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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德 | zh_TW |
| dc.contributor.advisor | Ta-Te Lin | en |
| dc.contributor.author | 程柏勳 | zh_TW |
| dc.contributor.author | Po-Hsun Cheng | en |
| dc.date.accessioned | 2025-08-19T16:19:43Z | - |
| dc.date.available | 2025-08-20 | - |
| dc.date.copyright | 2025-08-19 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-11 | - |
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Bee swarm activity acoustic classification for an IoT-based farm service. Sensors, 20(1), 21. https://doi.org/10.3390/s20010021 Ziegler, C., Ueda, R. M., Sinigaglia, T., Kreimeier, F., & Souza, A. M. (2022). Correlation of Climatic Factors with the Weight of an Apis mellifera Beehive. Sustainability, 14(9). | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98820 | - |
| dc.description.abstract | 本研究開發一套基於智慧蜂箱的蜂群健康監測系統，利用多感測器長期蒐集蜂箱內外部環境資料，結合時間序列預測與異常分類模型，實現蜂群重量、進出流量及花粉採集率的精確預測與健康狀態異常偵測。資料來源為 2024 年 9 月至 2025 年 4 月於台灣雲林兩處場域蒐集的感測數據與蜂群檢查表，蜂農每週填寫健康評估表單以標註蜂群狀態，作為機器學習模型的訓練與驗證依據。模型部分採用 GRU 與 SARIMAX 進行單變量與多變量時間序列預測。結果顯示多變量模型在三項目標變數的預測精度均優於單變量模型，其中 GRU 模型在跨蜂箱驗證下的誤差表現最佳，蜂群重量的平均絕對百分比誤差（MAPE）為 0.6%～2.1%，進出流量的 MAPE 為 19%～24.6%，花粉採集率的對稱平均絕對百分比誤差（sMAPE）為 16.7%～31.3%。透過 SHAP 分析進一步辨識影響預測結果的關鍵特徵，模型未出現預測停滯或漂移現象，具備長期穩定性。GRU 二元分類模型在蜂群健康狀態預測的整體準確率達 94%，能有效偵測失王、蜂巢壅擠等關鍵異常事件。系統能夠提供穩定且可靠的數據化蜂群動態洞察，支援早期健康問題識別與精準養蜂管理，提升蜂群存活率與管理效率。 | zh_TW |
| dc.description.abstract | This study presents a smart beehive-based health monitoring system that combines multi-sensor data collection, time-series forecasting, and anomaly detection to monitor honeybee colony health. Data were collected from September 2024 to April 2025 at two apiaries in Yunlin County, Taiwan. Sensor measurements and beekeeper-completed evaluation forms were used to label colony health status for training and validating machine learning models. Two forecasting models, GRU and SARIMAX, were developed to predict hive weight, bee traffic, and pollen collection rate under both univariate and multivariate settings. Experimental results showed that multivariate models consistently outperformed univariate ones, with the multivariate GRU achieving the most stable and generalizable performance. Specifically, during cross-hive validation, the forecasting errors were: a Mean Absolute Percentage Error (MAPE) of 0.6%–2.1% for weight prediction, 19%–24.6% MAPE for bee ingress–egress traffic, and a Symmetric Mean Absolute Percentage Error (sMAPE) of 16.7%–31.3% for pollen collection rate. SHAP-based interpretability analysis further identified the most influential features contributing to model accuracy, and the model exhibited no signs of prediction stagnation or shifting. Additionally, a GRU-based binary classifier for colony health status achieved an overall accuracy of 94%, effectively detecting critical anomalies such as queen loss and hive overcrowding. Overall, the proposed system reliably provides data-driven insights into colony dynamics, enabling early detection of health issues and supporting precision apiculture practices for improved hive management. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-19T16:19:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-19T16:19:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii Tables of Contents v List of Figures x List of Tables xii CHAPTER 1 Introduction 1 CHAPTER 2 Literature Review 6 2.1 Colony Ecology 6 2.1.1 Honeybee Social Structure and Worker Division of Labor 6 2.1.2 Microclimate and Temperature-Humidity Variations 8 2.1.3 Bee Physiology and Its Significance 10 2.1.4 Swarming and Absconding Behavior 12 2.1.5 Impact of Agricultural Diseases and Pests on Bees 14 2.2 Beehive