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Agriculture
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Application of Machine Learning and Artificial Intelligence in Precision Beekeeping
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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 September 2025 | Viewed by 1393

Special Issue Editors

Dr. Tomyslav Sledevič

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Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), Plytinės g. 25, LT-10105 Vilnius, Lithuania
Interests: image and sound processing; action recognition; convolutional neural networks; edge devices; field-programmable gate arrays; beekeeping
Prof. Dr. Darius Plonis

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Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), Plytinės g. 25, LT-10105 Vilnius, Lithuania
Interests: neural networks; electronics engineering; machine learning; signal processing; tracking; embedded systems; beekeeping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Artūras Serackis

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Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University (VILNIUS TECH), Plytinės g. 25, LT-10105 Vilnius, Lithuania
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 15144 KiB  
Article
Evaluation of Deep Learning Models for Insects Detection at the Hive Entrance for a Bee Behavior Recognition System
by Gabriela Vdoviak, Tomyslav Sledevič, Artūras Serackis, Darius Plonis, Dalius Matuzevičius and Vytautas Abromavičius
Agriculture 2025, 15(10), 1019; https://doi.org/10.3390/agriculture15101019 - 8 May 2025
Viewed by 313
Abstract
Monitoring insect activity at hive entrances is essential for advancing precision beekeeping practices by enabling non-invasive, real-time assessment of the colony’s health and early detection of potential threats. This study evaluates deep learning models for detecting worker bees, pollen-bearing bees, drones, and wasps, [...] Read more.
Monitoring insect activity at hive entrances is essential for advancing precision beekeeping practices by enabling non-invasive, real-time assessment of the colony’s health and early detection of potential threats. This study evaluates deep learning models for detecting worker bees, pollen-bearing bees, drones, and wasps, comparing different YOLO-based architectures optimized for real-time inference on an RTX 4080 Super and Jetson AGX Orin. A new publicly available dataset with diverse environmental conditions was used for training and validation. Performance comparisons showed that modified YOLOv8 models achieved a better precision–speed trade-off relative to other YOLO-based architectures, enabling efficient deployment on embedded platforms. Results indicate that model modifications enhance detection accuracy while reducing inference time, particularly for small object classes such as pollen. The study explores the impact of different annotation strategies on classification performance and tracking consistency. The findings demonstrate the feasibility of deploying AI-powered hive monitoring systems on embedded platforms, with potential applications in precision beekeeping and pollination surveillance. Full article
(This article belongs to the Special Issue Application of Machine Learning and Artificial Intelligence in Precision Beekeeping)
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25 pages, 10241 KiB  
Article
Machine Learning-Based Acoustic Analysis of Stingless Bee (Heterotrigona itama) Alarm Signals During Intruder Events
by Ashan Milinda Bandara Ratnayake, Hartini Mohd Yasin, Abdul Ghani Naim, Rahayu Sukmaria Sukri, Norhayati Ahmad, Nurul Hazlina Zaini, Soon Boon Yu, Mohammad Amiruddin Ruslan and Pg Emeroylariffion Abas
Agriculture 2025, 15(6), 591; https://doi.org/10.3390/agriculture15060591 - 11 Mar 2025
Viewed by 586
Abstract
Heterotrigona itama, a widely reared stingless bee species, produces highly valued honey. These bees naturally secure their colonies within logs, accessed via a single entrance tube, but remain vulnerable to intruders and predators. Guard bees play a critical role in colony defense, [...] Read more.
Heterotrigona itama, a widely reared stingless bee species, produces highly valued honey. These bees naturally secure their colonies within logs, accessed via a single entrance tube, but remain vulnerable to intruders and predators. Guard bees play a critical role in colony defense, exhibiting the ability to discriminate between nestmates and non-nestmates and employing strategies such as pheromone release, buzzing, hissing, and vibrations to alert and recruit hive mates during intrusions. This study investigated the acoustic signals produced by H. itama guard bees during intrusions to determine their potential for intrusion detection. Using a Jetson Nano equipped with a microphone and camera, guard bee sounds were recorded and labeled. After preprocessing the sound data, Mel Frequency Cepstral Coefficients (MFCCs) were extracted as features, and various dimensionality reduction techniques were explored. Among them, Linear Discriminant Analysis (LDA) demonstrated the best performance in improving class separability. The reduced feature set was used to train both Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. KNN outperformed SVM, achieving a Precision of 0.9527, a Recall of 0.9586, and an F1 Score of 0.9556. Additionally, KNN attained an Overall Cross-Validation Accuracy of 95.54% (±0.67%), demonstrating its superior classification performance. These findings confirm that H. itama produces distinct alarm sounds during intrusions, which can be effectively classified using machine learning; thus, demonstrating the feasibility of sound-based intrusion detection as a cost-effective alternative to image-based approaches. Future research should explore real-world implementation under varying environmental conditions and extend the study to other stingless bee species. Full article
(This article belongs to the Special Issue Application of Machine Learning and Artificial Intelligence in Precision Beekeeping)
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