发表机构
Texas A&M AgriLife High Plains Research and Extension Center; Texas A&M University（德克萨斯农工大学农业生命高平原研究与推广中心; 德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用对比面板特征和机器学习（XGBoost）估算牛饲养场高浓度PM10，在250-1000微克/立方米范围内实现R²为0.792，验证了图像方法在扩展浓度下的可行性。
AI 中文摘要
对于牛饲养场行业而言，经济实惠的粉尘监测仍然是一项迫切需求，然而基于摄像头的PM估算，尽管在城市空气质量领域的研究日益增多，尚未在集约化畜牧作业特有的扩展浓度范围内得到评估。本研究开发了一种基于图像的方法，利用对比面板特征和机器学习来估算商业牛饲养场中的PM10浓度，该饲养场的小时平均PM10范围为250至1,000微克/立方米，瞬时浓度达到5,000至20,000微克/立方米。在傍晚粉尘高峰时段捕获灰度图像，并提取了包括面板对比度、黑白面板像素值以及整体图像亮度在内的特征。该模型还纳入了来自先前图像的近期历史值以及太阳天顶角作为预测变量。在评估的候选模型中，XGBoost取得了最高的预测性能，其R²为0.792，中位绝对误差为103微克/立方米。特征重要性分析显示：(a) 距离相机最远的面板对预测的贡献最大；(b) 黑色面板像素值对PM10浓度变化的敏感度高于白色面板像素值。在日落过渡期间（恰逢饲养场傍晚粉尘高峰开始）的预测准确性仍有待进一步改进。这些发现证明了基于图像的PM10估算在远超先前城市研究报告的PM浓度范围内的可行性，并为未来在饲养场环境中的部署提供了实用指南。
英文摘要
Affordable dust monitoring remains a pressing need for the cattle feedlot industry, yet camera-based PM estimation, despite its growing body of research in urban air quality settings, has not been evaluated under the extended concentration ranges characteristic of intensive livestock operations. This study developed an image-based approach using contrast panel features and machine learning to estimate PM10 concentrations in a commercial cattle feedlot, where hourly average PM10 ranged from 250 to 1,000 ug/m^-3 and instantaneous concentrations reached 5,000 to 20,000 ug/m^-3. Grayscale images were captured during the evening dust peak period, and features including panel contrast, black and white panel pixel values, and overall image brightness were extracted. The model also incorporated recent past values from preceding images and solar zenith angle as predictors. Among the candidate models evaluated, XGBoost achieved the highest predictive performance, with an R^2 of 0.792 and a median absolute error of 103 ug/m^-3. Feature importance analysis revealed that (a) panels positioned farthest from the camera contributed most strongly to predictions and (b) that black panel pixel values were more sensitive than white panel values to changes in PM10 concentration. Prediction accuracy during the sunset transition, which coincides with the onset of the feedlot evening dust peak, remains an area for further refinement. These findings demonstrate the feasibility of image-based PM10 estimation across PM concentration ranges substantially exceeding those reported in prior urban studies and provide practical guidelines for future deployment in feedlot environments.
