Statistical evaluation of texture analysis from the biocrystallization method

The consumers are becoming more concerned about food quality, especially regarding how, when and where the foods are produced (Haglund et al., 1999; Kahl et al., 2004; Alföldi, et al., 2006). Therefore, during recent years there has been a growing interest in the methods for food quality assessment, especially in the picture-development methods as a complement to traditional chemical analysis of single compounds (Kahl et al., 2006). The biocrystallization as one of the picture-developing method is based on the crystallographic phenomenon that when crystallizing aqueous solutions of dihydrate CuCl2 with adding of organic solutions, originating, e.g., from crop samples, biocrystallograms are generated with reproducible crystal patterns (Kleber & Steinike-Hartung, 1959). Its output is a crystal pattern on glass plates from which different variables (numbers) can be calculated by using image analysis. However, there is a lack of a standardized evaluation method to quantify the morphological features of the biocrystallogram image. Therefore, the main sakes of this research are (1) to optimize an existing statistical model in order to describe all the effects that contribute to the experiment, (2) to investigate the effect of image parameters on the texture analysis of the biocrystallogram images, i.e., region of interest (ROI), color transformation and histogram matching on samples from the project 020E170/F financed by the Federal Ministry of Food, Agriculture and Consumer Protection(BMELV).The samples are wheat and carrots from controlled field and farm trials, (3) to consider the strongest effect of texture parameter with the visual evaluation criteria that have been developed by a group of researcher (University of Kassel, Germany; Louis Bolk Institute (LBI), Netherlands and Biodynamic Research Association Denmark (BRAD), Denmark) in order to clarify how the relation of the texture parameter and visual characteristics on an image is. The refined statistical model was accomplished by using a lme model with repeated measurements via crossed effects, programmed in R (version 2.1.0). The validity of the F and P values is checked against the SAS program. While getting from the ANOVA the same F values, the P values are bigger in R because of the more conservative approach. The refined model is calculating more significant P values. The optimization of the image analysis is dealing with the following parameters: ROI(Region of Interest which is the area around the geometrical center), color transformation (calculation of the 1 dimensional gray level value out of the three dimensional color information of the scanned picture, which is necessary for the texture analysis), histogram matching (normalization of the histogram of the picture to enhance the contrast and to minimize the errors from lighting conditions). The samples were wheat from DOC trial with 4 field replicates for the years 2003 and 2005, “market samples”(organic and conventional neighbors with the same variety) for 2004 and 2005, carrot where the samples were obtained from the University of Kassel (2 varieties, 2 nitrogen treatments) for the years 2004, 2005, 2006 and “market samples” of carrot for the years 2004 and 2005. The criterion for the optimization was repeatability of the differentiation of the samples over the different harvest(years). For different samples different ROIs were found, which reflect the different pictures. The best color transformation that shows efficiently differentiation is relied on gray scale, i.e., equal color transformation. The second dimension of the color transformation only appeared in some years for the effect of color wavelength(hue) for carrot treated with different nitrate fertilizer levels. The best histogram matching is the Gaussian distribution. The approach was to find a connection between the variables from textural image analysis with the different visual criteria. The relation between the texture parameters and visual evaluation criteria was limited to the carrot samples, especially, as it could be well differentiated by the texture analysis. It was possible to connect groups of variables of the texture analysis with groups of criteria from the visual evaluation. These selected variables were able to differentiate the samples but not able to classify the samples according to the treatment. Contrarily, in case of visual criteria which describe the picture as a whole there is a classification in 80% of the sample cases possible. Herewith, it clearly can find the limits of the single variable approach of the image analysis (texture analysis).

Digital image analysis as a tool to estimate legume contributions in legume-grass swards

