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基于改进型模糊聚类算法的植物病斑检测 被引量:6
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作者 冯登超 杨兆选 乔晓军 《计算机工程与应用》 CSCD 北大核心 2007年第24期203-204,245,共3页
针对植物病害图像成分复杂、病斑排列无规则等特点,提出了一种改进型模糊聚类的病斑检测算法。该算法采用Markov随机场与模糊聚类算法耦合策略,能够有效解决植物病斑检测时的模糊性和随机性问题。仿真实验表明,改进后的算法能够实现植... 针对植物病害图像成分复杂、病斑排列无规则等特点,提出了一种改进型模糊聚类的病斑检测算法。该算法采用Markov随机场与模糊聚类算法耦合策略,能够有效解决植物病斑检测时的模糊性和随机性问题。仿真实验表明,改进后的算法能够实现植物病斑的自适应检测,鲁棒性较好。然而,对于Markov与模糊聚类算法的最佳耦合方式及对于如何减少算法的运算量仍需作深入的研究。 展开更多
关键词 植物病斑模糊C均值Markov随机场隶属度函数
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金沙江干热河谷典型植物叶片C、N、P生态化学计量特征研究 被引量:8
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作者 李鸿博 许云蕾 +3 位作者 余志祥 罗正平 董云祥 马焕成 《西北林学院学报》 CSCD 北大核心 2021年第3期10-16,共7页
选择攀枝花干热河谷区同一地块的22种植物叶片,分别对其C、N、P化学计量特征指标进行分析,以便了解其生化计量特征与抗旱性的关联性。结果表明,22种植物叶片叶C含量变化范围为365.03~476.33 g·kg^(-1),平均C含量为433.92 g·kg... 选择攀枝花干热河谷区同一地块的22种植物叶片,分别对其C、N、P化学计量特征指标进行分析,以便了解其生化计量特征与抗旱性的关联性。结果表明,22种植物叶片叶C含量变化范围为365.03~476.33 g·kg^(-1),平均C含量为433.92 g·kg^(-1);叶N含量变化范围为10.91~35.08 g·kg^(-1),平均N含量为20.51 g·kg^(-1),高于全球及全国叶N含量平均水平;叶P含量变化范围为1.94-3.10 g·kg^(-1),均值为2.56 g·kg^(-1),高于全球及全国叶P含量平均水平。聚类分析将22种植物分为4个类群,第Ⅰ类群C、N、P含量均值均较最高;第Ⅱ类群C含量较低,N含量较高,P含量较低;第Ⅲ类群C含量较高,N、P含量为4个类群中最低;第Ⅳ类群C含量最低,N含量较低,P含量较高。有11种植物叶片N∶P<10且N含量<20.0 g·kg^(-1),受到N元素限制;没有植物受到P元素限制;除白刺花、毛红椿外,其余9种植物均未受到2种元素限制。综合分析22个植物种N含量与N∶P呈显著正相关,而P含量与之相关性较弱的特征,认为N元素更可能限制干热河谷地区植物生长发育,而P含量较高表明植物体内存在主动吸收P元素来提高抗逆性的机制。 展开更多
关键词 干热河谷 植物叶片 生化计量特征 植物聚类 限制元素
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Fluorescence Discrimination and Determination Technique for Phytoplankton Composition by Coif2 Wavelet Packet
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作者 DUAN Yali SU Rongguo +3 位作者 SHI Xiaoyong WANG Xiulin ZHU Chenjian SUN Yan 《Journal of Ocean University of China》 SCIE CAS 2013年第1期53-62,共10页
An in vivo fluorescence discrimination technique for phytoplankton population was developed by using Wavelet packet transform, cluster analysis and non-negative least squares. The technique was used to analyze water s... An in vivo fluorescence discrimination technique for phytoplankton population was developed by using Wavelet packet transform, cluster analysis and non-negative least squares. The technique was used to analyze water samples from different sea regions. For simulative mixtures, when dominant species account for 60%, 70%, 80%, 90% at the division level, the correct discrimination ratios (CDRs) are 83.0%, 99.1%, 99.7% and 99.9% with the relative contents of 58.5%, 68.4%, 77.7% and 86.3%, respectively; when the algae dominance are 60%, 70%, 80%, 90%, 100% at the genus level, the CDRs are 86.1%, 94.9%, 95.2%, 96.8% and 96.7%, respectively. For laboratory mixtures, the CDRs are 88.1% and 78.4% at the division and genus level, respectively. For field samples, the CDRs were 91.7% and 80% at the division and genus level, respectively (mesocosm experiments), and the CDRs were 100% and 66.7% at the division and genus level, respectively (Jiaozhou Bay). The fluorometric technique was used to estimate the phytoplankton community composition and relative abundance of different classes for the April 2010 cruise in the Yellow Sea with the results agreeing with those in published papers by other authors. 展开更多
关键词 phytoplankton species wavelet packet transform feature spectra norm spectra database DISCRIMINATION
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Influences of sea ice on eastern Bering Sea phytoplankton
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作者 周茜茜 王鹏 +3 位作者 陈长平 梁君荣 李炳乾 高亚辉 《Chinese Journal of Oceanology and Limnology》 SCIE CAS CSCD 2015年第2期458-467,共10页
The influence of sea ice on the species composition and cell density of phytoplankton was investigated in the eastern Bering Sea in spring 2008. Diatoms, particularly pennate diatoms, dominated the phytoplankton commu... The influence of sea ice on the species composition and cell density of phytoplankton was investigated in the eastern Bering Sea in spring 2008. Diatoms, particularly pennate diatoms, dominated the phytoplankton community. The dominant species were Grammonema islandica (Grunow in Van Heurck) Hasle, Fragilariopsis cylindrus (Grunow) Krieger, F. oceanica (Cleve) Hasle, Navicula vanhoeffenii Gran, Thalassiosira antarctica Comber, T. gravida Cleve, T. nordenskioeldii Cleve, and T. rotula Meunier. Phytoplankton cell densities varied from 0.08× 10^4 to 428.8× 10^4 cells/L, with an average of 30.3× 10^4 cells/L. Using cluster analysis, phytoplankton were grouped into three assemblages defined by ice-forming conditions: open wate.r, ice edge, and sea ice assemblages. In spring, when the sea ice melts, the phytoplankton dispersed from the sea ice to the ice edge and even into open waters. Thus, these phytoplankton in the sea ice may serve as a “seed bank” for phytoplankton population succession in the subarctic ecosystem. Moreover, historical studies combined with these results suggest that the sizes of diatom species have become smaller, shifting from microplankton to nannoplankton-dominated communities. 展开更多
关键词 PHYTOPLANKTON sea ice Bering Sea community structure
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