Monitoring 15 2.2.1 Analysis of Weight and Temperature-Humidity Data 15 2.2.2 Analysis of In-Hive Acoustic Data 17 2.2.3 Analysis of Bee and Pollen Traffic 19 2.2.4 Effects of Climatic Factors on Bee Colonies 20 2.3 Studies on Bee Health Status Analysis 22 2.4 Machine Learning Approaches for Beehive Monitoring 24 2.4.1 Selection and Application of Machine Learning Algorithms 24 2.4.2 Colony State Prediction Using Time Series Models 25 CHAPTER 3 Methodology 29 3.1 Smart Multi-Sensor Beehive System 29 3.1.1 Hardware Configuration and Sensor Modules 30 3.1.2 Cloud-Based Data Management and Real-Time Monitoring 32 3.2 Data Collection 35 3.2.1 Experimental field 35 3.2.2 Hive Weight Data 37 3.2.3 Hive Temperature and Humidity 38 3.2.4 Bee and Pollen Traffic Data 39 3.2.5 Audio Data 40 3.2.6 Weather Data 40 3.2.7 Honeybee Colony Checklist 41 3.2.8 Dataset Summary 43 3.2.9 Target Variable Definition and Selection 44 3.3 Data Preprocessing 45 3.3.1 Missing Value Handling 46 3.3.2 Normalization and Outlier Handling 47 3.4 Feature Selection 48 3.4.1 Correlation Analysis 48 3.4.2 Variance Inflation Factor 50 3.5 Time Series Model 51 3.5.1 Gated Recurrent Unit 52 3.5.2 SARIMAX 55 3.6 Model Interpretation and Shifting Detection 58 3.6.1 SHAP-Based Feature Interpretation 58 3.6.2 Stagnation and Forecast Shifting Detection 60 CHAPTER 4 Results and Discussions 63 4.1 Feature Selection Results 63 4.2 Hive Weight Prediction Results 64 4.2.1 Univariate Hive Weight Prediction Results 65 4.2.2 Multivariate Hive Weight Prediction Results 67 4.2.3 Performance Evaluation of Long-Horizon Multi-Step Forecasting 69 4.2.4 Effects of Human Interference on Hive Weight 71 4.3 Bee Traffic Prediction Results 72 4.3.1 Univariate Bee Traffic Prediction Results 73 4.3.2 Multivariate Bee Traffic Prediction Results 75 4.4 Pollen Collection Rate Prediction Results 77 4.4.1 Univariate Pollen Collection Rate Prediction Results 78 4.4.2 Multivariate Pollen Collection Rate Prediction Results 80 4.5 Cross-Location Model Comparison and Generalization 82 4.5.1 Comparative Analysis of Univariate and Multivariate Models Across Two Hive Locations 83 4.5.2 Cross-Hive Evaluation of Multivariate GRU Models 86 4.6 SHAP Analysis and Forecast Shifting Detection 88 4.6.1 Feature Importance in Hive Weight Forecasting 88 4.6.2 Feature Importance in Bee Traffic Forecasting 90 4.6.3 Feature Importance in Pollen Collection Rate Forecasting 92 4.6.4 Forecast Stability Evaluation 94 4.7 Colony Health Status Prediction Results 96 CHAPTER 5 Conclusions 101 5.1 Conclusions 101 5.2 Suggestions 103 References 105 | - |
| dc.language.iso | en | - |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 時間序列 | zh_TW |
| dc.subject | 蜂箱健康預測 | zh_TW |
| dc.subject | 智慧農業 | zh_TW |
| dc.subject | time series | en |
| dc.subject | smart agriculture | en |
| dc.subject | beehive health prediction | en |
| dc.subject | machine learning | en |
| dc.title | 基於多元感測與機器學習之智慧蜂箱健康狀態預測系統 | zh_TW |
| dc.title | Smart Beehive Health Prediction System Based on Multi-Sensor Data and Machine Learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 陳世芳;楊恩誠 | zh_TW |
| dc.contributor.oralexamcommittee | Shih-Fang Chen;En-Cheng Yang | en |
| dc.subject.keyword | 機器學習,時間序列,蜂箱健康預測,智慧農業, | zh_TW |
| dc.subject.keyword | machine learning,time series,beehive health prediction,smart agriculture, | en |
| dc.relation.page | 112 | - |
| dc.identifier.doi | 10.6342/NTU202503964 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-13 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物機電工程學系 | - |
| dc.date.embargo-lift | 2025-08-20 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
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