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Yayın Optimization of fertile land usage and agricultural production of Turkey(Işık Üniversitesi, 2017-01-18) Arslan, Şafak; Altunbay, Seyhun; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Endüstri Mühendisliği - Yöneylem Araştırması Yüksek Lisans ProgramıTurkey has a large amount of land which is suitable for agriculture. On the other hand, agricultural activities gradually decrease every year. Moreover, the fertile lands are not used effectively. As a result, production of agricultural goods does not satisfy the domestic demand. Thus, Turkey is obliged to import lots of agricultural goods in the recent decade.Turkey has to develop the agricultural potential and productivity and demand -supply stability as soon as possible. For this purpose, an optimization model has been developed to optimize the fertile land usage and agricultural production to satisfy the increasing agricultural demand of Turkey. Residential settlements on fertile lands are another problem about the agriculturalland usage of Turkey. The fertile lands are allowed for the construction of residential settlements and this causes the decrease of the fertile land area beside a lot of negative effects to the national economy and nature.This study aims to find solutions to these problems with the help of an optimization model. Specifically, which parts of land has to be farmed, which products has to be cultivated, how much has to be cultivated, how much area has to be used for the cultivation of a specific product. Additionally, the model will yield the effect of loss of fertile land due to residential settlements on fertile lands.Yayın Design and implementation of a smart beehive and its monitoring system using microservices in the context of IoT and open data(Elsevier B.V., 2022-05) Aydın, Şahin; Aydın, Mehmet NafizIt is essential to keep honey bees healthy for providing a sustainable ecological balance. One way of keeping honey bees healthy is to be able to monitor and control the general conditions in a beehive and also outside of a beehive. Monitoring systems offer an effective way of accessing, visualizing, sharing, and managing data that is gathered from performed agricultural and livestock activities for domain stakeholders. Such systems have recently been implemented based on wireless sensor networks (WSN) and IoT to monitor the activities of honey bees in beehives as well. Scholars have shown considerable interests in proposing IoT- and WSN-based beehive monitoring systems, but much of the research up to now lacks in proposing appropriate architecture for open data driven beehive monitoring systems. Developing a robust monitoring system based on a contemporary software architecture such as microservices can be of great help to be able to control the activities of honey bees and more importantly to be able to keep them healthy in beehives. This research sets out to design and implementation of a sustainable WSN-based beehive monitoring platform using a microservice architecture. We pointed out that by adopting microservices one can deal with long-standing problems with heterogeneity, interoperability, scalability, agility, reliability, maintainability issues, and in turn achieve sustainable WSN-based beehive monitoring systems.Yayın Microservices-based databank for Turkish hazelnut cultivars using IoT and semantic web technologies(John Wiley and Sons Ltd, 2024-03-30) Aydın, Şahin; Aldara, DieaaInformation and communication technologies (ICTs) can play a crucial role in facilitating access to comprehensive information on the quality standards of Turkish hazelnut cultivars. In this regard, this study introduces a Hazelnut Databank System (HDS) that utilizes the microservices architecture, an integrated software system supported by the Internet of Things (IoT) and semantic web, to categorize Turkish hazelnut cultivars. The study focuses on developing microservices using various programming languages and frameworks. Specifically, C# on the.NET Core Framework was used for both microservices and the web-based application implemented through the ASP.NET Core MVC Framework. Mobile-based software applications were created using Xamarin. Forms, and the IoT application was developed using the Python programming language. The data storage is facilitated through the MS SQL Server database. Additionally, the study incorporates the implementation of a hazelnut species classification system using the DNN + ResNet50 machine learning model, achieving an impressive accuracy rate of 95.77%. The overall usability of the system was evaluated, resulting in a score of 42 out of 50. By providing detailed information on Turkish hazelnut cultivars, the HDS has the potential to greatly improve hazelnut production quality in Turkey and increase awareness of hazelnut agriculture among relevant stakeholders.
