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Öğe A Design Software for Plastic-Covered, Pipe-Framed Greenhouses(Univ Namik Kemal, 2010) Kizil, U.; Genc, L.; Sacan, M.Greenhouses in Turkey are generally light frame structures that are mainly subject to wind and snow loads. Currently, there is no standard design procedure for pipe-framed greenhouse structures. The greenhouse contractors are usually pipe producers or plumbers who don't apply engineering calculations. They basically build the greenhouses based on their experiences. However, pipe-framed greenhouse building design procedures do include the calculation of forces such as dead, wind, and snow, moments, reaction forces, etc. There has been a need for a user-friendly design tool that can minimize the failure of pipe-framed greenhouse structures. A software program is developed using MS Visual Basic programming language. The program employs Kleinlogel equations in the calculations of moments and reaction forces. The simplicity of building pipe-framed system and Kleinlogel equations provide a unique opportunity to develop a simple program. This study gives a detailed explanation of the methods and the software.Öğe Assessment of the effect of salinity on the early growth stage of the common sunflower (Sanay cultivar) using spectral discrimination techniques(Academic Journals, 2008) Turhan, H.; Genc, L.; Smith, S. E.; Bostanci, Y. B.; Turkmen, O. S.Salinity is one of the main limiting factors for agricultural production. This is especially true in arid and semi-arid regions of the world like Turkey. The objective of this study was to determine if the effect of salt concentration on the physiological and physiological features of the sunflower (Helianthus annuus L) could be measured using remote sensing techniques. Sunflower seedlings were grown under controlled conditions and irrigated with Hoagland Solution containing three different concentrations of NaCl (salt) (0.0, 0.5, 1.0 and 1.5%). The results showed that plant growth decreased proportionally with increasing levels of NaCl. Chlorophyll concentration and a Normalized Difference Vegetation Index (NDVI) were derived for the plants using a spectroradiometer. There was found to be a significant (r(2) = 0.76) correlation between chlorophyll and NDVI values. Therefore, factors that can be derived through remote sensing such as NDVI and chlorophyll can be used to indirectly demonstrate the impact salinity has on sunflower plants. Therefore, agriculturalists can assess growth rate changes caused by salinity using remote sensing techniques.Öğe Assessment of Water Stress Using Chlorophyll Readings and Leaf Water Content for Watermelon(Univ Namik Kemal, 2010) Demirel, K.; Genc, L.; Camoglu, G.; Asik, S.The objective of this study was to determine plant water stress using Chlorophyll Readings (ChRs) and Leaf Water Content (LWC) measurements for watermelon in the Canakkale region of western Turkey. ChRs and LWC were measured before (BI) and after irrigation (AI). Six different irrigation treatments (S-100, (control), S-80, S-60 S-40, S-20 and S-0 (non-irrigated)) were applied with drip irrigation. Growth stages were divided into three categories: (1) flowering (F), (2) fruit growth (FG) and (3) ripening and harvest (RH). ChRs and LWC for both irrigation treatments and all growing stages were calculated by means of ANOVA using SPSS for Windows statistical software. It was seen that ChRs and LWC decreased from S-100 to S-0 during growth period. The coefficient of determination (R-2) and linear equation between ChRs and LWC for F, FG and RH stages were found to be 0.751, 0.805 and 0.878, respectively. Result of this study has shown that LWC and ChRs measurements can be used to determine water stress especially F period and the beginning FG periods.Öğe DESIGN AND TEST OF A LOW-COST ELECTRONIC NOSE SYSTEM FOR IDENTIFICATION OF SALMONELLA ENTERICA IN POULTRY MANURE(Amer Soc Agricultural & Biological Engineers, 2015) Kizil, U.; Genc, L.; Rahman, S.; Khaitsa, M. L.; Genc, T. T.The objective of this study was to design and evaluate the performance of a metal-oxide sensor-based electronic nose system (e-nose) for detecting Salmonella enterica in poultry manure. The system has hardware and software components for signal acquisition, data processing, and sample classification. An artificial neural network (ANN) model was used to classify manure samples as Salmonella-positive or Salmonella-negative. Seven manure samples were collected from different broiler houses and divided into four portions. Two portions were spiked with 10(3) and 2 x 10(3) CFU g(-1) of Salmonella enterica (ATCC 13311). The third portion was used for determining natural manure microflora, and the fourth portion was sterilized. All portions were incubated at 37 degrees C for 48 h. A total of 84 e-nose readings were recorded at different time intervals from the manure portions. A multilayer, feed-forward back-propagation ANN model was developed (training step) and validated with the e-nose readings. Of the 84 readings, 48 were used to develop the ANN model and the remainder was used to validate model performance. The model was able to classify the remaining 36 manure samples with an accuracy of 96%. In order to test the actual performance of the ANN model, 16 manure samples were collected from different barns and analyzed. The e-nose system was able to determine the Salmonella status of the manure samples with 100% accuracy.