水稻叶片气孔长度、宽度和密度及其相关性的研究

采用改良刮制法对水稻叶片气孔的长度、宽度和密度及其相关性进行了研究。结果表明，籼稻和粳稻的叶片平均气孔长度、宽度和密度的大小顺序均为籼稻>粳稻，两种类型水稻叶片的气孔长度的差异和气孔宽度的差异均不显著。气孔性状间的相关分析表明，气孔长度与宽度，气孔宽度与密度间的相关不显著，而气孔长度与密度间存在明显的负相关(p＜0.05)。     

减弱光照强度对水稻叶片气孔性状的影响

采用改良刮制法研究了典型粳稻，典型籼稻，偏籼型，偏粳型和陆稻等10个水稻品种的气孔性状，研究结果发现，剑叶的气孔密度在水稻类型间和品种间具有明显差异。是一个品种特性，光照强度减弱使各品种上三叶的气孔密度明显减少。其原因是由于叶面积增大，气孔行数减少和每一行中相邻气孔间距离增大共同作用的结果。品种不同，其气孔密度对光强变化的反应亦不同，光照强度减弱对剑叶气孔大小有显影响。     

典型粳稻植株气孔分布的研究

用改良刮制法观测了典型粳稻品种秋光剑叶、叶鞘、穗颈及颖壳的气孔分布情况,结果表明：剑叶背面气孔分布有一定规律性,但此规律性常因气孔行的突然中断被削弱。剑叶背面气孔密度纵向变化呈偏态分布,叶中部气孔密度大于叶基又大于叶尖部;叶中部气孔长度最小,气孔宽度最大。剑叶背面横向气孔密度呈中部最大的正态分布,气孔大小变化不大。叶片边缘气孔行不分组。本研究还发现,中脉和叶鞘上亦有气孔分布,而且中脉上的气孔密度在所测各器官中最大,叶鞘上的气孔密度约为剑叶背面气孔密度的1／3左右。穗颈上有少量气孔分布。     

水分胁迫对水稻叶片气孔密度,大小及净光合速率的影响

在水分胁迫下，水稻叶片的气孔密度明显增大，气孔的长、宽明显减小．气孔密度与气孔长度、宽度呈显著的负相关（r＝－0．90*，n＝7）、而气孔长度和宽度呈显著的正相关（r＝0．71*，n＝7）．在水分胁迫下，水稻叶片的净光合速率也显著下降，并表现出与气孔密度呈显著负相关（r＝－0．89，n＝7）．     

不同类型水稻品种对稻田土壤基础供氮量的响应

选用常规粳稻、杂交粳稻、常规籼稻、杂交籼稻及两系杂交稻5种类型共7个水稻品种,在大田中不施氮条件下,研究了供试水稻材料对土壤基础供氮量利用的差异。结果表明:①籼稻的土壤基础供氮量平均比粳稻高25.6%;杂交籼稻比常规籼稻高19.5%,杂交粳稻比常规粳稻高32.5%。②土壤基础供氮量在植株器官中的分配比例以叶、茎鞘的变异幅度较大,而穗的变异幅度则较小;叶中氮的分配比例以粳稻最高,而杂交粳稻与常规粳稻间及杂交籼稻与常规籼稻之间无明显差异。③土壤氮素利用效率(NUEs)随着土壤基础肥力上升而提高,粳稻的NUEs提高幅度大于籼稻。土壤氮籽粒利用效率(Ng/Ns)以两系杂交籼稻最高,籼稻的Ng/Ns大于粳稻,杂交籼稻与常规籼稻及杂交粳稻与常规粳稻间Ng/Ns无明显差异。④100kg籽粒需氮量增加幅度也是粳稻高于籼稻。     

基于Snake模型的铁路铸件气孔检测

研究Snake模型在铁路铸件数字化辐射成像(DR)图像气孔缺陷自动检测中的应用,改进初始轮廓点的获取方法。综合运用阈值分割和区域生长方法得到各气孔的重心,依次对其采用一种射线法得到初始控制点,对初始控制点进行收敛和拟合。对仿真图像的实验结果表明,最小检测尺寸为3×3,能较好地收敛到目标的凹陷区域;对实际铁路铸件DR图像检测的实验结果表明,该方法能准确得到检测区域内多个气孔缺陷的轮廓,不会检测出伪缺陷,具有较高的自动化程度。     

