Application of Machine Learning and Artificial Intelligence in Precision Beekeeping
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".
Deadline for manuscript submissions: 25 March 2025 | Viewed by 49
Special Issue Editors
Interests: image and sound processing; action recognition; convolutional neural networks; edge devices; field-programmable gate arrays; beekeeping
Interests: neural networks; electronics engineering; machine learning; signal processing; tracking; embedded systems; beekeeping
Special Issues, Collections and Topics in MDPI journals
Interests: real-time image and signal processing; development of intelligent systems; application of intelligent systems; beekeeping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of machine learning (ML) and artificial intelligence (AI) into precision beekeeping represents a significant advancement in apiculture, addressing the challenges posed by declining bee populations and increasing environmental stressors. Historically, beekeeping has relied on traditional methods, but the emergence of digital technologies has paved the way for more efficient and effective hive management practices. Precision beekeeping utilizes IoT devices and data analytics to monitor individual colonies, enhancing decision-making processes for beekeepers.
This Special Issue aims to explore cutting-edge research that highlights the application of ML and AI in precision beekeeping. We invite contributions that examine innovative technologies for monitoring bee health, optimizing hive conditions, and improving overall productivity. Topics may include real-time data collection through sensors, predictive modeling for disease detection, and automated systems for hive management.
We are soliciting a diverse range of papers, including original research articles, reviews, and case studies. We encourage interdisciplinary studies that incorporate insights from agricultural sciences, computer science, environmental studies, and engineering. Contributions that assess the societal impacts of precision beekeeping technologies or explore the sustainability of these practices are particularly welcome.
By fostering collaboration among researchers and practitioners, this Special Issue aims to advance the field of precision beekeeping through innovative applications of machine learning and artificial intelligence, ultimately supporting sustainable agricultural practices and enhancing bee health.
Dr. Tomyslav Sledevič
Prof. Dr. Darius Plonis
Prof. Dr. Artūras Serackis
Guest Editors
Manuscript Submission Information
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Keywords
- precision beekeeping
- machine learning
- artificial intelligence
- hive monitoring
- data analytics
- bee health management
- predictive modeling
- automated systems
- sustainable agriculture
- pollination efficiency
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