Hidden Markov Models for corn progress percents estimation in multivariate time series
Hidden Markov Models for corn progress percents estimation in multivariate time series
复制标题
多元时间序列中玉米进度百分比估计的隐马尔可夫模型
DOI：
10.1109/agro-geoinformatics.2012.6311726
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Guoxian Yu
中科院分区：
文献类型：
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作者：
Yonglin Shen;L. Di;Lixin Wu;Genong Yu;Hong Tang;Guoxian Yu
作物发育信息对美国农业经济和决策至关重要。本文提出了一种基于隐马尔可夫模型的玉米生长动态估计方法的一般框架。多元时间序列，包括平均NDVI，分形维数，和累计生长度日（AGDDs）嵌入到修改后的HMM。平均NDVI和分维特征来自MODIS（ModerateResolutionImagingSpectroradiometer）NDVI（NormalizedDifferenceVegetationIndex）时间序列，AGDDs来自AWDN。在我们的模型中，阶段转移概率和观测概率直接从支持的数据中确定。阶段转移概率由AGDD校正。每个阶段的三个连续特征的概率密度函数采用多元高斯模型。值得一提的是，不仅检测特定时间片处的进展阶段，而且可以同时估计相应进展阶段的比例。在爱荷华州进行了为期十年（2002年至2011年）的实验研究，并通过NASS（国家农业统计局）的CPR（作物进展报告）进行了评估和验证。研究结果表明，所提出的解决方案在国家级玉米生产进度估算中是可行的。
Crop development information is critical to U.S. agricultural economy and decision making. In this paper, a general framework of Hidden Markov Models (HMMs) based corn progress percents esitmation method has been presented. Multivariate time series involving mean NDVI, fractal dimension, and Accumulated Growing Degree Days (AGDDs) are embedded into the modified HMM. Features of mean NDVI and fractal dimension are derived from MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) time series, and AGDDs is from Automated Weather Data Network (AWDN). In our model, stage transition probabilities and observation probabilities are determined directly from supported data. Stage transition probabilities are corrected by AGDDs. Probability density function associated with three continuous features of each stage is modeled by multivariate Gaussian. It is worth mentioning that not only progress stages at a specific time slice is detected, but the proportion of corresponding progress stage can be estimated, simultaneously. Experimental studies have been conducted on state of Iowa, over a decade period (2002 through 2011) with assessment and validation by NASS's (National Agricultural Statistics Service) CPRs (Crop Progress Reports). The results demonstrate the feasibility of proposed solutions on corn progress percents estimation in the state-level.