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In evaluating agricultural products, knowing the specific product varieties is important for the producer, the industrialist, and the consumer. Human labor is widely used in the classification of varieties. It is generally performed by visual examination of each sample by experts, which is very laborious and time-consuming with poor sensitivity. There is a need in commercial hazelnut production for a rapid, non-destructive and reliable variety classification in order to obtain quality nuts from the orchard to the consumer. In this study, a convolutional neural network, which is one of the deep learning methods, was preferred due to its success in computer vision. A total of 17 widely grown hazelnut varieties were classified. The proposed model was evaluated by comparing with pre-trained models. Accuracy, precision, recall, and F1-Score evaluation metrics were used to determine the performance of classifiers. It was found that the proposed model showed a better performance than pre-trained models in terms of performance evaluation criteria. The proposed model was found to produce 98.63% accuracy in the test set, including 510 images. This result has shown that the proposed model can be used practically in the classification of hazelnut varieties.
Alper Taner; Yeşim Öztekin; Hüseyin Duran. Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut. Sustainability 2021, 13, 6527 .
AMA StyleAlper Taner, Yeşim Öztekin, Hüseyin Duran. Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut. Sustainability. 2021; 13 (12):6527.
Chicago/Turabian StyleAlper Taner; Yeşim Öztekin; Hüseyin Duran. 2021. "Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut." Sustainability 13, no. 12: 6527.
For sustainable agriculture, efficient use of energy is of utmost importance. This study was conducted for a period of three years in Central Anatolia region of Turkey to find out how the energy balance was affected under different planting methods. Split plot design with four replications was used in the study. While the main plots included planting methods, the subplots included the varieties. Energy use efficiency, energy productivity, specific energy and net energy were investigated. According to the results obtained, energy inputs were found to be lower in bed planting (26.38 GJ ha−1), when compared with conventional flat planting (26.51 GJ ha−1). Energy use efficiency was found to be higher in conventional flat planting (7.15) when compared with bed planting (5.39). While grain yield (GY), biomass yield (BY) and number of spikes m2 (NS) were found to be higher with conventional flat planting, harvest index (HI) and thousand kernel weight (TKW) were found to be higher with bed planting. It was concluded that the variety chosen was extremely important in the success of bed planting method.
Alper Taner; Rifat Zafer Arısoy; Yasin Kaya; Irfan Gültekin; Fevzi Partigöç. Comparison of energy of planting methods in wheat production in a semi-arid region. Archives of Agronomy and Soil Science 2020, 1 -13.
AMA StyleAlper Taner, Rifat Zafer Arısoy, Yasin Kaya, Irfan Gültekin, Fevzi Partigöç. Comparison of energy of planting methods in wheat production in a semi-arid region. Archives of Agronomy and Soil Science. 2020; ():1-13.
Chicago/Turabian StyleAlper Taner; Rifat Zafer Arısoy; Yasin Kaya; Irfan Gültekin; Fevzi Partigöç. 2020. "Comparison of energy of planting methods in wheat production in a semi-arid region." Archives of Agronomy and Soil Science , no. : 1-13.
The present study investigated the possible use of artificial neural networks (ANN) to classify five chestnut (Castanea sativa Mill.) varieties. For chestnut classification, back-propagation neural networks were framed on the basis of physical and mechanical parameters. Seven physical and mechanical characteristics (geometric mean diameter, sphericity, volume of nut, surface area, shell thickness, shearing force and strength) of chestnut were determined. It was found that these characteristics were statistically different and could be used in the classification of species. In the developed ANN model, the design of the network is 7-(5-6)-1 and it consists of 7 input, 2 hidden and 1 output layers. Tansig transfer functions were used in both hidden layers, while linear transfer functions were used in the output layer. In ANN model, R2 value was obtained as 0.99999 and RMSE value was obtained as 0.000083 for training. For testing, R2 value was found as 0.99999 and RMSE value was found as 0.00031. In the approximation of values obtained with ANN model to the values measured, average error was found as 0.011%. It was found that the results found with ANN model were very compatible with the measured data. It was found that the ANN model obtained can classify chestnut varieties in a fast and reliable way.
Yeşim Benal ÖZTEKİN; Alper Taner; Hüseyin Duran. Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 2020, 48, 366 -377.
