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Honeybee Colony Growth Period Recognition Based on Multivariate Temperature Feature Extraction and Machine Learning

文献类型： 外文期刊

作者： Lu, Chuanqi 1 ; Li, Lin 1 ; Li, Denghua 2 ; Huang, Qiuying 3 ; Hong, Wei 4 ;

作者机构： 1.Huazhong Agr Univ, Coll Engn, Wuhan 430070, Peoples R China

2.Chinese Acad Agr Sci, Minist Agr & Rural Affairs, Key Lab Agr Monitoring & Early Warning Technol, Agr Informat Inst, Beijing 100081, Peoples R China

3.Huazhong Agr Univ, Coll Plant Sci & Technol, Wuhan 430070, Peoples R China

4.Huazhong Univ Sci & Technol, MOE Key Lab Fundamental Phys Quant Measurement, Wuhan 430074, Peoples R China

5.Huazhong Univ Sci & Technol, Hubei Key Lab Gravitat & Quantum Phys, PGMF, Wuhan 430074, Peoples R China

6.Huazhong Univ Sci & Technol, Sch Phys, Wuhan 430074, Peoples R China

关键词： temperature monitoring; time domain characteristic; machine learning; growth period recognition; honeybee colonies

期刊名称：SENSORS （ 影响因子：3.5； 五年影响因子：3.7 ）

ISSN：

年卷期： 2025 年 25 卷 13 期

页码：

收录情况： SCI

摘要： Identifying the growth period of bee colonies can guide beekeepers to make better decisions and promote the development of bee colonies. Unlike traditional manual experience-based recognition, this paper proposes a new approach, which combines multivariate temperature feature extraction and machine learning to intelligently recognize the growth period of bee colonies. Firstly, the year-round temperature data from 38 hives in Tai'an and Guilin was collected. Then, the 17 time domain characteristic indices were extracted from this dataset. To acquire the most sensitive features, the impact of different time scales on temperature feature extraction was analyzed. Subsequently, principal component analysis (PCA) was employed to reduce the dimensionality of the original feature vectors, thereby decreasing computational load and enhancing feature sensitivity. Finally, six machine learning algorithms, including both supervised and unsupervised learning, were utilized to identify the growth period of bee colonies. The results demonstrate that the proposed features can effectively characterize the growth period of bee colonies, and the BP method performs best in predicting growth period categories, with an MAE of only 1.45%. Moreover, the identification results of different regions also prove the practicability of the proposed method.

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