综合选择指数的决策分析

建立了综合选择指数的通径分析化模型,即综合选择指数的相关遗传力分解模型,提出了综合选择指数的决策分析方法,从而使综合选择指数的路径信息最优化。提出了确定主选性状、辅助性状和限制性状的方法,便于育种者根据个体的性状表现进行选择。     

新疆细毛羊综合选择指数和列线图的使用

综合选择指数,在进行绵羊育种中可获得最大的遗传进展和最佳选择效果。绘制列线图可减少计算手续,我们利用新疆细毛羊产毛量、毛长、活重三个性状制定了综合选择指数,并绘制列线图。     

强优势杂交小麦综合选择指数分析

利用1999-2001年选用的9个性状差异较大的品种(系),按完全双列杂交(正交)组配成36个杂交组合,用Nair单性状水平分组法将杂交组合进行了分组,依产量和产量优势性状可分为4组。按分组结果对产量优势处于前两组的杂交组合进行综合选择指数分析,得到了综合选择指数I值,并对I值排序与杂交组合的产量排序进行比较,发现其拟合度为80%,证明综合选择指数分析方法在强优势杂交小麦组合的选育中具有较强的实用性。     

亚麻籽实产量选择指数的研究

本文以我所近年来选育出的8个高产、抗病品种（系）为材料,研究亚麻籽实单产性状的选择指数。结果表明亚麻籽实单产性状遗传力较低,直接选择效果差,通过相关性状进行间接选择效果亦不好。根据所有选择指数方程为单产性状进行选择,只有综合主茎分枝数,单株桃数,单桃粒数,单株粒重四性状为单产进行间接选择其效果才最佳,选择效率达153．6％,用选择指数对供试的8个品种评审结果表明,坝亚7号是综合性状优良的高产、抗病品种,1009是综合性状最差的品种。     

中国荷斯坦奶牛不同选择方法的比较

利用浙江金华伊康奶牛场第1胎次181头中国荷斯坦奶牛的305日龄产奶量、乳脂率及体形外貌评分资料,开展综合指数选择,并与顺序选择法和独立淘汰法进行比较。结果表明,奶牛选择指数呈正态分布,选择指数≥90的奶牛头数占整个群体的88.95%;选择指数的排序与奶牛在实际生产中表现基本一致,产奶量和乳脂率的尤为明显。在同一淘汰率的情况下,综合指数选择法选择效果优于顺序选择法和独立淘汰法,尤其是产奶量。综合选择指数法具有准确性高、效果好的特点,可用于奶牛的育种实践。     

杨树纸浆材优良无性系选择方法的研究

该文利用主成分分析法、遗传距离聚类分析、选择指数和综合性状评分法 ,对 7年生 2 5个杨树无性系进行优良无性系多性状选择 .通过对上述几种方法的比较认为 :遗传距离聚类分析可把无性系分为在主成分值上各具不同特点的类群 ,从而使具有相似特征的无性系归为一类 ,且在优良类群内可以继续选择 ;主成分分析可以评价各无性系综合性状的优劣 ,并作为无性系选择的参考 ;选择指数是评价生长和材性性状较为理想的方法 ;综合性状评分法 ,从选择结果看与前 3种方法结合起来所选的基本一致 ,而且综合性状评分法是一种较为简便、直观的方法 .4种方法共同应用 ,能够较准确地选出生长迅速和材性优良的无性系 .     

陆地棉主要纤维品质性状的遗传力、遗传相关和选择指数的估测

1983至1985三年间,在合肥市安徽农学院试验农场,研究了陆地棉四个杂交组合的纤维长度、细度、强度和断裂长度的遗传力,遗传相关和选择指数。结果表明:长度、细度的遗传力属于中等,强度、断裂长度的遗传力偏低;强度与断裂长度表现出极显著的正相关,而与细度则成中等程度的负相关。利用选择指数进行选择,选择效率有随参加选择性状数的增加而提高的趋势。     

利用互不相关的选择指数进行独立水平选择

利用主成分分析和因子分析法来构造相互独立的选择指数组;介绍了利用这些选择指数组进行独立水平选择的方法,且将这2种指数与典范选择指数进行了比较分析.     