AMA StyleYeşim Benal ÖZTEKİN, Alper Taner, Hüseyin Duran. Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 2020; 48 (1):366-377.
Chicago/Turabian StyleYeşim Benal ÖZTEKİN; Alper Taner; Hüseyin Duran. 2020. "Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach." Notulae Botanicae Horti Agrobotanici Cluj-Napoca 48, no. 1: 366-377.
A 2-(5-8)-2 artificial neural network (ANN) model, with a back propagation learning algorithm, was developed to predict specific draft force and fuel consumption requirements of mouldboard plough in a clay loam soil under varying operating conditions. The input parameters of the network were tillage depth and forward speed of operation. The output from the network were the specific draft force and fuel consumption requirement of the mouldboard plough. The developed model predicted the specific draft force and fuel consumption requirement of mouldboard plough with an error <1 % when compared to the measured draft force and fuel consumption values. Such encouraging results indicate that the developed ANN model for specific draft force and fuel consumption requirement prediction could be considered as an alternative and practical tool for predicting draft force and fuel consumption requirement of tillage implements under the selected experimental conditions in clay loam soils. Further work is required to demonstrate the generalised value of this ANN in other soil conditions.
Kazım Çarman; Ergün Çıtıl; Alper Taner. Artificial Neural Network Model for Predicting Specific Draft Force and Fuel Consumption Requirement of a Mouldboard Plough. Selcuk Journal of Agricultural and Food Sciences 2019, 33, 241 -247.
AMA StyleKazım Çarman, Ergün Çıtıl, Alper Taner. Artificial Neural Network Model for Predicting Specific Draft Force and Fuel Consumption Requirement of a Mouldboard Plough. Selcuk Journal of Agricultural and Food Sciences. 2019; 33 (3):241-247.
Chicago/Turabian StyleKazım Çarman; Ergün Çıtıl; Alper Taner. 2019. "Artificial Neural Network Model for Predicting Specific Draft Force and Fuel Consumption Requirement of a Mouldboard Plough." Selcuk Journal of Agricultural and Food Sciences 33, no. 3: 241-247.
Tillage is one of the most important agronomical practices especially for plant height (PH), grain yield (GY) and yield components in wheat production. This study was carried out in 2007 – 08 and 2008 – 09 growing seasons in Kahramanmaras, Turkey, to investigate response of five wheat cultivars (Adana, Ceyhan, Dogankent, Menemen and Yuregir) to conventional (CT) and reduced tillage (RT) systems after cotton harvest for PH, number of fertile spikes per m 2 (SM), spike length (SL), number of fertile spikelets spike – 1 (SS), number of grains spike – 1 (GS), 1000 – kernel weight (KW) and GY components. The soil was ploughed at a depth of 25–30 cm in CT system, while it was not used in RT. The results indicated that all traits had greater values in 2008 – 09 than in 2007 – 08 except KW and GY. The tillage systems significantly affected PH, SM, SL, SS, GS and GY except KW. Over the two years, values of all traits in CT were higher than those of RT. There was a significant and positive correlation for GY, SS, GS and KW between CT and RT. The cultivars were affected by year and tillage system. Dogankent cultivar had better performance and stable for most of the traits compared to others under CT and RT in both years.
Mustafa Yildirim; Ziya Dumlupinar; Alper Taner. Pamuk-Buğday sistemlerinde buğdayın bazı özellikleri üzerine geleneksel ve azaltılmış toprak işlemenin etkileri. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi 2018, 21, 678 -685.
AMA StyleMustafa Yildirim, Ziya Dumlupinar, Alper Taner. Pamuk-Buğday sistemlerinde buğdayın bazı özellikleri üzerine geleneksel ve azaltılmış toprak işlemenin etkileri. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi. 2018; 21 (5):678-685.
Chicago/Turabian StyleMustafa Yildirim; Ziya Dumlupinar; Alper Taner. 2018. "Pamuk-Buğday sistemlerinde buğdayın bazı özellikleri üzerine geleneksel ve azaltılmış toprak işlemenin etkileri." Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi 21, no. 5: 678-685.
