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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.Yayın İnsansız hava aracı ve Sentinel-2 görüntüleri kullanılarak ayçiçeği haritalamasına dayalı kovan yerleştirme karar destek sistemi(BZT Turan Publishing House, 2025-12-31) Yelmenoğlu, Elif Deniz; Aydın, Şahin; Çavdaroğlu, Gülsüm Çiğdem; Deniz, Hüseyin; Pajenado, Rex S.; Dilli, ŞirinAyçiçeği, yüksek nektar üretim kapasitesi nedeniyle gezici arıcılık faaliyetleri açısından stratejik öneme sahip tarımsal bitkilerden biridir. Ayçiçeği ekim alanlarının mekânsal ve zamansal dağılımı, arı kolonilerinin beslenme olanaklarını ve dolayısıyla bal verimini doğrudan etkilemektedir. Bu nedenle, arı kovanlarının uygun alanlara ve doğru zaman dilimlerinde yerleştirilmesi, gezici arıcılığın verimliliği açısından kritik bir karar sürecini oluşturmaktadır. Ancak mevcut uygulamalarda, kovan yer seçimi çoğunlukla arıcıların bireysel deneyimlerine ve sezgisel yaklaşımlarına dayalı olarak gerçekleştirilmekte; uzaktan algılama, görüntü işleme ve mekânsal analiz gibi veri temelli yöntemlerden yeterince yararlanılmamaktadır. Bu durum, potansiyel olarak verim kayıplarına ve kaynakların etkin kullanılmamasına yol açabilmektedir. Bu çalışmada, ayçiçeği yoğunluğunun doğru ve güvenilir biçimde belirlenmesi yoluyla kovan yerleştirme planlamasını desteklemeyi amaçlayan, çok ölçekli bir uzaktan algılama tabanlı karar destek çerçevesi önerilmektedir. Önerilen yaklaşım, saha ölçeğinde yüksek mekânsal çözünürlük sağlayan insansız hava aracı (İHA) görüntüleri ile bölgesel ölçekte geniş alan kapsama imkânı sunan Sentinel-2 uydu görüntülerinin entegrasyonuna dayanmaktadır. Çalışma alanı olarak, Türkiye’nin önemli ayçiçeği üretim merkezlerinden biri olan Kırklareli ili seçilmiş; veri seti, nektar üretiminin en yüksek olduğu ayçiçeği çiçeklenme dönemi dikkate alınarak oluşturulmuştur. Ayçiçeği tespiti, makine öğrenmesi tabanlı Random Forest sınıflandırma yöntemi kullanılarak gerçekleştirilmiş ve geliştirilen model %90,7 genel doğruluk değerine ulaşmıştır. Sınıf bazlı performans değerlendirmelerinde ise, ayçiçeği ekili alanlar ile ayçiçeği olmayan alanlar için F1-skoru her iki sınıf açısından da 0,91 olarak hesaplanmıştır. Bu sonuçlar, modelin hem nektar açısından zengin ayçiçeği alanlarını hem de ayçiçeği bulunmayan bölgeleri güçlü ve dengeli bir şekilde ayırt edebildiğini göstermektedir. Elde edilen ayçiçeği yoğunluk haritaları temel alınarak, ayçiçeği oranının yüksek olduğu alanlar arı kovanı yerleştirilmesi için uygun bölgeler olarak tanımlanmış; ayçiçeği yoğunluğunun düşük olduğu veya hiç bulunmadığı alanlar ise kovan yerleştirilmesine uygun olmayan bölgeler olarak değerlendirilmiştir. Çalışmadan elde edilen bulgular, çok ölçekli uzaktan algılama verilerinin makine öğrenmesi yöntemleriyle bütünleştirilmesinin, gezici arıcılık uygulamalarında veri temelli, güvenilir ve ölçeklenebilir karar destek sistemlerinin geliştirilmesine önemli katkılar sağlayabileceğini ortaya koymaktadır.Yayın Yeni nesil yönetim bilişim sistemlerinde veri, sistem ve analitik bütünleşmesi(Serüven Yayınevi, 2025-12) Aydın, Şahin; Aydın, Mehmet Nafiz[No abstract available]Yayın An ontology for apiculture practices (Onto4API): towards semantic interoperability and knowledge sharing in the apiculture community(Ege Tarımsal Araştırma Enstitüsü Müdürlüğü, 2025-12-31) Aydın, Şahin; Okuyan, Samet; Solmaz, SerhatThis study presents the development of Onto4API, a domain ontology designed to support semantic interoperability and structured knowledge sharing in the field of apiculture. The ontology addresses the lack of standardized, machine-interpretable vocabularies that hinder knowledge integration and decision support in traditional beekeeping practices. Developed under the guidance of subject-matter experts from the Türkiye Apiculture Research Institute, Onto4API formalizes key concepts, relationships, and production practices in modern beekeeping. The ontology was built using OWL 2 and RDF/XML syntax, and includes 67 classes, six object properties, and 10 data properties. Following the METHONTOLOGY framework, our approach ensures methodological rigor from specification to implementation and evaluation, combining expert validation, reasoning-based consistency checks, and SPARQL-based functional testing. To demonstrate its practical utility, a web-based educational tool was implemented using ASP.NET MVC and dotNetRDF. This prototype enables users to explore apiculture knowledge through SPARQL-based queries in a guided question-and-answer format. By providing a reusable and extensible semantic framework, Onto4API lays the groundwork for future ontology-driven agricultural systems, including intelligent decision support, educational tools, and interoperable data services in apiculture and beyond.
