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    A hybrid approach of data envelopment analysis based grey relational analysis: a study on egg yield
    (ZOOLOGICAL SOC PAKISTAN, 2019) Küçükönder, Hande; Demirarslan, Pınar Çelebi; Burgut, Aykut; Boğa, Mustafa
    The aim of the study is to evaluate the effect of conditions of both feeding and the climate in poultry house on production performance in a commercial poultry enterprise with a hybrid approach. In accordance with this purpose, the hybrid approach has two main objectives: i) Determination of the effective period for which the output factors [(chicken survival rate (%), egg yield (%)] are optimized at the same time and the appropriate value ranges for the input factors [temperature (degrees C), humidity (%) and feed per hen (g)] that provide effectiveness ii) Determination of targeted improvement values for the ineffective months to become effective. With this hybrid approach, which is based on the integration of the Data Envelopment Analysis (DEA) and Grey Relational Analysis (GRA), the effective months are determined by DEA method and a performance rank is performed between the effective months by GRA method. It has been investigated whether the results of different Multicriteria Decision Making (MCDM) techniques combined with the data fusion technique support the proposed hybrid approach's results. In this context, the proposed hybrid approach was applied to evaluate the monthly production performance of a commercial enterprise with Lohman Brown genus 8000 chickens. According to the findings of the analysis, it was seen that January, March, October, November and December are the months when production performance is high. When these months were ranked among themselves, it was observed that January, March and November are the first three ranks, respectively, and that the rank was also supported by the combined results of different techniques. As a result, in terms of production performance for the enterprise, it can be said that the optimum temperature is 20.25 degrees C-26.41 degrees C, humidity ratio is 47.60%-54.25%, and feed amount per hen is 98-128 g.
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    Classifying Milk Yield Using Deep Neural Network
    (Zoological Soc Pakistan, 2020) Boga, Mustafa; Cevik, Kerim Kursat; Burgut, Aykut
    This study aim to describe the impact of the number of lactation, lactation days, age at first calving and breeding, and number of insemination (ratio) on cattle milk yield (last seven days in average). For this purpose, the milk yields of 156 Holstein Friesian cattle were investigated according to different age, lactation, calving and insemination associated parameters. Optimum values in literature were organized by an expert in establishing classification data. The expert determined the classes of the outputs data (average milk) through the input data (calving age, milking days, number of lactation and insemination). Applying deep neural networks, we established that average classification success of the system was 69.23% as a result of 6-Layers Cross-Verification Test which is commonly used in the literature for small datasets. In these datasets, it was found that 84 animals had GOOD, 39 animals carried POOR and 33 animals possessed MEDIUM milk yield. It was revealed that there is provided animal raising conditions by 53,84% (84/156*100); therefore, there is no professional farm management. Taken together, the finding show that there is a need of additional controlled management on animal raising and mistakes of the enterprise need to be recovered as early as possible.
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    Computer-Assisted Automatic Egg Fertility Control
    (2019) Boğa, Mustafa; Çevik, Kerim Kürşat; Koçer, Hasan Erdinç; Burgut, Aykut
    This research aimed to determine the fertilization control of the eggs in an incubator between 0th and 5th days by image processing techniques via low-priced tools. Three different datasets that were composed of eggs whose images taken at different times in the incubator were prepared. Several filtering and morphology methods, gray level conversion and dynamic thresholding were utilized to process the 15 egg images. Moreover, the original processing codes based on the problem were given. White and Black percentages of binary images were utilized to determine the egg control. According to the test results, for the first dataset; 73.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy was achieved on the fourth day, for the second dataset; 93.34% of fertility accuracy was achieved on the third day; 93.34% of fertility accuracy was achieved again on the fourth day; for the third dataset, 93.34% of fertility accuracy was achieved on the third day; 100% of fertility accuracy again was achieved on the fourth day. When the results were evaluated, it was seen that egg fertility has been determined successfully automated with low cost tools.