In this study, an Artificial Neural Network (ANN) model was developed in order to classify varieties belonging to grain species. Varieties of bread wheat, durum wheat, barley, oat and triticale were utilized. 11 physical properties of grains were determined for these varieties as follows: thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters. It was found that these properties had been statistically significant for the varieties. An Artificial Neural Network was developed for classifying varieties. The structure of the ANN model developed was designed to have 11 inputs, 2 hidden and 2 output layers. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour were used as input parameters; and species and varieties as output parameters. While classifying the varieties by the ANN model developed, R2, RMSE and mean error were found to be 0.99, 0.000624 and 0.009%, respectively. In classifying the species, these values were found to be 0.99, 0.000184 and 0.001%, respectively. It has shown that all the results obtained from the ANN model had been in accordance with the real data.
Alper Taner; Yeşim Benal Öztekin; Ali Tekgüler; Hüseyin Sauk; Hüseyin Duran. Classification of Varieties of Grain Species by Artificial Neural Networks. Agronomy 2018, 8, 123 .
AMA StyleAlper Taner, Yeşim Benal Öztekin, Ali Tekgüler, Hüseyin Sauk, Hüseyin Duran. Classification of Varieties of Grain Species by Artificial Neural Networks. Agronomy. 2018; 8 (7):123.
Chicago/Turabian StyleAlper Taner; Yeşim Benal Öztekin; Ali Tekgüler; Hüseyin Sauk; Hüseyin Duran. 2018. "Classification of Varieties of Grain Species by Artificial Neural Networks." Agronomy 8, no. 7: 123.
Orta Anadolu Bölgesi kuru koşullarında tarımsal üretimin çoğunluğunu geleneksel toprak işleme ile yürütülen buğday üretimi oluşturmaktadır. Buğday veriminde istikrarın sağlanması ve aktırılması için, nadas uygulaması önerilmekte ve tercihte görmektedir. Ancak yoğun toprak işlemeye dayanan nadas üretim maliyetlerini artırmakta ve topraklarda olumsuz etkilere neden olmaktadır. Buna karşın dünyada doğal kaynakların korunup geliştirilmesi ve getirdiği ekonomik avantajlar nedeniyle doğrudan ekim (DE) giderek yaygınlaşmaktadır. Bölge koşullarında yürütülen farklı çalışmalara göre DE uygulanabilir bir yöntem olduğu belirlenmiştir. DE’de toprakların işlenmemesi, toprak yüzeyinin mümkün olduğunca bitki veya anızla kaplı tutulması yanında, ekim nöbeti de önerilmektedir. Değişik ekim nöbeti etkilerinin belirlenmesi için 2011-2014 yılları arasında Konya’da geleneksel ekim (GE) ve DE yöntemlerinde nadas, nohut, mercimek, macar fiği ve aspir sonrasında buğday verimlerine bakılmıştır. Elde edilen sonuçlara göre, buğday verimlerinin yıllara, ekim yöntemlerine ve ön bitkilere bağlı olarak değişkenlik gösterdiği tespit edilmiştir. Çalışmanın ilk yılında 219 kg/da olarak belirlenen verim daha sonraki yıllarda sırasıyla 244 ve 237 kg/da olarak gerçekleşmiştir. Ortalama olarak GE’de 226 kg/da DE’de 241 kg/da, buğday verimi alınmıştır. Nadas sonrasında buğday verimleri 286 kg/da olarak belirlenirken, 249, 228, 223 ve 182 kg/da olarak mercimek, nohut, macar fiği ve aspir sonrasında elde edilmiştir. Uygulama yöntemlerinin her ikisinde de en düşük verimler aspir sonrasında en yüksek verimler nadas uygulamasından alınmıştır. DE yönteminden elde edilen verimleri üzerinde uygulanan yetiştirme tekniği işlemlerinin de bariz etkileri gözlenmiştir. Özellikle, ekim zamanının ve yabancı ot yoğunluğunun etkisi belirgün olmuştur. Sonuç olarak DE farklı bir üretim yöntemi olduğu kendi içerinde gerekli uygulamaların yapılması durumunda geleneksel yöntemden daha iyi sonuçlar verdiği görülmektedir.
Irfan Gültekin; Fevzi Partigöç; Serpil Gültekin; Yasin Kaya; Rifat Zafer Arısoy; Alper Taner. Orta Anadolu Bölgesi Kuru Koşullarında Buğday Tabanlı Üretimde Doğrudan Ekim. Kahramanmaraş Sütçü İmam Üniversitesi Doğa Bilimleri Dergisi 2017, 20, 283 -286.