端部结构三大梁焊缝气孔原因分析及解决措施

气孔是铝合金焊接中常见的缺陷之一,其会降低焊缝的致密性,削弱焊缝的有效截面积,从而降低焊缝的力学性能。本文通过分析环境温湿度、焊接工艺、自动焊接设备等因素,找出了造成铝合金车体底架端部结构三大梁焊缝密集型气孔的原因。通过逐一排查和试验分析,发现此密集型气孔是由于悬臂MIG自动焊接专机的气路系统密封性问题所导致的。由于自动焊专机焊接系统焊枪喷嘴处的保护气体流量过小,气体保护不足,致使焊缝产生密集型气孔。经测量发现,从气瓶到焊枪喷嘴的保护气流量发生了严重的衰减,衰减值高达13 L/min。针对此问题,改造自动焊专机原有气路系统,确保焊枪喷嘴处保护气体流量到达22 L/min,保护了电弧、金属熔滴、熔池和焊接区,有效控制了焊接气孔的产生。     

多相机系统精确识别和分析压铸制品上的微小气孔

在铸造领域,熔体在凝固过程中因体积收缩可能会形成气孔,进而影响压铸制品的功能——这是任何压铸件厂商都不愿意看到的事。自2016年以来,Gruber&Kaja公司使用AUTFORCE公司的多相机系统,能够可靠检测出350×400mm的压铸件中直径在0.4mm以上的气孔,以确保为客户提供无气孔的铝压铸件。借助6台堡盟LX系列工业相机,专业人员成功地解决了一个被认为不可能通过自动化来完成的任务。     

基于X射线图像的厚钢管焊缝中气孔缺陷的自动检测

由于厚钢管X射线图像强度分布不均匀,对比度低、噪声大,且气孔缺陷的大小、形状、位置、对比度各异,使得自动检测各种类型的气孔较为困难。针对传统缺陷检测算法中手工标记缺陷数据工作量大,焊缝边缘难以准确提取等问题,提出一种新的无监督学习的各种气孔缺陷检测算法。首先,采用快速独立分量分析从钢管X射线图像集合中学习一组独立基底,并用该基底的线性组合来选择性重构带气孔缺陷的测试图像;随后,测试图像与其重构图像相减获得差异图像,通过全局阈值从差异图像中将各种气孔分割出来。实验的训练集有320幅,测试集有60幅图像,所提算法检测结果的平均敏感性和准确率为90.5%和99.7%。实验结果表明,该算法无需手工标记数据或提取焊缝边缘,可准确检测各种气孔缺陷。     

大穗型超级杂交粳稻辽优3072产量结构及灌浆特性研究

对北方大穗型超级杂交粳稻辽优3072进行了产量结构和灌浆特性的研究。结果表明：辽优3072在产量结构上表现出了较强的杂交优势，比对照辽粳294增产明显。辽优3072在灌浆上的特点为穗上、中、下各部间干物质充实及灌浆速率差异较小：穗下部灌浆启动早，后期灌浆速率大；整穗表现出较强的持续灌浆能力。辽优3072良好的籽粒灌浆特性是其在产量上表现出超高产的生理基础之一。同时探讨了利用水稻杂交优势选育具有特异灌浆特性的杂交组合的育种思路以及在北方冷凉稻区推广大穗型超级杂交粳稻品种的可能性。     

水稻叶绿素浓度与光谱反射率关系研究

结合田间小区实验,获取了水稻生育期9组叶片叶绿素浓度和相应的光谱反射率数据,进行了相关分析研究,结果表明:水稻叶片叶绿素浓度与其光谱反射率之间具有相关性,在450～686nm和750～770 nm光谱区内相关性较好,且在686 nm处两者达到最高的相关性;水稻叶片的“红边”拐点波长位置与其叶绿素浓度之间具有很强的相关性,复相关系数为0.88,并由此进一步讨论了高光谱遥感定量探测水稻叶绿素浓度的可行性。
     

A portable field ultrasonic sensor for crop canopy characterization

An ultrasonic pulse wave transceiver was devised and tested in com, soybean, rice, and sorghum fields for sensitivity to canopy structural differences. An ultrasonic pulse was emitted 30–50 cm above the canopy surface, and its echo was measured. Received echo patterns were logged into a microcomputer memory and analyzed. Light extinction and leaf area by strata measurements were made right after the ultrasonic measurements. Profiles of echo peaks for 20, 40, and 60 kHz “sound” frequencies with height in the canopies were compared with profiles of plant parts (green leaves, stems, dry leaves, tassels, ears, and pods), dry weight, and relative transmitted light. The ultrasonic pulse was especially sensitive to horizontal components in the plant canopies, indicating the feasibility of surveying the vertical distribution of crop leaves in a canopy nondestructively. The 20 kHz frequency reached the lower, inner positions of the canopies better than the 40 kHz frequency.     

