From hive-level insight to smarter beekeeping decisions. We develop data-driven, innovative digital solutions for sustainable agriculture and apiculture.
Team Members
International Partners
Countries Involved
Founded at Işık University
INTALA LAB delivers data-driven research and innovative solutions for sustainable apiculture. Our expertise lies in advanced honey characterization, GIS-based land analysis, and machine learning applications for smart beekeeping.
Through optimization models and digital information systems, we support efficient migratory beekeeping, cost reduction, and ecosystem-friendly honey production, contributing to higher quality, traceability, and regional value recognition.
Origin recognition and PGI labelling
Satellite and drone imagery for land identification
Deep learning for beekeeping optimization
Semantic web and microservice architecture
International, data-driven research projects transforming the honey and beekeeping value chains across borders.
BEE-OPTECH4Honey aims to enhance honey production quality in Malta and Turkey by optimizing honeybee foraging sites and beehive transhumance routes using ICT and a GIS-based model. The project focuses on utilizing real-world data to identify ideal locations for nectar sites and optimal migratory routes, increasing the efficiency, productivity, and sustainability of beekeeping.
By characterizing monofloral honey and obtaining Protected Geographical Indication labels, the project improves honey quality and recognizes its origins. The integration of digital technologies into an information system enhances accessibility and utility of apiculture data, ultimately improving the efficiency and profitability of the industry.
Comprehensive analysis of monofloral honey from selected regions through melissopalynological studies for floral origin identification.
Utilizing satellite and drone imagery with deep learning to identify lands suitable for beekeeping, ensuring cost-effective production.
Developing a model to assign beekeepers to optimal nectar sites and establish efficient migratory routes, reducing operational costs.
Implementing a system that combines digital technologies, optimization models, and GIS-based image processing for apiculture.
For over 140 million years, bees have played a critical role in flowering plants. Although Argentina and Turkey are among the top three global honey producers, they have not been recognized for high-value end products due to honey fraud affecting the international market. TOP4HoneyChains generates a smart value chain driven by quality-based market preferences and consumer demands, building trust between buyers and sellers.
The platform develops an Open Data Platform employing state-of-the-art technologies including dynamic data collection and semantic integration services enabled by a scalable, robust microservice architecture. Apiaries can access quality test results of original honey and blends, while consumers access information about apiary practices and test results.
Transparent, traceable chains with records from apiary to consumer across four pilot cases in Argentina and Turkey.
Scalable microservice architecture with semantic integration for dynamic data collection and value-added services.
Novel ontology development and specification of data APIs for external data sources and data consumers.
Policy development to incentivize quality adoption for beekeepers, cooperatives, exporters, and consumers.
Comprehensive services spanning international project development, academic mentorship, and digital awareness.
Developing and managing agriculture and livestock projects on an international scale with experienced, cross-border teams producing innovative solutions.
Undergraduate and graduate students gain hands-on experience by participating directly in research projects and contributing to laboratory courses.
Accelerating digital transformation awareness in agriculture and apiculture through seminars, workshops, and training programs with industry stakeholders.
Satellite and drone imagery for beekeeping land identification
Machine learning models for honey characterization and optimization
Linked open data and microservice architectures
Transparent smart value chains from apiary to consumer
A multidisciplinary group of researchers and students advancing smart agriculture and apiculture.
Faculty member in the Department of Management Information Systems at Işık University since 2020. His research focuses on open data, AI, machine learning, and big data analytics applied to smart agricultural information systems through IoT, wireless sensor networks, crop-specific ontologies, semantic web, SOA, and microservices. Ph.D. from Kadir Has University.
Our latest contributions to the scientific community in smart beekeeping and digital agriculture.
Interested in collaborating, joining our team, or learning more about our research? We'd love to hear from you.
Meşrutiyet Mahallesi, Üniversite Sokak, No:2
34980 Şile / İstanbul, Turkey