AMA StyleIrfan Gültekin, Fevzi Partigöç, Serpil Gültekin, Yasin Kaya, Rifat Zafer Arısoy, Alper Taner. Orta Anadolu Bölgesi Kuru Koşullarında Buğday Tabanlı Üretimde Doğrudan Ekim. Kahramanmaraş Sütçü İmam Üniversitesi Doğa Bilimleri Dergisi. 2017; 20 ():283-286.
Chicago/Turabian StyleIrfan Gültekin; Fevzi Partigöç; Serpil Gültekin; Yasin Kaya; Rifat Zafer Arısoy; Alper Taner. 2017. "Orta Anadolu Bölgesi Kuru Koşullarında Buğday Tabanlı Üretimde Doğrudan Ekim." Kahramanmaraş Sütçü İmam Üniversitesi Doğa Bilimleri Dergisi 20, no. : 283-286.
Kazım Çarman; Tamer Marakoğlu; Alper Taner; Fariz Mikailsoy. Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel Vėjo erozijos matavimai ir modeliavimas įvairiose žemės dirbimo sistemose, naudojant vėjo erozijos tunelį. Zemdirbyste-Agriculture 2016, 103, 327 -334.
AMA StyleKazım Çarman, Tamer Marakoğlu, Alper Taner, Fariz Mikailsoy. Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel Vėjo erozijos matavimai ir modeliavimas įvairiose žemės dirbimo sistemose, naudojant vėjo erozijos tunelį. Zemdirbyste-Agriculture. 2016; 103 (3):327-334.
Chicago/Turabian StyleKazım Çarman; Tamer Marakoğlu; Alper Taner; Fariz Mikailsoy. 2016. "Measurements and modelling of wind erosion rate in different tillage practices using a portable wind erosion tunnel Vėjo erozijos matavimai ir modeliavimas įvairiose žemės dirbimo sistemose, naudojant vėjo erozijos tunelį." Zemdirbyste-Agriculture 103, no. 3: 327-334.
This study investigates the prediction of kinematic viscosity values of hazelnut oil methyl ester (HOME) using empirical equations and artificial neural network (ANN) methods under varying temperature and blend ratio conditions with ultimate euro diesel (UED) fuel. Four different fuel blends (20, 40, 60, and 80% by volume mixing ratio) were studied along with UED fuel and pure biodiesel. Tests for kinematic viscosity were performed in the temperature range of 293.15–373.15 K at the intervals of 1 K for each fuel sample. Moreover, physicochemical properties of hazelnut crude oil (HCO), HOME and its blends, and also fatty acid composition of HCO and HOME were measured and discussed in light of ASTM and EN standards. Regression analyses were conducted using MATLAB software to determine the coefficient of determination (R2), root mean square error (RMSE), and correlation constants. The best R2 and RMSE values obtained were obtained by Eq. 6 as 0.9999 and 0.0068, respectively. In the analyses conducted using ANN, R2, and RMSE were obtained as 0.999986 and 0.00149 respectively based on the overall HOME–UED fuel blends. Although R2 values obtained by these two methods were close to each other, RMSE obtained using ANN was smaller than that of the one obtained by Eq. 6. In conclusion, the ANN method captures the best accuracy for the prediction of biodiesel kinematic viscosity with the highest R2 of 0.999986 and the lowest RMSE of 0.00149, which is within ±1% error range of the experimental data. © 2016 American Institute of Chemical Engineers Environ Prog, 2016
Tanzer Eryilmaz; Mevlut Arslan; Murat Kadir Yesilyurt; Alper Taner. Comparison of empirical equations and artificial neural network results in terms of kinematic viscosity prediction of fuels based on hazelnut oil methyl ester. Environmental Progress & Sustainable Energy 2016, 35, 1827 -1841.
AMA StyleTanzer Eryilmaz, Mevlut Arslan, Murat Kadir Yesilyurt, Alper Taner. Comparison of empirical equations and artificial neural network results in terms of kinematic viscosity prediction of fuels based on hazelnut oil methyl ester. Environmental Progress & Sustainable Energy. 2016; 35 (6):1827-1841.
Chicago/Turabian StyleTanzer Eryilmaz; Mevlut Arslan; Murat Kadir Yesilyurt; Alper Taner. 2016. "Comparison of empirical equations and artificial neural network results in terms of kinematic viscosity prediction of fuels based on hazelnut oil methyl ester." Environmental Progress & Sustainable Energy 35, no. 6: 1827-1841.