春小麦单株产量及其产量的选择指数

研究了１６个春小麦新品系的植株高度，穗颈长，单株穗数，单穗粒数及千粒重等性状与单株籽产量的选择指数的关系，结果表明，用单穗粒数和千粒重２个相关性构成的选择指数，对单株籽粒产量进行了选择的效果最好。     

水稻数量性状的相关和单株产量的选择指数

本文研究了水稻10种数量性状间的相关性,遗传进度和选择指数等遗传参数。结果表明,相关和通径分析以每穗粒数,每株穗数、株高和穗长对单株产量贡献较大,故选择时应予以重视。在单株粒重的选择指数中每穗粒数是重要的性状,每株穗数是相关选择的关键性状,选择大穗同时兼顾株高对单株产量也有一定的提高效果,应作为限制性选择指标。在水稻育种中应用选择指数方法进行综合选择比对单株产量单一性状直接选择效率高20.42%,应用前景广泛。     

An integrated method of selecting environmental covariates for predictive soil depth mapping

Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. In this study, we proposed an integrated method to select environmental covariates for predictive soil depth mapping. First, candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge. Second, three conventional methods (Pearson correlation analysis (PsCA), generalized additive models (GAMs), and Random Forest (RF)) were used to generate optimal combinations of environmental covariates. Finally, three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate. We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China. A total of 129 soil sampling sites were collected using a representative sampling strategy, and RF and support vector machine (SVM) models were used to map soil depth. The results showed that compared to the set of environmental covariates selected by the three conventional selection methods, the set of environmental covariates selected by the proposed method achieved higher mapping accuracy. The combination from the proposed method obtained a root mean square error (RMSE) of 11.88 cm, which was 2.25–7.64 cm lower than the other methods, and an R² value of 0.76, which was 0.08–0.26 higher than the other methods. The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.     

智能手机原位牧草生物量估算

【目的】生物量是草地生态系统物质和能量基础,是最基本的生态参量。以往基于卫星和航空遥感定量反演草地生物量过于专业化,难以在牧民间推广。因此,本文提出一种用手机近距离拍摄的真彩色图像估算牧草生物量方法,构建牧草生物量估算模型,为牧民方便、快捷、无损地掌握牧场牧草长势提供理论依据和技术支撑。【方法】首先,利用手机超高分辨率真彩色图像,分别基于植被指数、纹理特征以及联合植被指数和纹理特征构建牧草生物量估算特征集合。其次,为防止过多的特征提取带来维度灾难,提出一种XGBoost与序列前向选择相结合的特征选择算法（XGB-SFS）,进行特征筛选及最优子集构建。最后,使用随机森林回归和留一法交叉验证对比不同特征集合构建模型的生物量估算效果,分析不同类型特征及XGB-SFS算法在牧草地上生物量（Above Ground Biomass,AGB）估算中的作用。【结果】（1）对比单类型特征构建的模型,基于空间纹理特征的估算模型（R²=0.76）要优于基于光谱植被指数估算模型（R²=0.73）,表明纹理特征在超高分辨率牧草AGB估算中具有一定作用;（2）对比特征选择后的模型,联合空谱多类型特征构建模型优于任何一种单类型特征模型（R²=0.83,RMSE=127.57 g·m^-2,MAE=81.25 g·m^-2）,表明使用多类型特征构建模型,可一定程度上提高牧草AGB估算精度。（3）对比特征选择前后构建的模型,特征选择后的模型估算AGB效果要明显好于未进行特征选择的模型,且筛选出的特征与牧草生物量之间都存在较高的相关性,表明XGB-SFS能够很好降低数据维度的同时提高牧草AGB估算精度。【结论】手机超高分辨率真彩色图像可以对牧草生物量进行准确估算,本文提出的XGB-SFS算法也能从众多特征中筛选出与牧草生物量相关性较高的特征并提高模型估算精度。与以往专业遥感定量反演草地生物量相比,本文方法具有面向大众、成本低廉、使用方便等优势,研究将手机现场采集的数据与遥感和机器学习方法相结合,可开辟新的视角,支持农业信息化发展。     

How do temporal and spectral features matter in crop classifi cation in Heilongjiang Province,China?

