Incremental learning framework for queen bee detection from acoustic beehive signals
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Other Title
Authors
Wang, Jiqiang
Author ORCID Profiles (clickable)
Degree
Master of Applied Technologies (Computing)
Grantor
Unitec, Te Pūkenga – New Zealand Institute of Skills and Technology
Date
2025
Supervisors
Song, Lei
Ardekani, Iman
Ardekani, Iman
Type
Masters Thesis
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
queen bees
bees
honeybees (Apis mellifera)
beehives
pattern recognition
apiculture industry
incremental learning
feature extraction
support vector machines
AI in agriculture
bees
honeybees (Apis mellifera)
beehives
pattern recognition
apiculture industry
incremental learning
feature extraction
support vector machines
AI in agriculture
ANZSRC Field of Research Code (2020)
Citation
Wang, j. (2025). Incremental learning framework for queen bee detection from acoustic beehive signals (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Applied Technologies (Computing)). Unitec, Te Pūkenga - New Zealand Institute of Skills and Technology
https://hdl.handle.net/10652/6955
Abstract
RESEARCH QUESTIONS
1. Compared to conventional methods, how does LDA-based feature extraction identify the most important features in a queen bee detection system?
2. In what ways can QR be applied to LDA-transformed features to improve computational efficiency and support scalable real-time queen bee detection?
3. What are the main drawbacks of traditional batch learning methods, and how can incremental learning improve the adaptability of queen bee detection?
4. How does the proposed LDA, QR, and incremental learning scheme perform in terms of memory usage, classification accuracy, and model update speed compared to other machine learning pipelines?
5. Does the reduction of feature dimensionality through LDA feature selection and QR help prevent overfitting and thus improve generalisation in dynamic queen bee detection scenarios?
ABSTRACT
Monitoring queen bee status is the foundation of modern beekeeping. Traditional methods are often disruptive, time-consuming, and unsuitable for the computational demands of processing large-scale acoustic data. Due to these challenges, this research proposed an innovative incremental learning framework that combined:
Linear Discriminant Analysis (LDA) for discriminative feature selection, QR Decomposition for efficient and numerically stable model updates, and Support Vector Machine (SVM) for robust classification. The system processed continuous hive audio streams using the incremental QR-based LDA method, enabling the feature space and classifier to adapt to new data without complete model retraining. The model demonstrated exceptional performance and efficiency when evaluated on three distinct and diverse public datasets (NU-Hive, SBCM, and UrBAN). It consistently maintained high classification accuracy while reducing model update time. A key finding of this study is that the framework exhibits significant advantages in mitigating overfitting. The inherent information abstraction capability during the incremental QR update process enables it to generalize to unseen test data better. Finally, this study validates a scalable, efficient, and accurate real-time audio-based queen bee detection solution. The solution balanced classification performance and dynamic acoustic environment requirements, thereby representing a breakthrough in precision beekeeping technology.
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