To study the effect of tobacco growth on Trichoderma population, we in vestigated the occurrence of Trichoderma species in the rhizosphere of tob acco plant during the period from transplanting (June) to harvesting (O...To study the effect of tobacco growth on Trichoderma population, we in vestigated the occurrence of Trichoderma species in the rhizosphere of tob acco plant during the period from transplanting (June) to harvesting (October) and measured relative environmental factors. Eleven species of Trichoderma we re isolated, among which T.harzianum, T.viride, T.hamatum, T.atroviride, T.lo ngibrachiatum, T.virens, T.koningii were identified, other four species Ty1, Ty2, Ty3, Ty4 are new species. Of the species which occurred at high frequencies , T.harzianum and T.hamatum were most abundant in the July and T. viride in the August. The occurrence of the above three abundant species corr elates significantly with the developmental phase of tobacco and correlates appa rently with the soil moisture content, but not with the temperature. For the oth er eight species, no obvious correlation was found between the above factors and them. Based on nucleotide sequences of the internal transcribed spacer (ITS) regions of the rDNA cluster (ITS1and 2) and partial sequences of translation elongation f ac tor 1-alpha (tef1a), phylogenetic position of four new species was determined. Ty2 is additions to Trichoderma section and Ty3.Ty9.Ty10 are additions to sec tion Pachybasium.展开更多
提出一种基于Faster RCNN(Faster Region with Convolutional Neural Networks)的电路板缺陷图像自动检测方法。该方法首先应用ResNet50网络作为主干网络以提取缺陷图像特征;然后针对电路板图像中缺陷的极端长宽比特点,提出基于特征金...提出一种基于Faster RCNN(Faster Region with Convolutional Neural Networks)的电路板缺陷图像自动检测方法。该方法首先应用ResNet50网络作为主干网络以提取缺陷图像特征;然后针对电路板图像中缺陷的极端长宽比特点,提出基于特征金字塔的区域推荐网络,得到多尺度特征融合图并生成建议框;最后,通过对感兴趣区域进行池化处理,并结合后续网络实现对电路板图像上缺陷的快速检测。试验证明,所提算法能够快速定位出电路板图像中的各种缺陷,并能实现准确的自动分类识别。展开更多
文摘针对合成孔径雷达(Synthetic Aperture Radar,SAR)图像目标识别算法识别准确率极易受到斑点噪声影响,且模型容易出现过拟合的问题,提出了一种基于Frost滤波和改进的卷积神经网络的目标识别方法。利用Frost滤波算法对SAR图像进行了滤波处理,减少了相干斑噪声;对CNN网络进行改进,建立了一个SAR图像目标识别模型,在网络中引入L2正则化和Dropout结构,抑制过拟合现象的发生;采用Adam优化算法,提高模型的收敛效率;最后采用组合的数据增强方法,扩充SAR图像数据集,进一步提高识别的准确率。利用美国运动和静止目标获取与识别(Moving and Stationary Target Acquisition and Recognition,MSTAR)SAR图像数据进行实验,综合识别准确率可以达到98.06%,结果表明本文所提的方法具有更好的识别效果。
文摘To study the effect of tobacco growth on Trichoderma population, we in vestigated the occurrence of Trichoderma species in the rhizosphere of tob acco plant during the period from transplanting (June) to harvesting (October) and measured relative environmental factors. Eleven species of Trichoderma we re isolated, among which T.harzianum, T.viride, T.hamatum, T.atroviride, T.lo ngibrachiatum, T.virens, T.koningii were identified, other four species Ty1, Ty2, Ty3, Ty4 are new species. Of the species which occurred at high frequencies , T.harzianum and T.hamatum were most abundant in the July and T. viride in the August. The occurrence of the above three abundant species corr elates significantly with the developmental phase of tobacco and correlates appa rently with the soil moisture content, but not with the temperature. For the oth er eight species, no obvious correlation was found between the above factors and them. Based on nucleotide sequences of the internal transcribed spacer (ITS) regions of the rDNA cluster (ITS1and 2) and partial sequences of translation elongation f ac tor 1-alpha (tef1a), phylogenetic position of four new species was determined. Ty2 is additions to Trichoderma section and Ty3.Ty9.Ty10 are additions to sec tion Pachybasium.
文摘提出一种基于Faster RCNN(Faster Region with Convolutional Neural Networks)的电路板缺陷图像自动检测方法。该方法首先应用ResNet50网络作为主干网络以提取缺陷图像特征;然后针对电路板图像中缺陷的极端长宽比特点,提出基于特征金字塔的区域推荐网络,得到多尺度特征融合图并生成建议框;最后,通过对感兴趣区域进行池化处理,并结合后续网络实现对电路板图像上缺陷的快速检测。试验证明,所提算法能够快速定位出电路板图像中的各种缺陷,并能实现准确的自动分类识别。
