12 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 10 / 12
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 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.Yayın IoT-based surveillance system for poultry farms using semantic web and deep learning(Plusbase Akademi Publishing, 2023-04-30) Aydın, Şahin; Jones, KarlPoultry diseases are among the most important problems encountered in poultry farming. Although detecting diseases before they infect all poultry seems to be a fundamental challenge, it is possible to detect poultry diseases in the early stages with an Internet of Things (IoT)-based surveillance system. IoT-based surveillance systems create an important opportunity to prevent the spread of diseases throughout the poultry house and to prevent the breeders from incurring financial losses. The internetbased surveillance system proposed within the scope of this study determines the presence of poultry and poultry species with the help of artificial intelligence (AI) and aims to prevent the spread of the disease to the entire poultry house by detecting diseased poultry with the data obtained from temperature sensors. The system will detect the presence of animals and the body temperature data of poultry in two different ways. The first is to detect the presence of the animal in the nests where the laying action is carried out using a weight sensor and to obtain the body temperature data of the poultry with a temperature sensor. Secondly, by using image processing techniques, it is to detect poultry roaming in the poultry house with a deep learning model and obtains body temperature data of poultry through infrared temperature sensors. The system will decide on the possible type of disease using ontologies related to poultry diseases according to the obtained body temperatures. As a result, this proposed system will enable early disease detection for poultry farms by using the perspectives of deep learning, semantic web, and ontology engineering disciplines, which are among the important fields of study in recent years.Yayın Spatial distribution of Türkiye’s livestock products economy (1995–2020): sustainability-oriented visualization analysis(Liberty Publishing House, 2025-10-25) Aydın, Şahin; Özkan, Oktay; Azgın, Şükrü TanerIntroduction and Purpose: Livestock production plays a strategic role in Türkiye’s agricultural economy and is directly linked to food security and sustainable development goals. The aim of this study is to examine the spatial distribution of the livestock products economy in Türkiye between 1995 and 2020, visualize regional differences, and reveal long-term trends. Materials and Methods: The study utilizes province-level annual livestock product values (in thousand TL) obtained from official statistical sources. The data were analyzed through spatial methods, including choropleth maps, trend analysis, and growth rate evaluations for selected crisis years (2001, 2008, 2018, 2020). The analyses were conducted from a sustainability perspective, and regional production centers were identified. Findings: The results show that the Marmara, Aegean, and Central Anatolia regions lead in livestock product values, while the Eastern and Southeastern Anatolia regions have recorded significant increases in recent years. Trend analysis indicates that Konya, İzmir, Erzurum, and Diyarbakır achieved the largest growth, whereas smaller provinces exhibited relatively limited increases. In terms of crisis years, the sector continued to grow except during the 2008 global financial crisis, with a notable increase observed during the 2020 pandemic. Discussion and Conclusion: Overall, Türkiye’s livestock products economy demonstrated a steady increase between 1995 and 2020. The findings suggest that while the sector is sensitive to global shocks, it remains relatively resilient to domestic crises and pandemic conditions. Spatial analyses highlight the necessity of considering regional disparities in the development of sustainable policies.Yayın Regional analysis and forecasting of broiler and layer poultry production in Türkiye: a statistical and machine learning approach(Liberty Publishing House, 2025-10-20) Aydın, Şahin; Gül, Osman KubilayIntroduction and Purpose: As well as cattle farming and sheep & goat farming, poultry farming also has a significant place in Türkiye’s agricultural economy. There are two important branches, such as broiler and egg in this sector. There is not enough systematic research which examines the regional perspectives and provide future projections in poultry farming as in many areas of agriculture and livestock. The main purpose of this study is to analyze broiler and layer production in Türkiye, identify the main producing regions, and generate forecasts using both traditional statistical models and modern machine learning algorithms. Materials and Methods: The regional broiler and layer production datasets have been acquired from the web-based data platform of Turkish Statistical Institute (TÜİK). Top producer regions and long-term changes in broiler and layer chicken production have been identified using descriptive statistics. Two statistical techniques- Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES)- have been used to anticipate the total national production of broiler and egg chicken. Two machine learning models such as Random Forest and Gradient Boosting, nevertheless, have been created. Random Forest allows for assessing variable importance and capturing nonlinearities, and Gradient Boosting provides flexible parameterization (e.g., learning rate, tree depth) and can be tuned effectively to the dataset. The model performance has been evaluated by way of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². The projections for ten years have been generated. Results: The broiler chicken production has been largely concentrated on the north-west line. The top three producer regions are TR42 (Kocaeli–Sakarya–Düzce–Bolu–Yalova), TR33 (Manisa–Afyonkarahisar–Kütahya–Uşak), and TR22 (Balıkesir–Çanakkale) respectively. The models ES and ML envisioned moderate growth in broiler chicken production, on the other hand, the suggestion of ARIMA is a flatter trend. The top three producer regions in layer chicken production are TR33 (Manisa, Afyonkarahisar, Kütahya, Uşak), TR52 (Konya–Karaman), and TR83 (Samsun–Tokat–Çorum–Amasya) respectively. A slight decline from the recent peak has been indicated by ES. On the other hand, moderate growth has been referred to by ARIMA. ML models harmonized the differences between statistical models by drawing a more balanced growth path. Discussion and Conclusion: This research shows the importance of using both statistical and machine learning approaches together with the purpose of identifying the trend dynamics and nonlinear relationships in broiler and layer chicken production. The results reveal that north-western regions are leading in the broiler chicken production. On the other hand, western-central regions are dominating the layer chicken production. The results of this study can be utilized to create critical policy deductions and decisions of targeted investments by considering these distinct geographies. The proposed methodological framework can be adapted to other livestock production data as well.Yayın Exploring two decades of change in Turkish apiculture through spatiotemporal data analysis(Siirt Üniversitesi Ziraat Fakültesi, 2025) Aydın, ŞahinThis study examines the apiculture sector in Türkiye between 2004 and 2024 using data from the Turkish Statistical Institute, focusing on temporal, spatial, and relational dimensions. Time-series analyses, spatial visualizations, productivity comparisons, and correlation assessments were applied to reveal the structural transformation of the sector. The findings indicate a steady increase in modern hive numbers alongside a gradual decline in traditional hives. While overall honey production has grown, per-hive productivity has not improved significantly, suggesting that modernization alone is insufficient. Spatial analyses revealed that provinces such as Ordu, Muğla, and Adana remain dominant in production, yet substantial regional inequalities persist. Comparative and relational analyses highlighted a strong positive relationship between modern hive adoption and honey output, whereas traditional hives contributed little. The study concludes that Turkish apiculture is undergoing a modernization-driven transformation of hive structures and production practices, but efficiency stagnation and regional disparities necessitate complementary policies and practices to ensure sustainable development.
