns down position compared to logs sawn for a greater profit return in a different log rotation. Such characteristics can be used to reduce the number of degrees of freedom in an optimisation and consider instead other parameters, such as positioning and sawing pattern. Other defects such as pitch pockets, splits and rot are also of interest. The results shows that there is a potential value increase when applying the log rotation that maximises the value for each log instead of processing all logs in the horns down position. However, the potential value increase depends on the rotational error of the used sawing machine and the price differences between the quality grades. The log properties that differ between logs sawn for the greatest profit return close to the horns down position compared to a different log rotation are the bow height and the log taper. Unfortunately, predictability of log rotation for greatest profit return based on the outer properties of logs is poor. It is not possible to differentiate logs which would be sawn for the greatest profit return close to the horns down position from those where a different log rotation results in the greatest profit return, based only on their outer properties.

  • 3.
    Gronskyte, Ruta
    et al.
    DTU Compute, Technical University of Denmark.
    Clemmensen, Line Harder
    DTU Compute, Technical University of Denmark.
    Hviid, Marchen Sonja
    Danish Meat Research Institute, Taastrup.
    Kulahci, Murat
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Business Administration and Industrial Engineering.
    Pig herd monitoring and undesirable tripping and stepping prevention2015In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 119, p. 51-60Article in journal (Refereed)
    Abstract [en]

    Humane handling and slaughter of livestock are of major concern in modern societies. Monitoring animal wellbeing in slaughterhouses is critical in preventing unnecessary stress and physical damage to livestock, which can also affect the meat quality. The goal of this study is to monitor pig herds at the slaughterhouse and identify undesirable events such as pigs tripping or stepping on each other. In this paper, we monitor pig behavior in color videos recorded during unloading from transportation trucks. We monitor the movement of a pig herd where the pigs enter and leave a surveyed area. The method is based on optical flow, which is not well explored for monitoring all types of animals, but is the method of choice for human crowd monitoring. We recommend using modified angular histograms to summarize the optical flow vectors. We show that the classification rate based on support vector machines is 93% of all frames. The sensitivity of the model is 93.5% with 90% specificity and 6.5% false alarm rate. The radial lens distortion and camera position required for convenient surveillance make the recordings highly distorted. Therefore, we also propose a new approach to correct lens and foreshortening distortions by using moving reference points. The method can be applied real-time during the actual unloading operations of pigs. In addition, we present a method for identification of the causes leading to undesirable events, which currently only runs off-line. The comparative analysis of three drivers, which performed the unloading of the pigs from the trucks in the available datasets, indicates that the drivers perform significantly differently. Driver 1 has 2.95 times higher odds to have pigs tripping and stepping on each other than the two others, and Driver 2 has 1.11 times higher odds than Driver 3.

  • 4.
    Johansson, Erik
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Johansson, Dennis
    SP Technical Research Institute of Sweden, Skellefteå.
    Skog, Johan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Fredriksson, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces2013In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 96, p. 238-245Article in journal (Refereed)
    Abstract [en]

    High speed industrial computed tomography (CT) scanning of sawlogs is new to the sawmill industry and therefore there are no properly evaluated algorithms for detecting knots in such images. This article presents an algorithm that detects knots in CT images of logs by segmenting the knots with variable thresholds on cylindrical shells of the CT images. The knots are fitted to ellipses and matched between several cylindrical shells. Parameterized knots are constructed using regression models from the matched knot ellipses. The algorithm was tested on a variety of Scandinavian Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) with a knot detection rate of 88–94% and generating about 1% falsely detected knots.

  • 5.
    Johansson, Erik
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Pahlberg, Tobias
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Hagman, Olle
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Fast visual recognition of Scots pine boards using template matching2015In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 118, p. 85-91Article in journal (Refereed)
    Abstract [en]

    This paper describes how the image processing technique known as template matching performs when used to recognize boards of Scots pine (Pinus sylvestris L.). Recognition of boards enables tracking of individual boards through an industrial process, which is vital for process optimization.A dataset of 886 Scots pine board images were used as a database to match against. The proposed board recognition method was evaluated by rescanning 44 of the boards and matching these to the larger dataset. Three different template matching algorithms have been investigated while reducing the pixel densities of the board images (downsampling the images). Furthermore, the effect of variations in board length has been tested and the computational speed of the recognition with respect to the database size has been measured. Tests were conducted using the open source software package OpenCV due to its highly optimized code which is essential for applications with high production speed.The conducted tests resulted in recognition rates above 99% for board lengths down to 1 m and pixel densities down to 0.06 pixels/mm. This study concluded that template matching is a good choice for recognition of wooden board surfaces.