Summary: Productivity and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. For these objectives the botanical composition of the swards is of particular importance, especially, the content of legumes due to their ability to fix airborne nitrogen. As it can vary considerably within a field, a non-destructive detection method while doing other tasks would facilitate a more targeted sward management and could predict the nitrogen supply of the soil for the subsequent crop. This study was undertaken to explore the potential of digital image analysis (DIA) for a non destructive prediction of legume dry matter (DM) contribution of legume-grass mixtures. For this purpose an experiment was conducted in a greenhouse, comprising a sample size of 64 experimental swards such as pure swards of red clover (Trifolium pratense L.), white clover (Trifolium repens L.) and lucerne (Medicago sativa L.) as well as binary mixtures of each legume with perennial ryegrass (Lolium perenne L.). Growth stages ranged from tillering to heading and the proportion of legumes from 0 to 80 %. Based on digital sward images three steps were considered in order to estimate the legume contribution (% of DM): i) The development of a digital image analysis (DIA) procedure in order to estimate legume coverage (% of area). ii) The description of the relationship between legume coverage (% area) and legume contribution (% of DM) derived from digital analysis of legume coverage related to the green area in a digital image. iii) The estimation of the legume DM contribution with the findings of i) and ii). i) In order to evaluate the most suitable approach for the estimation of legume coverage by means of DIA different tools were tested. Morphological operators such as erode and dilate support the differentiation of objects of different shape by shrinking and dilating objects (Soille, 1999). When applied to digital images of legume-grass mixtures thin grass leaves were removed whereas rounder clover leaves were left. After this process legume leaves were identified by threshold segmentation. The segmentation of greyscale images turned out to be not applicable since the segmentation between legumes and bare soil failed. The advanced procedure comprising morphological operators and HSL colour information could determine bare soil areas in young and open swards very accurately. Also legume specific HSL thresholds allowed for precise estimations of legume coverage across a wide range from 11.8 - 72.4 %. Based on this legume specific DIA procedure estimated legume coverage showed good correlations with the measured values across the whole range of sward ages (R2 0.96, SE 4.7 %). A wide range of form parameters (i.e. size, breadth, rectangularity, and circularity of areas) was tested across all sward types, but none did improve prediction accuracy of legume coverage significantly. ii) Using measured reference data of legume coverage and contribution, in a first approach a common relationship based on all three legumes and sward ages of 35, 49 and 63 days was found with R2 0.90. This relationship was improved by a legume-specific approach of only 49- and 63-d old swards (R2 0.94, 0.96 and 0.97 for red clover, white clover, and lucerne, respectively) since differing structural attributes of the legume species influence the relationship between these two parameters. In a second approach biomass was included in the model in order to allow for different structures of swards of different ages. Hence, a model was developed, providing a close look on the relationship between legume coverage in binary legume-ryegrass communities and the legume contribution: At the same level of legume coverage, legume contribution decreased with increased total biomass. This phenomenon may be caused by more non-leguminous biomass covered by legume leaves at high levels of total biomass. Additionally, values of legume contribution and coverage were transformed to the logit-scale in order to avoid problems with heteroscedasticity and negative predictions. The resulting relationships between the measured legume contribution and the calculated legume contribution indicated a high model accuracy for all legume species (R2 0.93, 0.97, 0.98 with SE 4.81, 3.22, 3.07 % of DM for red clover, white clover, and lucerne swards, respectively). The validation of the model by using digital images collected over field grown swards with biomass ranges considering the scope of the model shows, that the model is able to predict legume contribution for most common legume-grass swards (Frame, 1992; Ledgard and Steele, 1992; Loges, 1998). iii) An advanced procedure for the determination of legume DM contribution by DIA is suggested, which comprises the inclusion of morphological operators and HSL colour information in the analysis of images and which applies an advanced function to predict legume DM contribution from legume coverage by considering total sward biomass. Low residuals between measured and calculated values of legume dry matter contribution were found for the separate legume species (R2 0.90, 0.94, 0.93 with SE 5.89, 4.31, 5.52 % of DM for red clover, white clover, and lucerne swards, respectively). The introduced DIA procedure provides a rapid and precise estimation of legume DM contribution for different legume species across a wide range of sward ages. Further research is needed in order to adapt the procedure to field scale, dealing with differing light effects and potentially higher swards. The integration of total biomass into the model for determining legume contribution does not necessarily reduce its applicability in practice as a combined estimation of total biomass and legume coverage by field spectroscopy (Biewer et al. 2009) and DIA, respectively, may allow for an accurate prediction of the legume contribution in legume-grass mixtures.

Learning analysis by reduction from positive data

Analysis by reduction is a linguistically motivated method for checking correctness of a sentence. It can be modelled by restarting automata. In this paper we propose a method for learning restarting automata which are strictly locally testable (SLT-R-automata). The method is based on the concept of identification in the limit from positive examples only. Also we characterize the class of languages accepted by SLT-R-automata with respect to the Chomsky hierarchy.

