摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据《中国人民日报》记者报道，“准确估算农田生态系统中CO(NEE)的净交换量对于了解农业生产方式和气候条件的影响至关重要，但由于景观异质性和模型升级效率的差异，区域农田生态系统中CO(NEE)的净交换量仍然存在很大的不确定性。”我们的新闻记者从中山大学的研究中得到一句话：“这里，”本文将基于对象的图像分析(OBIA)技术与先进的机器学习(ML)方法相结合,应用于UPSC ALE区域农田NEE,对具有不同气候条件的四个不同农田区域的PROACH进行了深入的评估,证实了OBIA技术能够有效地分割农田目标,并对其进行了分析。在ML模型集成的3种方法中,序贯最小二乘规划算法HM在预测NEE方面表现出了异常的性能,各研究区域的R值均超过0.80,最成功区域的R值达到0.90.与基于像素的ML模型相比，放大的区域产品在表征作物土地NEE模式方面表现出更好的性能，此外，我们利用SHapley加性解释(SHAP)来评估驱动因素的重要性，揭示物候和辐射对预测精度的影响最大。受温度和土壤湿度的影响。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Guangdong, People’s Re public of China, by NewsRx correspondents, research stated, “Accurately estimati ng the net ecosystem exchange of CO (NEE) in cropland ecosystems is essential fo r understanding the impacts of agricultural practices and climate conditions. Ho wever, significant uncertainties persist in the estimation of regional cropland NEE due to landscape heterogeneity and variations in the efficacy of upscaling m odels.” Our news journalists obtained a quote from the research from Sun Yat-sen Univers ity, “Here, we applied an integrated approach that combined object-based image a nalysis (OBIA) techniques with advanced machine learning (ML) approaches to upsc ale regional cropland NEE. We conducted a thorough evaluation of the upscaling a pproach across four distinct cropland areas characterized by diverse climate con ditions. Our study confirmed that OBIA techniques can efficiently segment cropla nd objects, thereby enhancing the representation and accuracy of characteristics relevant to cropland features. The sequential least squares programming algorit hm, among the three methods used for ML model integration, demonstrated exceptio nal performance in predicting NEE, with an R value exceeding 0.80 across all stu dy areas and peaking at 0.90 in the most successful area. On average, there was an 18 % improvement compared to the poorestperforming ML model an d a 6 % enhancement compared to the best-performing ML model. The upscaled regional products exhibited superior performance in characterizing crop land NEE patterns compared to pixel-based products. Additionally, we utilized th e SHapley Additive exPlanations (SHAP) to assess driver importance, revealing th at phenology and radiation had the greatest influence on prediction accuracy, fo llowed by temperature and soil moisture.”
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