排序方式： 共有17条查询结果，搜索用时 31 毫秒
1.
Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category. 相似文献
2.
Hanxiang Wu Hanhong Xu Ccile Marivingt‐Mounir Jean‐Louis Bonnemain Jean‐Franois Chollet 《Pest management science》2019,75(6):1507-1516
Systemicity of agrochemicals is an advantageous property for controlling phloem sucking insects, as well as pathogens and pests not accessible to contact products. After the penetration of the cuticle, the plasma membrane constitutes the main barrier to the entry of an agrochemical into the sap flow. The current strategy for developing systemic agrochemicals is to optimize the physicochemical properties of the molecules so that they can cross the plasma membrane by simple diffusion or ion trapping mechanisms. The main problem with current systemic compounds is that they move everywhere within the plant, and this non‐controlled mobility results in the contamination of the plant parts consumed by vertebrates and pollinators. To achieve the site‐targeted distribution of agrochemicals, a carrier‐mediated propesticide strategy is proposed in this review. After conjugating a non‐systemic agrochemical with a nutrient (α‐amino acids or sugars), the resulting conjugate may be actively transported across the plasma membrane by nutrient‐specific carriers. By applying this strategy, non‐systemic active ingredients are expected to be delivered into the target organs of young plants, thus avoiding or minimizing subsequent undesirable redistribution. The development of this innovative strategy presents many challenges, but opens up a wide range of exciting possibilities. © 2018 Society of Chemical Industry 相似文献
3.
以广西南部某县部分区域为研究区,采用ZY-1-02C卫星遥感图像为数据源,通过目视解译、屏幕矢量化方法,提取2012年实际采伐的伐区空间分布信息,在AreGIS平台支持下,与2012年伐区调查图进行空间叠置分析,对2012年实际采伐情况分析.结果表明：2012年实际采伐伐区共396个,总面积为4 557.8 hm2,其中有证采伐伐区267个,占总伐区数的68.4％,面积3 823.4 hm2,占总面积的83.9％;无证采伐伐区129个,占总伐区数的31.6％,面积734.4 hm2,占总面积的16.1％.在有证采伐伐区中,面积总误差为2.7％,总体重叠率为82.0％,其中超界采伐712.2 hm2,少采607.2 hm2.说明该研究区森林采伐中,无证采伐情况较为严重,即使是有证采伐,也存在部分伐区未严格按设计要求进行采伐的情况. 相似文献
4.
5.
空间数据计算机屏幕矢量化精度研究 总被引：2，自引：0，他引：2
研究屏幕分辨率和放大倍数对AutoCAD的空间数据屏幕矢量化精度的影响。结果表明，屏幕缩放方法对矢量化精度有较大影响：放大倍数提高使点位中误差和极限误差均下降；图形放大则提高了定点精度，放大8～10倍以上可以将点位中误差控制在0．08mm以下，即在允许中误差0．2mm的40％以下；放大8～10倍是进行屏幕矢量化的最佳方式；屏幕分辨率对点位中误差的影响较小。认为地理信息系统空间数据的人工干预屏幕数字化定点精度应该受到重视。 相似文献
6.
为解决文本特征提取不准确和因网络层次加深而导致模型分类性能变差等问题,提出基于深度卷积神经网络的水稻知识文本分类方法。针对水稻知识文本的特点,采用Word2Vec方法进行文本向量化处理,并与OneHot、TF-IDF和Hashing方法进行对比分析,得出Word2Vec方法具有较高的分类精度,正确率为86.44%,能够有效解决文本向量表示稀疏和信息不完整等问题。通过调整残差网络(Residual network,Res Net)结构,分析残差模块结构和网络层次对分类网络的影响,构建了9种分类网络结构,测试结果表明,具有4层残差模块结构的网络具有较好的特征提取精度,Top-1准确率为99.79%。采用优选出的4层残差模块结构作为基本结构,使用胶囊网络(Capsule network,Caps Net)替代其池化层,设计了水稻知识文本分类模型。与Fast Text、Bi LSTM、Atten-Bi GRU、RCNN、DPCNN和Text CNN等6种文本分类模型的对比分析表明,本文设计的文本分类模型能够较好地对不同样本量和不同复杂程度的水稻知识文本进行精准分类,模型的精准率、召回率和F1值分别不小于95.17%、95.83%和95.50%,正确率为98.62%。本文模型能够实现准确、高效的水稻知识文本分类,满足实际应用需求。 相似文献
7.
8.
9.
10.
在虚拟植物技术研究中,植物几何形态数据的获取及测量是一项基础而重要的工作.为此,提出了一种将植物叶片的栅格图像数据转换为矢量图像数据的方法.采用这种方法可以减少植物几何数据获取工作的时间,提高测量数据的精度和工作效率. 相似文献