Untersuchungen zu kumulativen psychischen und physiologischen Effekten des fliegenden Personals auf der Kurzstrecke

Die fliegerische Tätigkeit auf der Kurzstrecke in der zivilen Luftfahrt unterliegt arbeitsspezifischen Belastungsfaktoren, die sich in wesentlichen Punkten von denen auf der Langstrecke unterscheiden. Eine hohe Arbeitsbelastung auf der Kurzstrecke ist mit vielen Starts und Landungen am Tag verbunden. Neben der Anzahl der Flugabschnitte können auch lange Flugdienstzeiten und/oder unregelmäßige Arbeitszeiten sowie der Zeitdruck während der Einsätze auf der Kurzstrecke zur Belastung für Cockpitbesatzungsmitglieder werden und zu Ermüdungserscheinungen führen. Bisher wurden flugmedizinische und -psychologische Daten hauptsächlich auf der Langstrecke in Bezug auf die Auswirkungen der Jet-Leg Symptomatik und kaum auf der Kurzstrecke erhoben. Deshalb wurde im Rahmen des DLR- Projekts „Untersuchungen zu kumulativen psychischen und physiologischen Effekten des fliegenden Personals auf der Kurzstrecke“ eine Langzeituntersuchung zur Belastung/Beanspruchung, Ermüdung sowie Erholung des Cockpitpersonals auf der Kurzstrecke über jeweils 56 Tage durchgeführt. In Zusammenarbeit mit der Deutschen Lufthansa AG dauerte die Untersuchung zu den Auswirkungen arbeitsspezifischer Belastungsfaktoren auf die Cockpitbesatzungsmitglieder der Boeing 737-Flotte von 2003 bis 2006. ZIEL: Unter Berücksichtigung theoretisch fundierter arbeitspsychologischer Konzepte war das Ziel der Studie, kumulative und akute Effekte auf das Schlaf-Wach-Verhalten, auf die Belastung/Beanspruchung sowie auf die Müdigkeit zu identifizieren, die durch aufeinander folgende Einsätze auf der Kurzstrecke innerhalb eines Zeitraums von acht Wochen auftreten können. Hierfür wurden Daten von 29 Piloten (N=13 Kapitäne; N=16 Erste Offiziere) aufgezeichnet. Das Durchschnittsalter lag bei 33,8 ± 7,9 Jahren (Kapitäne: 42,0 ± 3,8 Jahre; Erste Offiziere: 27,4 ± 2,2 Jahre). METHODEN: Über ein Handheld PC konnten effizient Fragebögen bearbeitet und das Sleep Log sowie das Flight Log geführt werden. Die subjektive Ermüdung und Arbeitsbeanspruchung wurden durch standardisierte Fragebögen (z.B. Ermüdungsskala von Samn & Perelli (1982), NASA-TLX) operationalisiert. Im Sleep Log und im Flight Log wurden das Schlaf-Wach-Verhalten sowie flugspezifische Daten dokumentiert (z.B. Dienstbeginn, Dienstende, Flugabschnitte, Zielorte, etc.). Der Schlaf-Wach-Zyklus wurde mittels der Aktimetrie während des gesamten Messverlaufs aufgezeichnet. Die objektive Leistungsfähigkeit wurde täglich morgens und abends mit Hilfe einer computergestützten Psychomotor Vigilance Task (PVT) nach Dinges & Powell (1985) erfasst. Die Leistung in der PVT diente als Indikator für die Ermüdung eines Piloten. Zusätzliche Befragungen mit Paper-Pencil-Fragebögen sollten Aufschluss über relevante, psychosoziale Randbedingungen geben, die bei den täglichen Erhebungen nicht berücksichtigt wurden (z.B. Arbeitszufriedenheit; Essgewohnheiten; Kollegenbeziehungen). ERGEBNISSE: Unter Beachtung kumulativer Effekte wurde über die Studiendauer keine Veränderung in der Schlafqualität und im Schlafbedürfnis festgestellt. Die Müdigkeit nahm dagegen während der achtwöchigen Untersuchung zu. Die Reaktionszeit in der PVT zeigte an Flugdiensttagen eine Verschlechterung über die Zeit. Insgesamt wurden keine kritischen längerfristigen Effekte analysiert. Akute signifikante Effekte wurden bei der Ermüdung, der Gesamtbelastung und der Leistungsfähigkeit an Flugdiensttagen gefunden. Die Ermüdung als auch die Gesamtbelastung stiegen bei zunehmender Flugdienstdauer und Leganzahl und die Leistung nahm in der PVT ab. Der „time on task“ Effekt zeigte sich besonders in der Ermüdung durch die fliegerische Tätigkeit ab einer Flugdienstzeit von > 10 Stunden und > 4 Legs pro Tag. SCHLUSSFOLGERUNG: Mit diesen Ergebnissen konnte eine wissenschaftliche Datenbasis geschaffen werden aus der Empfehlungen resultieren, wie die Einsatzplanung für das Cockpitpersonal auf der Kurzstrecke unter flugmedizinischen und flugpsychologischen Gesichtspunkten optimiert werden kann. Zudem kann ein sachgerechter Beitrag im Rahmen der Diskussion zur Flugdienst- und Ruhezeitenregelung auf europäischer Ebene geleistet werden.