Determinination of efects of sunn pest on wheat grain by artificial neural networks

Buğday Türkiye için olduğu kadar dünyadaki pek çok ülke için de stratejik bir üründür ve süne zararlısı ise buğday üretiminde temel bir sıkıntıdır. Süne zararlısı, buğdayı bitkisel büyüme, baş verme ve olgunluk dönemlerinde negatif olarak etkiler. Bu etki, buğday danesi üzerinde verim kaybı ve kalitede düşüş olmak üzere iki çeşit hasar meydana getirir. Bu kalite düşüşü de insan beslenmesinde temel gıda maddesi olan buğdaydan üretilen pek çok üründe üretim kayıplarına sebep olmak-tadır. Bu durumu ortadan kaldırabilmek için buğday daneleri işlenmeden önce süne hasarlı olanların hasarlı olmayanlardan ayrılması gerekmektedir. Bu ise Türkiye'de uzmanlar tarafından gerçekleştirilmektedir. Ancak bu hasar kimi zaman çok be-lirgin ve gözle anlaşılabiliyorken kimi zaman anlaşılamayacak şekilde olabilir. Bu durumda hasarlı buğday danelerini hasar-sızlar arasından gözle tespit edebilmek mümkün olmayabilir. Sunulan çalışmada buğday danesi üzerindeki süne zararlısının oluşturduğu hasarı tespit etmek amacıyla Yapay Sinir Ağlarına (YSA) dayalı otomatik bir görüntü tanıma sistemi sunulmak-tadır.Wheat is a very strategic crop for Turkey as well as many other countries and sunn pest is a major constraint to the production of wheat. Sunn pest negatively affects wheat crops during their vegetative growth, heading and maturity stages. This effect causes two types of damage on wheat grain by leading to wheat yield loss and grain quality decrease. The decrease in the quality leads in turn to production losses in many products which depends on wheat. Wheat crops therefore should be examined before the production processes in order to separate the sunn pest affected ones from non-affected ones. Such a discrimination task in Turkey is performed by experts. However, the damage can sometimes be visible but also sometimes it migth be hard to notice the damage. So, the damaged grains may not be distinguished among undamaged ones with simple eye observation. In this study, an automatic system which uses Artificial Neural Networks (ANN) to determine the wheat grains damaged by sunn pest is propose

The effect of different seed densities on some hybrid maize (zea mays l.) type’s yield in Eskişehir conditions

Çalışmada Orta Anadolu Bölgesi’nin Batı Geçit kuşağını temsil eden Eskişehir’de bazı ticari mısır çeşitlerinin farklı tohum sıklıklarında yetiştirildikleri zaman tarımsal özelliklere olan etkileri incelenmiştir. Mısır çeşitleri olarak ADA_9510, ADA_9516, TTM_815 ve BC_6661 kullanılırken yetiştirilme sıklığı olarak da “70×20”, “70×15”, “60×20”, “60×25” ve “50×30”cm değerleri uygulanmıştır. Eskişehir koşullarında gerçekleştirilen ve tesadüf blokları bölünmüş parseller deseninde ve dört tekerrürlü olarak kurulan denemede mısırlar, damla sulama yöntemi ile sulanmış, gübreleme ise fergitasyon şeklinde uygulanmıştır. Bu çalışmada, seçilen çeşide ve sıklığa bağlı olarak verim ve verim öğelerinin farklı olabildiği görülmüştür. Bir yıllık bu çalışmada da “60×25” cm ile “50×30”cm tohum sıklığı öne çıkmıştır.In this study, the effects of different plant spacing of some commercial maize varieties on agricultural features were investigated in Eskişehir, forming the western transitional zone of Central Anatolia. ADA_9510, ADA_9516, TTM_815 and BC_6661 were used as the varieties while the plants were applied as “70×20”, “70×15”, “60×20”, “60×25” and “50×30”cm. In trials performed in Eskişehir conditions, plantation was performed according to split plots in Randomized Complete Blocks with 4 replications, irrigation was performed with microirrigation and fertilizer application was performed as fertilization. The results showed that yield and yield factors could vary according to the maize variety and plantation density. In the present oneyear study, “60×25”cm and “50×30”cm density appeared to become the prominent spacing value

Correlation Analyses of Herbage Yield and Quality Components in Certain Sorghum × Sudangrass (Sorghum bicolor L.×Sorghumsudanense Staph.) Hybrid Cultivars

The purpose of this research study was to evaluate phenotypic correlation between yield, quality and certain yield components, and to determine the direct and indirect effects of 13 different components on yield and quality in sorghum×sudangrass hybrids. The research was conducted in the trial area of the Bilecik Seyh Edebali University Faculty of Agriculture and Natural Sciences in Bilecik, Turkey, in the 2015 crop year. The randomized complete block design with 4 replications was used. In the study, Aneto and Teide sorghum×sudangrass hybrid varieties belonging to Fito Seed Company and Gözde 80, Leoti, Nes, Rox and Early Sumac sorghum×sudangrass hybrid varieties belonging to Mediterranean Agricultural Research Institute were used as the materials. Relationships between ADF (Acid Detergent Fiber) and NDF (Neutral Detergent Fiber) ratios, RFV (Relative Feed Value) and ME (Metabolic Energy) values and characters were investigated in the study, in addition to plant height, panicle height, leaf ratio, stem ratio, panicle ratio, green grass yield, hay yield and crude protein yields of sorghum×sudangrass hybrid varieties. Results show that the Teide variety showed the highest performance in terms of herbage yield, crude protein ratio, ADF, NDF, RFV and ME, while the lowest yields were obtained from Rox and Early Sumac. Crude protein ratio was found to significantly correlate with leaf ratio, ADF, NDF, RFV and ME similarly, leaf ratio correlated with ADF and NDF; ADF with NDF; RFV with leaf ratio, ADF and NDF; and ME with leaf ratio, ADF, NDF and RFV