倒数累加非等间距GM(1,1)模型及其在农业机械中的应用

在研究非等间距GM（1,1）模型的基础上,发现当原始数据递增时,拟合和预测的结果较好;当原始数据递时,拟合和预测的结果误差较大。因此,将递减的原始数据转换为递增的数据可以提高拟合和预测的精度,该文采用的方法是对原始数据进行倒数运算。实例结果显示,该方法能有效地拟合和预测系统的发展,为灰色提高预测精度提供了一个有效的方法。     

基于无线局域网的AIS数据显示终端的开发

提供了一种新颖的无线AIS数据终端解决方案.AIS主机通过AIS无线数据终端将数据发送至无线局域网,任何接入无线局域网的节点均可通过无线方式获取AIS数据.在安装了应用软件的手持终端上测试,成功实现了AIS数据解析并生成AIS海图.     

SIMHYD模型在Goulburn流域中的应用

介绍了SIMHYD降雨径流模型的基本结构及计算原理,并用该模型对Goulburn流域的主要产流区Eildon以上汇流区进行了校核和验证。研究区的降雨数据采用澳大利亚昆士兰州基础工业部的Rainman Streamflow数据光盘,由于光盘数据省略了降雨量为0的所有天数,因此在该研究中用Visual Basic编写了1个Excel的宏程序对记录数据中降雨量为0的情况进行了处理。结果表明,该模型可以很好地对该流域月径流进行模拟,采用的Nash-Sutcliffe效率系数在校核和检验期分别达到了90.6%和94.0%。该模型不仅具有结构简单、参数较少的特点,而且考虑了超渗与蓄满2种产流机制,对输入资料要求低,一般流域资料都可以满足其输入需要。因此,模型具有适用范围大、易于推广应用的优点。     

模糊数据库技术在高速公路养护系统中的应用

为解决目前高速公路养护系统模糊数据的处理问题,在BS模式高速公路养护系统中,利用模糊法则,考虑路况取样对几种不同情况的隶属度,并将其定义为连续、平滑的隶属函数.将其中的模糊数据整合到ORACLE数据库中,利用加权平均法对收集的每km路段各种模糊数据进行处理,给出该路段状态量化数值.建立数据模型,在ORACLE数据库中对模糊对象的输入、判别和处理进行了研究,并以实例说明重心法、最大隶属度法、系数加权平均法和隶属度限幅元素平均法等模糊判决方法的计算过程及其SQL查询的实现,以及查询和分析模糊数据的技巧,同时给出对应的部分Java实现代码.在高速公路养护系统中使用模糊数据对象的数据库,提高了数据的安全性和封装性,处理过程也不复杂,此处理方法可以使用到类似的MIS系统中.     

农产品质量溯源用RFID温度测量记录系统的研究

提出了一种用于农产品质量溯源的RFID温度测量记录系统.该系统针对农产品小包装,测量包装内的温度数据,并将测量结果记录到附着于包装上的RFID标签内,作为食用农产品质量溯源的依据.试验结果表明该系统测量、记录温度数据结果可靠,切实可行.     

光谱技术结合化学计量学分析方法快速检测阿维菌素的试验研究

生物农药阿维菌素是一种微溶于水的高毒杀虫剂,有广谱、高效等特点,在防治水稻螟虫,稻纵卷叶螟方面表现优异,但对水生生物高毒。为了探讨利用光谱技术现场快速检测水体中生物农药阿维菌素的可能性,测定了其在紫外/可见光波长范围内的不同浓度吸光度光谱数据,建立其快速有效的定量分析模型。使用不同光程比色皿采集一定浓度范围的阿维菌素农药样本光谱数据进行对比,得到最佳光谱数据用于后续定量处理分析。将波长范围为200～500 nm的光谱数据采用Savitzky-Golay卷积平滑法（S-G平滑法）进行数据预处理,将原始光谱数据和S-G平滑法预处理后的光谱数据校正集和预测集分别按不同比例采用Sample set partitioning based on joint x-y distance(SPXY)算法进行样本集划分,并分别建立PLS模型进行比较。再将划分样本集后优选出的光谱数据采用主成分分析（Principle component analysis,PCA）结合马氏距离阈值法（Mahalanobis Distance,MD）,即PCAMD算法剔除异常样本,再将剔除异常样本后的光谱数据采用竞争性自适应重...     