Alper Taner; Yasin Kaya; Rifat Zafer Aisoy; Irfan Gültekin; Fevzi Partigöç. Effect of Tillage Systems on Energy Use Efficiency in Wheat Based Cropping Sequence. International Journal of Agriculture and Biology 2016, 18, 353 -361.
AMA StyleAlper Taner, Yasin Kaya, Rifat Zafer Aisoy, Irfan Gültekin, Fevzi Partigöç. Effect of Tillage Systems on Energy Use Efficiency in Wheat Based Cropping Sequence. International Journal of Agriculture and Biology. 2016; 18 (2):353-361.
Chicago/Turabian StyleAlper Taner; Yasin Kaya; Rifat Zafer Aisoy; Irfan Gültekin; Fevzi Partigöç. 2016. "Effect of Tillage Systems on Energy Use Efficiency in Wheat Based Cropping Sequence." International Journal of Agriculture and Biology 18, no. 2: 353-361.
In the present study, the seeds named as wild mustard (Sinapis arvensis L.) and safflower (Carthamus tinctorius L.) were used as feedstocks for production of biodiesels. In order to obtain wild mustard seed oil (WMO) and safflower seed oil (SO), screw press apparatus was used. wild mustard seed oil biodiesel (WMOB) and safflower seed oil biodiesel (SOB) were produced using methanol and NaOH by transesterification process. Various properties of these biodiesels such as density (883.62–886.35 \({{\rm kg\,\rm m}^{-3}}\)), specific gravity (0.88442–0.88709), kinematic viscosity (5.75–4.11 \({{\rm mm}^{2}\,{\rm s}^{-1}}\)), calorific value (40.63–38.97 \({{\rm MJ\,\rm kg}^{-1}}\)), flash point (171–\({175\,^{\circ}{\rm C}}\)), water content (328.19–412.15 \({{\rm mg\,\rm kg}^{-1}}\)), color (2.0–1.8), cloud point [5.8–\({(-4.7)\,^{\circ}{\rm C}]}\), pour point [(–3.1)–(–13.1)\({\,^{\circ}{\rm C})}\), cold filter plugging point [(−2.0)–\({(-9.0)\,^{\circ}{\rm C})}\)], copper strip corrosion (1a–1a) and pH (7.831–7.037) were determined. Furthermore, kinematic viscosities of biodiesels and euro-diesel (ED) were measured at 298.15–373.15 K intervals with 1 K increments. Four different equations were used to predict the viscosities of fuels. Regression analyses were done in MATLAB program, and \({R^{2}}\), correlation constants and root-mean-square error were determined. 1–7–7–3 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the viscosities of fuels. The performance of neural network-based model was compared with the performance of viscosity prediction models using same observed data. It was found that ANN model consistently gave better predictions (0.9999 \({R^{2}}\) values for all fuels) compared to these models. ANN model was showed 0.34 % maximum errors. Based on the results of this study, ANNs appear to be a promising technique for predicting viscosities of biodiesels.
Tanzer Eryilmaz; Murat Kadir Yesilyurt; Alper Taner; Sadiye Ayse Celik. Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks. Arabian Journal for Science and Engineering 2015, 40, 3745 -3758.
AMA StyleTanzer Eryilmaz, Murat Kadir Yesilyurt, Alper Taner, Sadiye Ayse Celik. Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks. Arabian Journal for Science and Engineering. 2015; 40 (12):3745-3758.
Chicago/Turabian StyleTanzer Eryilmaz; Murat Kadir Yesilyurt; Alper Taner; Sadiye Ayse Celik. 2015. "Prediction of Kinematic Viscosities of Biodiesels Derived from Edible and Non-edible Vegetable Oils by Using Artificial Neural Networks." Arabian Journal for Science and Engineering 40, no. 12: 3745-3758.