不同穗型水稻群体垂直入射光分布及其对光合作用的影响

依据推导的水稻上三叶截获垂直入射光的模型，并结合叶面光强与光合速率的经验公式对上三叶光合作用理论值进行了分析，结果表明：穗的直立或弯曲是决定垂直入射光在群体内分布及其对应的上三叶总光合作用的关键性状。直立穗型水稻沈农265株形好，具有较高的光合作用理论值，如果将辽粳294的叶型与沈农265的穗型相结合，有可能通过密植提高上三叶光合作用量，达到增产的目的。     

Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data

Two nitrogen experiments on rice were conducted in 2002, and the reflectances (350 to 2500 nm) and pigment contents (chlorophylls a and b, total chlorophylls and carotenoids) for leaf and panicle samples at different growth stages were measured in the laboratory. After performing an outlier analysis, the number of samples were 843 for leaves and 188 for panicles. Absorption features at 430, 460, 470, 640 and 660 nm for different pigments, and the relative reflectance of the green peak around 550 nm calculated by the continuum‐removed method, as well as the red edge position (REP) of rice leaves and panicles were selected as the independent variables, and measured pigment contents were selected as the dependent variables. Then, back propagation neural network (BPN) models, a kind of artificial neuron network (ANN), and multivariate linear regression models (MLR) were trained and tested. The main objective of this study was to compare the predictive ability of the ANN models to that of the MLR models in estimating the content of pigments in rice leaves and panicles. Results showed that all BPN models gave higher coefficients of determination (R²) and lower absolute errors (ABSEs) and root mean squared errors (RMSEs) than the corresponding MLR models, in both calibration and validation tests. Further significance tests by paired t tests and bootstrapping algorithms indicated that most of the BPN models outperformed the MLR models. When trained by combination data that did not meet the assumption of normal distribution, the BPN models appeared to not only have a better learning ability, but also had a more accurate predictive power than the MLR models. The estimation of leaf pigments was more accurate than that of panicle pigments, independent of which model was used.     

Mining Causality for Explanation Knowledge from Text

Mining causality is essential to provide a diagnosis within multiple sentences or EDUs （Elementary Discourse Unit） This research aims at extracting the causality existing The research emphasizes the use of causality verbs because they make explicit in a certain way the consequent events of a cause, e.g., ＂Aphids suck the sap from rice leaves. Then leaves will shrink. Later, they will become yellow and dry.＂. A verb can also be the causal-verb link between cause and effect within EDU（s）, e.g., ＂Aphids suck the sap from rice leaves causing leaves to be shrunk＂ （＂causing＂ is equivalent, to a causal-verb link in Thai）. The research confronts two main problems： identifying the interesting causality events from documents and identifying their boundaries. Then, we propose mining on verbs by using two different machine learning techniques, Naive Bayes classifier and Support Vector Machine. The resulted mining rules will be used for the identification and the causality extraction of the multiple EDUs from text. Our multiple EDUs extraction shows 0.88 precision with 0.75 recall from Naive Bayes classifier and 0.89 precision with 0.76 recall from Support Vector Machine.     

基于Android的水稻病害图像识别系统设计与应用

针对目前水稻病害图像识别系统依赖于数码相机和计算机,缺乏便携性和实时性的问题,设计了一款基于Android手机的水稻病害图像识别系统。系统通过分析水稻稻瘟病、胡麻斑病、干尖线虫病、白叶枯病四种病害的颜色、形状、纹理特征,采用图像预处理、图像增强、图像分割、特征提取以及图像识别的处理方法,实现基于图像识别的及时准确诊断水稻病害类型的目的。实验结果表明,系统准确率可达93.78%,正检率96.22%,误检率6.22%,虚警率1.56%,平均诊断用时2.802s。该系统能有效地拍摄并诊断水稻病害,迅速、准确地给出病害防治措施。     

水稻叶片三维建模与叶色渲染

提出一种简单的水稻叶片三维建模与可视化方法。通过输入一张二维水稻叶片图像,提取叶片的基本轮廓,创建样条曲线并计算这些样条曲线的交点,然后将这些样条曲线相互连接构建水稻叶片分支脉络,从而建立叶片三维网格模型。利用纹理映射技术,将真实水稻叶片灰度纹理图像作为模型材质贴图,得到富含材质信息的叶片模型,结合前期建立的水稻叶色R、G、B三通道与SPAD的关系模型,实现了水稻叶色的实时渲染,得到的结果真实感强。