强对流天气过程成因及系统结构特征分析

利用多普勒天气雷达资料、MM5模式资料对2005年4月20日发生在江苏境内的一次强对流天气过程进行了分析,讨论了这次强对流天气的成因,并结合多普勒天气雷达资料及模式资料,对强风暴系统的特征及其演变进行了较详细的描述,揭示出强风暴云体内部动力场的结构特征,为分析和预报强对流天气的发生发展提供了一些可参考的信息。     

An integrated framework for software to provide yield data cleaning and estimation of an opportunity index for site-specific crop management

This paper proposes an integrated framework for software that provides yield data cleaning and yield opportunity index (Y _i ) calculation for site-specific crop management (SSCM). The artifacts in many yield data sets, which inevitably occur, can pose a significant effect on the validity of Yi. Automated and standardised yield correction procedures were designed to improve the data quality by removing: (1) unreasonable outliers; (2) distribution outliers (globally and locally); and (3) position errors. The calculation of Yi uses two aspects of crop yield assessment, the magnitude of yield variation and the spatial structure of the variation. The cleaning algorithms were applied to four yield data sets with known integrity issues to demonstrate effectiveness. Approximately 13–20 % of the original yield data were removed, and this resulted in an increased mean yield of 0.13 t/ha (average). The semivariograms of cleaned data were shown to possess smaller nugget values compared with the original data. The opportunity index calculation algorithm was demonstrated on a field with nine seasons of yield data. The results demonstrated that using a ranking of Yi provides a rational, agronomic assessment of the opportunity for SSCM based on the quantity and pattern of production variability displayed in yield data sets. This provides farm managers with a rapid way to assess whether the observed variability deserves further investigation and eventual investment in SSCM operations.     

Selection of ancillary data to derive production management units in sweet corn (<Emphasis Type="Italic">Zea Mays</Emphasis> var. rugosa) using MANOVA and an information criterion

In production systems where high-resolution harvest data are unavailable there is often a reliance on ancillary information to generate potential management units. In these situations correct identification of relevant sources of data is important to minimize cost to the grower. For three fields in a sweet corn production system in central NSW, Australia, several sets of high-resolution data were obtained using soil and crop canopy sensors. Management units were derived by k-means classification for 2–5 classes using three approaches: (1) with soil data, (2) with crop data and (3) a combination of both soil and crop data. Crop quantity and quality were sampled manually, and the sample data were related to the different management units using multivariate analysis of variance (MANOVA). The corrected Akaike information criterion (AICc) was then used to rank the different sources of data and the different orders of management units. For irrigated, short-season sweet corn production the management units derived from the crop canopy sensor data explained more variation in key harvest variables than management units derived from an apparent soil electrical conductivity (EC_a) survey or a mixture of crop and soil sensor data. Management units derived from crop data recorded just prior to side-dressing outperformed management units derived from data recorded earlier in the season. However, multi-temporal classification of early and mid-season crop data gave better results than single layer classification at any time. For all three fields in this study, a 3- or 4-unit classification gave the best results according to the information criterion (AICc). For growers interested in adopting differential management in irrigated sweet corn, investment in a crop canopy sensor will provide more useful high-resolution information than that in a high-resolution EC_a survey.     

TM与SPOT5数据对农田防护林景观评价影响的对比分析

选择同一区域的TM与SPOT5数据,分别提取农田防护林与土地利用数据,分析不同空间分辨率数据对防护林数量及防护林景观评价指标的影响.结果表明,从数量值上来看,两者间林带长度的相对误差为7.5％,林带条数的相对误差为7.9％；从景观指标来看,带斑比基本不受数据源的影响,不同数据源的土地利用数据对连接度与优势度的影响较大,...     

土壤水分自动观测数据分析与订正

对山东莱阳气象局近3年获得的自动土壤水分观测数据（简称器测数据）和人工观测土壤湿度数据（简称人工数据）进行对比分析。运用逐步回归分析建立了以器测数据为因子的订正方程,并进行效果验证。结果表明,各层土壤湿度经订正后误差明显减小;各种订正方程的订正效果不同;不同的深度层订正效果不同,深层土壤湿度订正后达到满意的效果,上层土壤湿度订正后未获得满意效果。同时,分析了造成偏差的可能原因。     

Geostatistical Analyses of Soil Salinity in a Large Field

Estimating spatial variability of soil salinity is an important issue in precision agriculture. Geostatistical methods provide a means to study the heterogeneous nature of spatial distributions of soil salinity. In this study, geostatistical methods, kriging and cokriging, were applied to estimate sodium adsorption ratio (SAR) in a 3375 ha agricultural field. In cokriging, more easily measured data of electrical conductivity (EC) were incorporated to improve the estimation of SAR. The estimated spatial distributions of SAR using the geostatistical methods with various reduced data sets were compared with the extensive salinity measurements in the large field. The results suggest that sampling cost can be dramatically reduced and estimation can be significantly improved using cokriging. Compared with the kriging results using total SAR data, cokriging with reduced data sets of SAR improves the estimations greatly by reducing mean squared error and kriging variance up to 70% and increasing correlation of estimates and measurements about 60%. The sampling costs for SAR estimation can be reduced approximately by 80% using extensive EC data together with a small portion of SAR data in cokriging.     

