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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.Yayın Comparison of choreography vs orchestration based Saga patterns in microservices(Institute of Electrical and Electronics Engineers Inc., 2022) Aydın, Şahin; Çebi, Cem BerkeMicroservice Architecture (MSA) is a design and architecture pattern created to deal with the challenges of conventional software programs in terms of stream processing, highly available flexibility, and infrastructural agility. Despite the many advantages of MSA, designing isolated services using the autonomous Databases per Services paradigm is difficult. We realized that because each microservice will have its repository, ensuring data coherence between databases becomes difficult, especially in reversals, where operations transcend different sites. Distributed networked transactions and rollbacks can be efficiently handled using two-phase commitment methods in hardware virtualization using RDBMS databases. However, these approaches can't be used in micro-services with segregated NoSQL servers. Three issues have been addressed in this study: (i) investigate the implementation of event choreography and orchestration methods for the Saga pattern execution in MSA, (ii) existing reality suggestions on the saga pattern adoption and implementation besides the use cases, and (iii) introduce the disbursed transaction records and rollbacks challenges in isolated No-SQL databases with reliant collections in MSA.Yayın Critical digital data enabling traceability for smart honey value chains(Taylor and Francis Ltd., 2025-02) Ziemba, Ewa Wanda; Maruszewska, Ewa Wanda; Karmanska, Anna; Aydın, Mehmet Nafiz; Aydın, ŞahinData analysis and sharing are becoming increasingly important in creating value within food supply chains, including honey value chains. While some data is readily shared between supply chain actors, unlocking further benefits requires additional investments in digital data capturing, particularly for value-based claims such as sustainability, equity, and traceability from hives to customers. This study aims to identify critical digital data necessary for smart honey value chains to ensure traceability and transparency while fostering trust among beekeepers, intermediaries, and consumers. Semi-structured interviews with 30 beekeeping experts were conducted to explore their perspectives. The analysis identified four critical categories of data—beekeeper data, apiary data, honey data, and apiary practices data—encompassing 21 specific data points essential for ensuring transparency, traceability, and trust. These findings provide novel insights into the digital data requirements necessary to support the honey industry’s evolving needs for transparent and traceable value chains.Yayın Designing a scalable agricultural information system for pest detection and decision support in hazelnut cultivation(World Scientific Publishing Company, 2025-11-12) Aydın, ŞahinThis study presents a microservices-based, multi-tiered information system to detect, monitör and manage pest species that cause yield losses in hazelnut production. The system integrates a deep learning model for classifying pest images submitted by field users, the generation of pest density maps and location-based early warning mechanisms for growers. Delivered through mobile, web and desktop platforms, the system enables data sharing among farmers, researchers and decision-makers, supporting agricultural decisions. Experimental findings show that the DNN+ResNet50 architecture achieved the highest accuracy (91.88%) among all tested CNN models. Performance evaluations indicated that the Authentication and Heatmap services sustained high stability under loads of up to 1000 requests, while the Bug Classification Service was reliable up to 750 requests before reaching a critical resource threshold. The usability test resulted in an overall score of 38 out of 50, with sub-scores of Appropriateness Recognizability (0.73, Acceptable), Learnability (0.71, Acceptable), Operability (0.65, Questionable), User Error Protection (0.86, Good), User Interface Aesthetics (0.83, Good) and Accessibility (0.74, Acceptable). With its robust technical architecture and practical implementation, the proposed system can generate economic, social and commercial outcomes. This study provides a software engineering-oriented approach to the digitalization of agricultural production and the sustainable management of pests.