It is of great importance to precisely and carefully apply the minimum amount of chemicals as needed because agricultural chemicals negatively impact the human health, environment and balance in the nature and increase the production costs. In this study, it was aimed at determining the density of broad leaf weeds and contributing to the reduction of herbicide use in wheat grown fields. For this purpose, Image Processing Techniques were used in this study; and Artificial Neural Networks (ANN) and regression models were developed for determination of weeds. In the ANN model, Weed Covered Areas Acquired by Image Processing Techniques (WCAAIPT) was used as input parameter; and Actual Weed Covered Areas (AWCA) as output parameter. In the study, a total of 262 data consisting of 244 data for training and 18 data for test were used. In the ANN model, the structure of the network was designed in the form of 1-(9-5)-1, consisting of 1 input layer, 2 hidden layers and 1 output layer; and the number of neurons in the hidden layer were determined to be 9-5. Also, tansig was used in the first hidden layer, logsig in the second hidden layer; and purelin transfer functions were used in the output layer. In the ANN and Regression models, R2 value of the ANN model was found to be 99% and the goodness of fit (U2) to be 0.000436; whereas R2 and U2 values of the Regression model were found to be 95% and 0.008431, respectively. It was determined that the results obtained from the ANN model were in agreement with the experimental data. By the developed ANN model, it would be possible to design and manufacture agricultural machinery in order to determine the weed density and reduce the herbicide use.
Onur Ağın; Alper Taner. Determination of weed intensity in wheat production using image processing techniques. ANADOLU JOURNAL OF AGRICULTURAL SCIENCES 2015, 30, 110-117 .
AMA StyleOnur Ağın, Alper Taner. Determination of weed intensity in wheat production using image processing techniques. ANADOLU JOURNAL OF AGRICULTURAL SCIENCES. 2015; 30 (2):110-117.
Chicago/Turabian StyleOnur Ağın; Alper Taner. 2015. "Determination of weed intensity in wheat production using image processing techniques." ANADOLU JOURNAL OF AGRICULTURAL SCIENCES 30, no. 2: 110-117.
In this study, an Artificial Neural Network (ANN) was developed in order to classify durum wheat varieties. For this purpose, physical properties of durum wheat varieties were determined and ANN techniques were used. The physical properties of 11 durum wheat varieties grown in our country, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and color parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, M-l, M-ll and M-lll were constructed. The performances of these models were compared. It was determined that the best fit model was M-1. In the M-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and color parameters of grain were used as input parameter; and varieties as output parameter. R2, RMSE and mean error for the M-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the M-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in Commodity (Grain) Exchange and flour mills.
Alper Taner; Ali Tekgüler; Hüseyin Sauk. Classification of durum wheat varieties by artificial neural networks. ANADOLU JOURNAL OF AGRICULTURAL SCIENCES 2015, 30, 51-59 .
AMA StyleAlper Taner, Ali Tekgüler, Hüseyin Sauk. Classification of durum wheat varieties by artificial neural networks. ANADOLU JOURNAL OF AGRICULTURAL SCIENCES. 2015; 30 (1):51-59.
Chicago/Turabian StyleAlper Taner; Ali Tekgüler; Hüseyin Sauk. 2015. "Classification of durum wheat varieties by artificial neural networks." ANADOLU JOURNAL OF AGRICULTURAL SCIENCES 30, no. 1: 51-59.
The purpose of this study was to investigate the relationship between travel reduction and tractive performance and to illustrate how artificial neural networks (ANNs) could play an important role in the prediction of these parameters. The experimental values were taken in a soil bin. A 1-4-6-2 artificial neural network (ANN) model with a back propagation learning algorithm was developed to predict the tractive performance of a driven tire in a clay loam soil under varying operating and soil conditions. The input parameter of the network was travel reduction. The output parameters of the network were net traction ratio and tractive efficiency. The relationships were investigated using non-linear regression analysis and ANNs. The performance of the neural network-based model was compared with the performance of a non linear regression-based model using the same observed data. It was found that the ANN model consistently gave better predictions compared to the non linear regression-based model. Based on the results of this study, ANNs appear to be a promising technique for predicting tire tractive performance.
Kazım Çarman; Alper Taner. Prediction of Tire Tractive Performance by Using Artificial Neural Networks. Mathematical and Computational Applications 2012, 17, 182 -192.
AMA StyleKazım Çarman, Alper Taner. Prediction of Tire Tractive Performance by Using Artificial Neural Networks. Mathematical and Computational Applications. 2012; 17 (3):182-192.
Chicago/Turabian StyleKazım Çarman; Alper Taner. 2012. "Prediction of Tire Tractive Performance by Using Artificial Neural Networks." Mathematical and Computational Applications 17, no. 3: 182-192.