基于关联数据融合的地表移动传感器节点

开发了具有多个相关联传感器的地表移动传感器节点;提出一种基于专家系统进行关联数据处理的几何方格面积数据融合方法,即主要采用软硬件结合的方式,利用专家系统预设的节点环境阈值与传感器实际感测的数据相结合进行关联数据处理,将数据关系转化为几何方格面积,从而实现分类分层次的数据融合.结果表明：该方法能够对多个不同类型传感器提供的相同特征数据、互补特征数据、并行特征数据、条件关联数据进行融合;与传统传感器节点相比,地表移动传感器节点感测信息的准确率更高,能量消耗更低.     

Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum

Remote sensing imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. The objective of this study was to evaluate the relationships between yield monitor data and airborne multidate multispectral digital imagery and to identify optimal time periods for image acquisition. Color-infrared (CIR) digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combine's effective cutting width. Correlation analyses showed that grain yield was significantly related to the digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne digital imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture.     

Relationship Between Spectral Data from an Aerial Image and Soil Organic Matter and Phosphorus Levels

Early ventures into site-specific management involved fertilizer management decisions based on soil chemical properties characterized by some form of grid sampling. This is both labor and capital intensive and practitioners quickly began investigating other methods to get a measure of spatial variability. Aerial photographs, which were mainly used to evaluate and assess crop status, allow for the collection of whole-field data at relatively low cost. Our objective is to determine what relationships exist between aerial spectral data and intensive grid soil test results and whether this information can be used to improve future soil sampling strategies. Soil-test organic matter (OM) and Bray-1 P concentrations were measured on soil samples taken using an alternating 12.2- by 24.4-m grid in late March 1994 from a quarter section under center pivot irrigation. Spectral data were collected in the spring of 1996 prior to planting using a multispectral network of digital cameras. Correlations of brightness values from the blue, green, and NIR bands with both OM and Bray-1 P were significant, but relatively low. Normality tests revealed that brightness values for the spectral data sets were generally evenly distributed while those of the soil test OM and Bray-1 P were positively skewed. Many of the very high soil-test data values were due to past management. When those values were removed from the database, greater correlations between spectral data and soil test data were obtained. These results substantiated that aerial imagery can be used to improve sampling strategies, but it must be used in conjunction with existing knowledge and past management histories.     

The use of near infrared (NIR) spectroscopy to improve soil mapping at the farm scale

The creation of fine resolution soil maps is hampered by the increasing costs associated with conventional laboratory analyses of soil. In this study, near infrared (NIR) reflectance spectroscopy was used to reduce the number of conventional soil analyses required by the use of calibration models at the farm scale. Soil electrical conductivity and mid infrared reflection (MIR) from a satellite image were used and compared as ancillary data to guide the targeting of soil sampling. About 150 targeted samples were taken over a 97 hectare farm (approximately 1.5 samples per hectare) for each type of ancillary data. A sub-set of 25 samples was selected from each of the targeted data sets (150 points) to measure clay and soil organic matter (SOM) contents for calibration with NIR. For the remaining 125 samples only their NIR-spectra needed to be determined. The NIR calibration models for both SOM and clay contents resulted in predictions with small errors. Maps derived from the calibrated data were compared with a map based on 0.5 samples per hectare representing a conventional farm-scale soil map. The maps derived from the NIR-calibrated data are promising, and the potential for developing a cost-effective strategy to map soil from NIR-calibrated data at the farm-scale is considerable.     

电力载波通信技术在温室数据采集系统上的应用

采用电力载波通信技术设计了温室数据采集系统,其硬件主要包括主通讯单片机系统电路、环境数据采集节点电路以及传感器测量电路.并根据数据采集的工作原理设计了相应的通讯协议及通讯算法.试验证明系统能实现多点环境数据的可靠采集和通讯,该系统为温室环境数据采集提供了一种可行的解决方案.     

Oestrus detection in dairy cows from activity and lying data using on-line individual models

Automated monitoring and detection of oestrus in dairy cows is attractive for reasons of economy in dairy farming. While high performance detection has been shown possible using high-priced progesterone measurements, detection results were less reliable when only low-cost sensor data were available. Aiming at improving detection scheme reliability with the use of low-cost sensor data, this study combines information from step count and leg tilt sensors. Introducing a lying balance for the individual animal, a novel change detection scheme is derived from observed distributions of the step count data and the lying balance. Detection and hypothesis testing are based on generalised likelihood ratio optimisation combined with time-wise joint probability windowing based on the duration of oestrus and oestrus intervals. It is shown to be essential that cow-specific parameters and test statistics are derived on-line from data to cope with behaviours of individuals. Performance is validated on 18 sequences of data where definite proof of prior oestrus was available in form of subsequent pregnancy. These data were extracted from data sequences from 44 dairy cows over an 8 months period. The results show sensitivity 88.9% and error rate 5.9.%, which is very satisfactory when only cheap sensor data are used.     

Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA

A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps, soil electrical conductivity (EC_a) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations. The soil EC_a sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful. Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps and soil EC_a are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted in zones with a stronger correlation with soil nitrate.