Öğe Determination of Agriculture Land Use and Land Cover Change Using Remote Sensing and GIS in TROIA National Park(Univ Namik Kemal, 2007) Genc, L.; Bostanci, Y. B.The area selected for land use land cover (LULC) dynamics, TROIA national park, is located in the city of Canakkale, TURKEY. The national park covers an area of about 13600 ha. Remote sensing studies especially multi-temporal analysis of changes provides sufficient information about the dynamics of historic landscape. Tasseled Cap Indexes and Normalized Difference Vegetation Index (NDVI) were used to create the new images from Landsat TM 1987 and Landsat TM 2006 images for classification. Supervised classification was applied with ground truth data and auxiliary data collected from different sources such as air photo, cadastral information and others. Four classes of changed and unchanged multi-temporal raster were discriminated from created new images as followed: Active Agriculture, Grassland, Forestry, and Water. Classification accuracy was determined for 1987 image and 2006 image as 81% and 87% respectively. It was found that LULC change was dynamic between classes because of the land consolidation in the region. Grassland was changed to active agriculture area by 75% and to forestry class by 5%. Forested area also converted to active agriculture by 46% and to grassland by 9%. It was concluded that land consolidation project in the study area was the main force to change land cover.Öğe Determination of Land Use And Land Cover Changes in Canakkale Province Using Remote Sensing(Univ Namik Kemal, 2013) Sayi, O.; Genc, L.The Landsat TM/ETM+ images obtained in 2000, 2006 and 2010 were used to generate Land Use and Land Cover (LULC) maps of Canakkale province including forest, grassland, agriculture, water and residential area-bare soil classes. Areas of LULC classes were calculated; changes between 2006-2000, 2010-2006, and 2010-2000 were compared. Accuracy assessments and Kappa statistics were calculated for each district. In Ayvacik, most of the areas were covered by agricultural lands in 2006 and 2010 with 40.13% and 42.70% respectively, while the least area were covered by water class. In Bayramig, while most of the areas were covered by forests in each year, the residential areas and water surfaces were covered less than 1% of total area. In Main Municipal district, Biga, can, Eceabat, Lapseki, and Yenice, the most of the areas were covered by forests, and water surfaces were covered the least area. In Eceabat, the minimum coverage rate was seen in Residential area Bare soil class for every year. In Ezine and Gelibolu, while agricultural areas were classified as the major land cover class, the minor land cover class was classified as water. The maximum accuracy was found for LULC map of Main Municipal district in 2000 (90.20%) while the minimum accuracy rate was found for Bayramig LULC map in 2000 (80.39%).Öğe E-nose identification of Salmonella enterica in poultry manure(Taylor & Francis Ltd, 2015) Kizil, U.; Genc, L.; Genc, T. T.; Rahman, S.; Khaitsa, M. L.A DiagNose II electronic nose (e-nose) system was tested to evaluate the performance of such systems in the detection of the Salmonella enterica pathogen in poultry manure. To build a database, poultry manure samples were collected from 7 broiler houses, samples were homogenised, and subdivided into 4 portions. One portion was left as is; the other three portions were artificially infected with S. enterica. An artificial neural network (ANN) model was developed and validated using the developed database. In order to test the performance of DiagNose II and the ANN model, 16 manure samples were collected from 6 different broiler houses and tested using these two systems. The results showed that DiagNose II was able to classify manure samples correctly as infected or non-infected based on the ANN model developed with a 94% level of accuracy.Öğe Effects of Different Irrigation Levels On Pepper (Capsicum Annum Cv. Kapija) Yield And Quality Parameters in Semi-Arid Conditions(Univ Namik Kemal, 2012) Demirel, K.; Genc, L.; Sacan, M.The effects of different irrigation levels on yield, quality parameters, evapotranspiration, water use efficiency, and irrigation water use efficiency were investigated for pepper (Capsicum Annum Cv. Kapija) plant grown in semi-arid region. The Research was carried out in 2009-2010 years in Canakkale province. Drip irrigation method was applied to irrigate the experimental plots with 4 different irrigation levels (10, 166, 133 and 1100). Total irrigation water amounts ranged from 30 to 567 mm in 2009 and from 62 to 489 mm in 2010 were applied according the treatments. Average seasonal evapotranspiration (ETa) were calculated between 322-796 mm with respect to treatments. Pepper yield were obtained 10.89-44.92 and 4.47-63.64 t ha(-1) in 2009 and 2010, respectively. With respect to irrigation levels, average water use efficiency (WUE), irrigation water use efficiency (IWUE) were changed between 2.36-6.95 kg m(-3) and 0-9.05 kg m(-3), respectively. Average yield response factor (ky) was found 1.468. While considering the both 2009 and 2010 years, differences between quality parameters of irrigation treatments (mean fruit weight, fruit width, fruit lenght, fruit thickness and water soluable dry matter) except pH, were statistically significant (p<0.05).