Browsing by Author "Beyaz, Abdullah"

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    Cooling Potential of Bin Stored Wheat by Summer and Autumn Aeration
    (Ankara Üniversitesi, 2022) Beyaz, Abdullah; Moueddeb, Khaled El; Tarım Makineleri ve Teknolojileri Mühendisliği; Ziraat Fakültesi
    A one-dimensional mathematical model based on the formulation of mass and energy balance in stored grain was used to simulate grains storage conditions. The objective of such simulations was to produce grain ventilation strategies. The model was validated using data obtained from the monitoring of wheat stored in a galvanized steel cylindrical tank with corrugated conical bottom ventilated by perforated distribution pipes. A control strategy based on night time aeration from July to November followed by day time aeration for December to January was applied. Good agreement between the predicted and measured storage conditions has been observed (R2= 0.9698, S.E.= 1.479 °C in average temperature and R2= 0.99, S.E.= 0.00079 kg kg-1for moisture content). Night time grain aeration provided sufficient cooling (temperature near 10 °C in November). However, an 18% grain humidification process was induced. Day time aeration started at the end of November corrected this humidification effect for a grain temperature of 15 °C and a grain moisture content of 15% on dry basis.
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    Elmalarda mekanik zedelenmenin görüntü analiz tekniği ile belirlenmesi
    (Fen Bilimleri Enstitüsü, 2008) Beyaz, Abdullah; Öztürk, Ramazan; Ziraat Mühendisliği
    In this research, it was aimed that to determine oxidation area and shell colour change at the damaged apples by using image analysis technique. Golden Delicious, Granny Smith and Starcrimson varieties of apples were chosen for the tests. A special pendulum unit has been used for impact test. Plastic and wood materials have been placed for contact surface. Apples were dropped from different heights. Each apple impacted symmetric two points and two areas that were determine for each height. Impact surface have been placed perpendicularly. After impact, colour change of damaged regions and oxidation areas of each sample have been determined by using a digital camera periodically. These processes have been repeated until the oxidation area determined clearly. Images were evaluated with image analysis software. Relationships between recorded data were determined statistically.
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    Görüntü işleme teknikleri kullanılarak zeytin varyetelerinin tanımlanması
    (Fen Bilimleri Enstitüsü, 2014) Beyaz, Abdullah; Ramazan, Öztürk; Tarım Makineleri
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    Meta-Learning-Based Prediction of Different Corn Cultivars from Color Feature Extraction
    (Ankara Üniversitesi, 2021-03-04) Beyaz, Abdullah; Gerdan, Dilara; Ziraat Fakültesi
    Image analysis techniques are developing as applicable to the approaches of quantitative analysis, which is aimed to determine cultivar grains. Additionally, corn (Zea mays) grain processing companies evaluate the quality of kernels to determine the price of these cultivars. Because of this reason, in the study, a computer image analysis technique was applied on three corn cultivars. These were Zea mays L. indentata, Zea mays L. saccharata and a hybrid corn (Yellow sweet corn). These cultivars are commercially important as dry grains in Turkey. In the study, the grain color values were tested in the cultivars from Turkey’s collection. One hundred samples were used for each corn cultivar, and 300 corn grains in total were used for evaluations. Each of nine color parameters (Rmin, Rmean, Rmax, Gmin, Gmean, Gmax, Bmin, Bmean, Bmax) which were obtained from original RGB color channels with maximum and minimum values was evaluated from the digital images of three different corn cultivar grains. The values were analyzed with the help of the Multilayer Perceptron (MLP), Decision Tree (DT), Gradient Boost Decision Tree (GBDT) and Random Forest (RF) algorithms by using the Knime Analytics Platform. The majority voting method was applied to MLP and DT for prediction fusion. All algorithms were run with a 10-fold cross-validation method. The success of prediction accuracy was found as 99% for RF and GBDT, 97.66% for MLP, 96.66% DT and 97.40% for Majority Voting (MAVL). The MAVL method increased the accuracy of DT while decreasing the accuracy of MLP partly for the fusion of MLP and DT.
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    The Effect of Adding Corn Silage at Different Ratios to Orange and Tangerine Wastes on Biogas Production Efficiency
    (Ankara Üniversitesi, 2018-12-05) Dağtekin, Metin; Aybek, Ali; Üçok, Serdar; Beyaz, Abdullah; Ziraat Fakültesi
    In this study, biogas production efficiencies of mixtures obtained by adding corn silage (CS) to citrus industrial wastes at different ratios were determined. Orange (OJPW) and tangerine processing juice wastes (TJPW) (crusts and shells) were selected as materials in the study. 25%, 50%, 75% of CS was added to these selected wastes. Changes in the obtained mixture chemical properties (dry matter, dry organic matter, crude ash, crude protein, crude oil, Acid Detergent Fiber (ADF), Neutral Detergent Fiber (NDF)), biogas production and methane content in the biogas were investigated. The results of the study showed that the highest crude protein content was found in 100% TJPW (10%), raw fat percentage in 100% TJPW (5.14%), dry matter content in 100% CS (93.56%), ADF in 100% CS (22.74%) and the NDF in a mixture of 25% OJPW + 75% CS (45.08%). The highest methane production was determined for a mixture of 100% TJPW and 50% TJPW + 50% OJPW (0.46 m3 kg-1 ODM). Also the highest biogas production was determined in a mixture of 50% OJPW + 50% TJPW (0.90 m3 kg-1 ODM). The mixing of CS in TJPW and OJPW reduced significantly the production of methane and biogas in the mixture. As a result of the statistical analysis, significant differences (P≤0.05) were found in both methane and biogas production of agricultural wastes.