试论我国农业专家系统的应用及发展

本文分析我国农业专家系统应用及发展现状 ,指出存在的问题 ,认为以“3S”技术为核心 ,由数据库技术、作物模拟模型、不精确推理等组成的技术体系 ,是我国农业专家系统的发展方向     

直接积分及分段积分在边界单元法中的应用

在边界单元法中,用积分递推公式代替高斯数值积分,以消除高斯数值积分在积分奇点上的误差,使计算靠近边界的内点应力和位移同其它内点具有相同的精确度,从而解决了应用边界元法计算狭长形状弹性体误差较大的问题。按该方法编制的计算机程序适合于线弹性的平面问题。可包含多介质及多连通体问题的计算。     

基于电子经纬仪及PDA自动量测的电子角规测树原理、功能及精度研究

针对常规角规测树较为粗放的现状,为了满足精准林业的需要,实现单木和林分精准测量,该文以电子经纬仪和掌上电脑为硬件,经PDA编程开发,形成电子角规测树系统,充分发挥了电子经纬仪精密的测角功能和PDA强大的数据处理和存贮功能.利用电子角规可获得树木树干的胸径、树高、任意处直径和材积,树冠部分的表面积和体积,林分的胸高断面积、林分平均高、平均株数、蓄积量和生长量等数据.经过分析,电子角规测树精度为1/7 000,而比特利希角规测树精度总误差达1/32,电子角规的测树精度比普通角规高出200倍以上,其速度也较快.     

星基差分GPS用于林区高分辨率遥感图像几何精校正

为解决林区缺乏大比例尺地形图而难以进行高空间分辨率遥感图像几何精校正问题,采用基于广域差分技术的星基差分GPS精确测定地面控制点(GCP),以多项式变换进行QuickBird pan(0.61 m)图像几何精校正.结果表明,全部GCP的点位误差均小于1个像元,总的点位误差为0.411 9.在进行图像灰度重采样中,最邻近点法、双线性内插法和三次褶积法的效果均无明显差异;就信息量和灰度值变动情况而言,以三次褶积法略好;就图像灰度保真而言,以最邻近点法略好.     

Error Sources Affecting Variable Rate Application of Nitrogen Fertilizer

The accuracy and interaction among global positioning system (GPS) horizontal accuracy, differential GPS (DGPS) sampling frequencies and machine delay times of a hypothetical variable rate applicator for nitrogen (N) fertilizer application based on an application map in Florida citrus were studied. Parameters studied included: five GPS horizontal accuracy levels, two levels of DGPS horizontal accuracy, two DGPS sampling frequencies and two machine delay times. Two integrated models were developed which documented the effects of the parameters. Machine delay time was the most important factor and GPS horizontal accuracy was the second most important.     

固定样地用于中分辨率遥感影像解译精度的研究

在总结了中国森林资源连续清查体系现状的基础上,以吉林省第7次森林资源连续清查固定样地资料和吉林省TM遥感影像为基础,对固定样地调查结果与遥感影像分类结果进行了对比,并采用点对点匹配法,分析了固定样地作为遥感影像解译标志时的精度.结果表明,采用固定样地调查与遥感影像分类调查森林资源总体精度较高,森林覆盖率误差仅为-1.04%,而点对点匹配检验时森林类型误差达到52.99%.     

采谱方式对竹材气干密度近红外预测模型精度的影响

该文对近红外光谱技术结合化学计量学分析方法快速预测毛竹材气干密度的可行性进行了研究,重点探讨不同采谱方式对所建模型预测精度的影响.为了便于对比,建模过程中光谱数据未经过任何预处理.结果表明,采谱方式影响着模型的预测精度.从竹材横切面采谱所建模型的预测精度最高,内表面（靠近竹黄面）居中,外表面（靠近竹青面）最差.但如果对竹材内外表面的光谱求平均后建模,则可以显著提高模型的预测精度.随后选择最优模型对随机抽取、未参与建模的30个未知样品的密度进行了预测,预测值与测量值的 相关性高,表明近红外光谱技术可以快速、准确地预测竹材的气干密度.      

6SF-40型加工番茄色选设备性能试验与参数优选

为研究加工番茄色选设备作业参数对其工作性能的影响规律,以分选精度和带出比为评价指标,对传送带速度、工作绝对压力及电磁阀脉频宽度等试验因素进行正交试验,对结果进行极差分析和方差分析,研究各因素对色选设备性能影响的主次关系和影响规律,确定较优工作参数组合.结果表明:输送带速度为0.816m/s、工作绝对压力0.5MPa、电磁阀脉频宽度为20ms时为较优参数组合,分选精度可达98%,带出比低于4,满足工厂加工要求.     

黄土高原沟壑区典型小流域高精度DEM制作及其应用研究

高精度数字高程模型(DEM)是水土流失规律研究与流域地形空间分析中重要的空间数据。以黄土高原沟壑区的南小河沟流域为例,以1：1万地形图为数据源,探讨了在Arcgis和ArcView地理信息平台下,生成高精度DEM及提取流域土壤侵蚀指标的步骤和方法。结果表明,提取的地形信息能准确地反映该区域地形起伏变化与破碎状况,满足当前“数字流域”建设的需要。     

Error assessment in decision-tree models applied to vegetation analysis

Methods were developed to evaluate the performance of a decision-tree model used to predict landscape-level patterns of potential forest vegetation in central New York State. The model integrated environmental databases and knowledge on distribution of vegetation. Soil and terrain decision-tree variables were derived by processing state-wide soil geographic databases and digital terrain data. Variables used as model inputs were soil parent material, soil drainage, soil acidity, slope position, slope gradient, and slope azimuth. Landscapescale maps of potential vegetation were derived through sequential map overlay operations using a geographic information system (GIS). A verification sample of 276 field plots was analyzed to determine: (1) agreement between GIS-derived estimates of decision-tree variables and direct field measurements, (2) agreement between vegetation distributions predicted using GIS-derived estimates and using field observations, (3) effect of misclassification costs on prediction agreement, (4) influence of particular environmental variables on model predictions, and (5) misclassification rates of the decision-tree model. Results indicate that the prediction model was most sensitive to drainage and slope gradient, and that the imprecision of the input data led to a high frequency of incorrect predictions of vegetation. However, in many cases of misclassification the predicted vegetation was similar to that of the field plots so that the cost of errors was less than expected from the misclassification rate alone. Moreover, since common vegetation types were more accurately predicted than rare types, the model appears to be reasonably good at predicting vegetation for a randomly selected plot in the landscape. The error assessment methodology developed for this study provides a useful approach for determining the accuracy and sensitivity of landscape-scale environmental models, and indicates the need to develop appropriate field sampling procedures for verifying the predictions of such models.