Öğe Using Chlorophyll Meter to Predict Sunflower Nitrogen Content after Olive Solid Waste Applications(Int Soc Horticultural Science, 2009) Kavdir, Y.; Ilay, R.; Turhan, H.; Genc, L.; Kavdir, I.; Sumer, A.Chlorophyll index is an instantaneous measurement of leaf greenness without the destruction of the plant and a new tool to determine plant nitrogen content and associated yield. A pot experiment was conducted under controlled conditions. Olive solid wastes were mixed with soil at the rates of 0, 3, 5 and 7% with and without additional nitrogen and phosphorous sources. Sunflower was grown in pots for two months. Plant length, leaf number, stem thickness, and chlorophyll meter readings were performed weekly. Plant nitrogen contents and plant weights were determined at harvest. Chlorophyll index and plant nitrogen contents were significantly related (r(2) = 0.86) at the V12 stage. The correlations between chlorophyll meter reading and plant biomass was 0.87 while plant N and plant biomass was 0.96. On the other hand, chlorophyll meter estimation of plant N contents in early stages (V2 and V4) of sunflower growth was not statistically significant. Additions of olive solid waste in the soil reduced chlorophyll meter readings and sunflower biomass.Öğe Using Leaf Based Hyperspectral Models for Monitoring Biochemical Constituents and Plant Phenotyping in Maize(Tarbiat Modares Univ, 2016) Kahriman, F.; Demirel, K.; Inalpulat, M.; Egesel, C. O.; Genc, L.The aim of this study was to develop and validate qualitative and quantitative models to discriminate different types of maize and also estimate biochemical constituents. Spectral data were taken from the central leaf of randomly-chosen plants grown in field trials in 2011 and 2012. Leaf chlorophyll and protein content and stalk protein content were determined in the same plants. Four different Support Vector Machine (SVM) models were generated and validated in this study. In qualitative models, maize type was designated as dependent variable while Full Spectral (FS) data (400-1,000 nm) and Spectral Indices (SI) data (34 indices/bands) were independent variables. In the two quantitative models (SVMR-FS and SVMR-SI), independent variables were the same, whereas dependent variables were assigned as the quantitatively measured traits. Results showed the qualitative models to be a robust method of classification for distinguishing different maize types, such as High Oil Maize (HOM), High Protein Maize (HPM) and standard (NORMAL) maize genotypes. The SVMC-FS model was superior to SVMC-SI in terms of the genotypic classification of maize plants. Quantitative models with full spectral data gave more robust prediction than the others. The best prediction result (RMSEC=222.4 mu g g(-1), R-2 for Cal=0.739, SEP=213.3 mu g g(-1); RPD=2.04 and r=0.877) was obtained from the SVMR-FS model developed for chlorophyll content. Indirect estimation models, based on relationships between leaf-based spectral measurements and leaf and stalk protein content, were less satisfactory.Öğe Vegetation indices as indicators of damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat(Academic Journals, 2008) Genc, H.; Genc, L.; Turhan, H.; Smith, S. E.; Nation, J. L.The sunn pest, Eurygaster integriceps Put. ( Hemiptera: Scutelleridae), also known as sting or cereal pest, is one of the most economically important pests of wheat in the world. In this study, a collapsible nylon cloth cage experiments were conducted to determine the feasibility of using remote sensing techniques to detect stress in wheat caused by the density of sunn pests. The results show we can detect the amount of stress in wheat caused by different life stages of sunn pest with a hand-held radiometer. Normalized difference vegetation index (NDVI) based indices; NDVIsg, NDVId, NDVIr, and structure insensitive pigment index ( SIPI) were chosen out of 19 indices initially tested. The NDVI based vegetation indices derived from hyperspectral data, recorded by a hand held spectroradiometer, were used to determine the predicted indices using the initial number of Sunn Pest (NOSP). Overall, r(2) values of all predicted indices calculated for 3(rd) instars were lower than those of 4(th) and adult stage. When r(2) was considered separately, predicted NDVIr index value (87.4) was the highest and predicted SIPI index value is lowest (80.7) in 3(rd) instars. The highest r(2) value was obtained in adult stage of sunn pest is NDVIsg (96.9) compare with NDVId (95.5), NDVIr (92.4) and SIPI (94.2). It was also concluded that remote sensing could detect not only the different stages pest damage on wheat, but also the number of sunn pest stages density affect in controlled experiments.
