2025-09-26
【學術亮點】針對荷蘭牛非妊娠期及分娩後階段體重預測的應用
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Intelligent Animal Husbandry: Development of intelligent and sustainable animal farming models【Department of Animal Science / Hsin-I Chiang/ Associate Professor】
智慧農牧場：應用行為指標建構智慧化永續生產飼養模式【動物科學系江信毅副教授】
上架日期：2025/9/11
智慧農牧場：應用行為指標建構智慧化永續生產飼養模式【動物科學系江信毅副教授】
| 論文篇名 | 英文：Application of weight prediction for Holstein dairy cows in non-pregnant and postpartum stages 中文：針對荷蘭牛非妊娠期及分娩後階段體重預測的應用 |
| 期刊名稱 | Biosystems Engineering |
| 發表年份, 卷數, 起迄頁數 | 2024, 259, 104276 |
| 作者 | Hsin-I Chiang(江信毅), Jia-Ming Zhou, Wen-Lin Chu(朱玟霖)* |
| DOI | 10.1016/j.biosystemseng.2025.104276 |
| 中文摘要 | 本研究開發了一套基於深度感測技術的非接觸式荷蘭乳牛體重預測系統，用於預測荷蘭乳牛在非妊娠期及分娩後階段的體重變化。該系統利用Intel RealSense D455 深度相機擷取牛隻背部、臀部及側面的深度影像資訊，經系統性的資料處理流程提取有效體表特徵資料。實驗結果顯示，高斯過程迴歸（Gaussian Process Regression，GPR）模型於乳牛背部區域表現最為卓越，以牛隻編號csn603為例，在非妊娠期表現出均方根誤差（RMSE）為19.37 kg、平均絕對誤差百分比（MAPE）為1.82%的預測精度；以牛隻編號csn700為例，分娩後階段亦維持均方根誤差（RMSE）為22.35 kg、平均絕對誤差百分比（MAPE）為2.74%，展現穩健的模型泛化能力。相較於傳統牧場基於體長與胸圍測量的估算方法，本研究提出的體重預測系統顯著提高了體重預測的準確性與穩定性，特別是在捕捉生理狀態變化（如分娩後體重下降）上具有優勢。實驗結果顯示，GPR模型在背部區域的特徵資料上展現出最佳的預測能力與泛化性，可有效支持乳牛體重的精確監測，並指出未來研究方向應著重於影像前處理技術的優化與融入更多生理參數（如飲食攝入量），並整合不同視角之深度資訊，提升系統在複雜環境下的適應能力，以強化體重預測模型之普適性與可靠性。 |
| 英文摘要 | A non-contact weight prediction system for Holstein dairy cows was developed based on depth sensing technology, designed to predict weight changes during non-pregnant and postpartum stages. The system utilises an Intel RealSense D455 depth camera to capture depth image information from cow's dorsal, hips, and side regions, extracting effective body surface feature data through a systematic data processing workflow. Experimental results demonstrate that the Gaussian Process Regression (GPR) model performed most excellently in the cow's dorsal region. For example, with cow number cid603 during the non-pregnant period, prediction accuracy reached a root mean square error (RMSE) of 19.37 kg and a mean absolute percentage error (MAPE) of 1.82 %; with cow number cid700 in the postpartum stage, the model maintained an RMSE of 22.35 kg and MAPE of 2.74 %, exhibiting robust model generalisation capability. Compared to traditional farm methods based on body length and heart girth measurements, the weight prediction system proposed in this study significantly improved the accuracy and stability of weight prediction, especially in capturing physiological state changes (such as postpartum weight loss). Experimental results indicate that the GPR model exhibited the best predictive ability and generalisation with feature data from the dorsal region, effectively supporting precise monitoring of dairy cow weight. Future research directions should focus on optimising image preprocessing techniques, incorporating more physiological parameters (such as feed intake), and integrating depth information from different angles to enhance the system's adaptability in complex environments, thereby strengthening the universality and reliability of the weight prediction model. |
| 發表成果與AI計畫研究主題相關性 | 基於AI技術用於預測荷蘭乳牛在非妊娠期及分娩後階段的體重變化，由深度相機擷取牛隻背部、臀部及側面的深度影像資訊，GPR模型在背部區域的特徵資料上展現出最佳的預測能力與泛化性，可有效支持乳牛體重的精確監測。 |
