微型数字式pH/mV计

本文阐述了由于当今新型传感器及微型电子元件的出现,为测试 pH/mv 值的仪表,由台式大型化转向便携式微型化开拓了广阔的前景。同时介绍已经上海市标准计量管理局测试合格和已经专家鉴定:精度高达0.02～0.01pH;能使用不同型号的氢离子传感器进行测试的微型数字式 pH/mv 计的研制原理和有关图表。     

承载板式谷物质量流量传感器设计与试验

设计了不同材料、不同尺寸承载板式谷物质量流量传感器承载板和不同量程的承载板式谷物质量流量传感器弹性元件,在谷物质量流量模拟加载试验装置上进行了试验,试验结果表明测量精度与承载板和弹性元件的参数密切相关。在此试验基础上,研究了谷物流量对测量精度的影响。试验结果表明,谷物流量在0~2kg/s内谷物质量流量测量相对误差不大于2%,精度较高。当谷物流量超过2.5kg/s时,谷物质量流量测量相对误差超过5%。     

温室环境控制系统的发展及现存问题

概述了在温室环境控制系统的现状,详细阐述了各个方向的发展过程、现有传感器布置方式及发展趋势,指出现有的温室环境控制系统信息获取准确性的不足,提出需要优化布置温室内的传感器,使获得的信息能准确地反映温室内的状况;需要综合利用来自多传感器的信息,提高测控系统的性能,提高温室环境的控制效果和减少调控所需的能源消耗的问题,提出在设计温室环境控制系统时,应考虑传感器的布置方式和数据处理的方法。     

基于SM2801V的土壤水分测试仪设计

[目的]设计了基于SM2801V的土壤水分测定仪.[方法]在AT89C52的控制下,传感器实时地采集土壤的温度及水分含量信息,然后经数据的模数转换,送微处理器进行具有温度补偿功能的信号处理,实现对土壤水分信息的存储、显示及与蓝牙的数据传输功能.[结果]野外的试验测量表明,仪器采用温度补偿,提高了测量精度,具有成本低、操作简单、性价比高、数据传输方便等优点.[结论]该仪器在农业气象和水利部门中的应用前景广泛.     

种子清选机喂入量的动态检测与控制系统设计

为提高种子清选机的的工作效率、清选性能和自动化水平,设计了一套喂入量动态检测与控制系统。介绍了种子清选机喂入量的动态检测与控制系统的工作原理,给出了硬件框图,阐述了系统的检测与控制原理,并着重讨论了系统硬件及软件的实现方法。该设计可以为以后农业机械上喂入量的精确测试提供一种可靠的方法。     

传感器技术在农产品无损检测中的应用研究

无损检测技术是有别于传统检测手段的一种新兴技术,近年来在工业和农业方面的应用研究得到了广泛的关注。针对传感器技术在农产品无损检测领域的研究,着重论述了视觉、嗅觉、味觉传感器技术以及生物传感器技术等多种无损检测技术在不同农产品检测中的基本原理、应用现状和最新研究进展,并对传感器无损检测技术的发展方向和应用前景进行了探讨。     

基于机器视觉与敲击振动融合的鸭蛋孵化特性检测

为提高判别种蛋孵化前期受精的准确性和稳定性,将视觉和声学2种传感器信息在孵化第5天进行特征层融合,采用2种人工神经网络构建种蛋孵化前期受精性判断的融合模型。研究表明:采用LVQ神经网络判别模型的准确率和稳定性,优于BP神经网络。单独利用计算机视觉技术和敲击振动技术对鸭蛋孵化早期受精情况的判别准确率为92%和88%,而将2种传感器信息进行融合构建的模型的准确率可达98%,说明传感器信息融合技术在判断鸭蛋孵化前期受精性方面是可行的。     

Sensor measurements revealed: Predicting the Gram-status of clinical mastitis causal pathogens

Automatic milking systems produce mastitis alert lists that report cows likely to have clinical mastitis (CM). A farmer has to check these listed cows to confirm a CM case and to start an antimicrobial treatment if necessary. In order to make a more informed decision, it would be beneficial to have information about the CM causal pathogen at the same time a cow is listed on the mastitis alert list. Therefore, this study explored whether decision-tree induction was able to predict the Gram-status of CM causal pathogens using in-line sensor measurements from automatic milking systems. Data were collected at nine Dutch dairy farms milking with automatic milking systems and included 140 bacteriological cultured CM cases with sensor measurements of electrical conductivity, colors red, green, and blue and milk yield for analyses. In total, 110 CM cases were classified as Gram-positive CM cases and 30 as Gram-negative. Stratified randomization was used to divide the data in a training set (n = 96) for model development, and a test set (n = 44) for validation. The decision tree used three variables to predict the Gram-status of the CM causal pathogen; two variables were based on electrical conductivity measurements, and one on measurements of the color blue. This decision tree had an accuracy of 90.6% and a kappa value of 0.76 based on data in the training set. When only those CM cases were considered with extreme high probability estimates for their Gram-status (either positive or negative), 74% of all records in the training set could be classified with a stratified accuracy of 97.1%. When validated, the decision tree performed poorly; accuracy dropped to 54.5% and the kappa value to −0.20. The stratified accuracy calculated for 75% of all records in the test set was 66.7%. Predicting the CM causal pathogen showed a similar poor result; the decision tree had an accuracy of 27.9% and a kappa of 0.12, based on data in the test set. Based on these results, it is concluded that decision-tree induction in conjunction with sensor information from the electrical conductivity, color, and milk yield provide insufficient discriminative power to predict the Gram-status or the CM causal pathogen itself.     

传感器原理及其在农业方面的应用

这些年来传感器在各个领域中的作用日益显著,农业方面也不会例外。主要阐述了传感器特性、分类以及常见的传感器,并论述了传感器技术在国内农业方面的研究现状。随着农业信息化的发展,传感器在农业方面的应用将会越来越受到人们的重视。     

基于WSN的坝上温室光照度控制系统设计

结合坝上气候特点及温室光照自动控制需求,设计了作物光照强度控制系统。首先构建温室环境模型,以自然光与人工补光进行温室光照建模,设计温室光照自动控制流程。在此基础上通过无线互联网和ZigBee设计并实现了温室光照度自动控制系统,阐述了系统构架设计、温度感知单元、数据传输单元、控制执行单元、监控后台单元、可调光源单元的设计,引入CodeWarriorIDE开发环境进行控制软件设计,最后通过测试证明其促进作物生长的效果。