How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study,taking Heilongjiang Province as the study area,aims to use time-series moderate resolution imaging spectroradiometer(MODIS) surface reflectance product(MOD09A1) data to evaluate the importance of spectral and temporal features for crop classification. In doing so,a feature selection strategy based on separability index(SI) was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised,which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index(NDTI),land surface water index(LSWI) and enhanced vegetation index(EVI) are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification,and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy(producers' accuracy of 94.03% and users' accuracy of 93.77%) with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops,and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.     

10个常绿树种叶片中铁锌铜锰的含量特征

通过对10个常绿树种在昆钢污染区和相对无污染区叶片中铁锌铜锰4种重金属元素含量的实验测定和分析,研究了各树种叶片中的金属含量特征和对污染的吸收净化能力。结果表明,不同的树种对不同重金属的抗性和吸收净化能力不同,参试的10个树种均对重金属元素表现出较好的抗性和吸收净化能力,叶片中铁含量最高的是飞蛾槭,锌含量最高的是鳞斑荚蒾,铜含量最高的是金叶子,锰含量最高的是滇青冈。叶片中铁和锌、铜含量的相关性达极显著和显著水平,其他元素之间相关性均不显著。按各树种的综合富积量进行排序,鳞斑荚蒾>红果树>飞蛾槭>茶条木>金叶子>云南泡花树>云南木樨榄>滇青冈>云南卫矛>子楝树。     

Image-based orchard insect automated identification and classification method

Insect identification and classification is time-consuming work requiring expert knowledge for integrated pest management in orchards. An image-based automated insect identification and classification method is described in the paper. The complete method includes three models. An invariant local feature model was built for insect identification and classification using affine invariant local features; a global feature model was built for insect identification and classification using 54 global features; and a hierarchical combination model was proposed based on local feature and global feature models to combine advantages of the two models and increase performance. The three models were applied and tested for insect classification on eight insect species from pest colonies and orchards. The hierarchical combination model yielded better performance over global and local models. Moreover, to study the pose change of insects on traps and the hypothesis that an optimal time to acquire and image after landing exists, advanced analysis on time-dependent pose change of insects on traps is included in this study. The experimental results on field insect image classification with field-based images for training achieved the classification rate of 86.6% when testing with the combination model. This demonstrates the image-based insect identification and classification method could be a potential way for automated insect classification in integrated pest management.     

特征抽取的一个新算法

特征抽取无论在模式识别还是在光谱信道和色谱柱组合选择中,皆具有重要意义.本文提出了一个基于复相关系数的特征抽取新方法.藉扫描算法,本法可在微机上方便快迷运行.将该法对用于诊断两类疑难疾患的模式识别数据进行了特征变量的选择.获得满意结果.     

冬小麦早代株高构成指数与产量关系的研究

试验以不同类型的小麦杂交组合后代为材料，探讨在异质竞争条件下植株形态性状与产量的关系，并以此对小麦高产群体株型结构选择指标进行分析。结果表明：植株各节间在异质竞争条件下对产量性状的影响作用不同。株高构成指数（１）的高低可以较好地反映群体产量的高低。Ⅰ值较高的组合有利选出高产类型。     

基于成像高光谱的小麦冠层白粉病早期监测方法

[目的]本研究利用近地面成像高光谱仪,获取接种白粉病菌后的小麦田间冠层时序影像,探索光谱信息与纹理信息的结合在冠层尺度上早期监测小麦白粉病的能力和表现.[方法]本试验以不同年份、不同抗病性小麦品种的田间试验为基础,利用连续小波(continuous wavelet transform,CWT)方法提取对小麦白粉病敏感的...     

决定系数遗传力及其在育种上的应用

提出了与相关变异、间接选择和综合选择有关的一个新的遗传参数，即决定系数遗传力，可定义为选择性状与目标性状的遗传协方差除以选择性状的表型标准差和目标性状的遗传标准差之积。本文证明了利用决定系数遗传力可以把间接选择与直接选择统一到一个公式中来，且把综合选择指数与通径分析统一起来，并讨论了它的应用。