  • 6. Johansson, J.
    et al.
    Hagman, Olle
    Oja, Johan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Predicting moisture content and density of Scots pine by microwave scanning of sawn timber2003In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 41, no 1-3, p. 85-90Article in journal (Refereed)
    Abstract [en]

    Non-destructive testing of wood for prediction of strength is significantly influenced by wood density and moisture content. A sensor capable of measuring both density and moisture content would be a good tool to aid in predicting the strength of sawn timber. This study was carried out to investigate the possibility of calibrating a prediction model for the moisture content and density of Scots pine (Pinus sylvestris) using microwave sensors. The material was initially at green moisture content, and thereafter dried in several steps to zero moisture content. At each step all the samples were weighted, scanned with a microwave camera (Satimo 9.4 GHz) and CT scanned with a medical CT scanner (Siemens Somatom AR.T.). The output variables from the microwave camera were used as predictors, and CT images correlated with known moisture content were used as response variables. Multivariate models to predict moisture content and density were calibrated using partial least squares (PLS) regression. The result shows that it is possible to predict both moisture content and density with very high accuracy using microwave sensors

  • 7. Nyström, Jan
    Automatic measurement of fiber orientation in softwoods by using the tracheid effect2003In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 41, no 1-3, p. 91-99Article in journal (Refereed)
    Abstract [en]

    Spiraled grain commonly occurs in softwood trees. Instead of running parallel to the pith, the grain runs spirally around the trunk like a helix. Since wood is an orthotropic material with higher shrinkage perpendicular to than parallel to the fibers, the log will twist when dried, and so will a plank or board cut from it. Several investigations have shown that the magnitude of twist in sawn wood is highly correlated to the fiber orientation, so by measuring the fiber orientation on green lumber, the risk of warp after drying can be indicated. Fiber orientation is also interesting for other purposes, for example stress grading and research applications. We concern the tracheid effect, which utilizes the light conducting properties of the softwood tracheids to measure fiber orientation. A small circular laser beam was projected onto the wood surface. The light was transmitted in the wood and scattered back to form an elliptical shape extended in the direction of the fibers. The ellipse of light was registered with a CMOS camera, and the orientation of the ellipse's major axis was calculated. This method has a correlation coefficient of 0.99996 to manually aligned fiber orientation, and repeated measurements show a standard deviation of 0.2°. Calculation time for one 64×64 pixel image was 67 μs, which must be regarded as useful for industrial applications

  • 8. Oja, Johan
    et al.
    Wallbäcks, Lars
    AssiDomän.
    Grundberg, Stig
    Hägerdal, Erik
    AssiDomän.
    Grönlund, Anders
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Automatic grading of Scots pine (Pinus sylvestris L.) sawlogs using an industrial X-ray log scanner2003In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 41, no 1-3, p. 63-75Article in journal (Refereed)
    Abstract [en]

    The successful running of a sawmill is dependent on its ability to achieve the highest possible value recovery from the sawlogs, i.e. to optimize the use of the raw material. Such optimization requires information about the properties of every log. One method of measuring these properties is to use an X-ray log scanner. The objective was to determine the accuracy when grading Scots pine (Pinus sylvestris L.) sawlogs using an industrial scanner known as the X-ray LogScanner. The study was based on 150 Scots pine sawlogs from a sawmill in northern Sweden. All logs were scanned in the LogScanner at a speed of 125 m/min. The X-ray images were analyzed on-line with measures of different properties as a result (e.g. density and density variations). The logs were then sawn with a normal sawing pattern (50 × 125 mm) and the logs were graded depending on the result from the manual grading of the center boards. Finally, partial least squares (PLS) regression was used to calibrate statistical models that predict the log grade based on the properties measured by the X-ray LogScanner. The study showed that 77-83% of the logs were correctly sorted when using the scanner to sort logs into three groups according to the predicted grade of the center boards. After sawing the sorted logs, 67% of the boards had the correct grade. When scanning the same logs repeatedly, the relative standard deviation of the predicted grade was 12-20%. The study also showed that it is possible to sort out 10 and 16%, respectively, of the material into two groups with high quality logs, without changing the grade distribution of the rest of the material to any great extent

  • 9.
    Pahlberg, Tobias
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Hagman, Olle
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Wood Science and Engineering.
    Thurley, Matthew
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Recognition of boards using wood fingerprints based on a fusion of feature detection methods2015In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 111, p. 164-173Article in journal (Refereed)
    Abstract [en]

    This paper investigates the possibility to automatically match and recognize individual Scots pine (Pinus sylvestris L.) boards using a fusion of two feature detection methods. The first method denoted Block matching method, detects corners and matches square regions around these corners using a normalized Sum of Squared Differences (SSD) measure. The second method denoted the SURF (Speeded-Up Robust Features) matching method, matches SURF features between images (Bay et al., 2008). The fusion of the two feature detection methods improved the recognition rate of wooden floorboards substantially compared to the individual methods. Perfect matching accuracy was obtained for board pieces with more than 20 knots using high quality images. More than 90% matching accuracy was achieved for board pieces with more than 10 knots, using both high- and low quality images.

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