基于土壤湿度控制的灌溉控制器设计【物联网开题报告外文翻译说明书论文】.zip
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毕 业 设 计（论 文）任 务 书1本毕业设计（论文）课题应达到的目的： 培养和增强学生以下能力与技能：(1)单片机软硬件知识基础与实践能力；(2)电路设计及分析能力；(3)论文撰写能力；(4)学习能力与研究精神2本毕业设计（论文）课题任务的内容和要求（包括原始数据、技术要求、工作要求等）： 在水资源日益匮乏的今天，科学地进行农业灌溉将有利于节约用水。本课题的研究采用土壤湿度为控制对象，实现自动农业灌溉，防止过灌或欠灌的现象产生。通过单片机进行土壤湿度传感器的信号采集，并以土壤湿度为控制对象来进行控制算法的编写。通过“自学习”功能，对灌溉控制器进行参数设置，并在灌溉过程中进行自动配置与控制优化。要求学生进行系统设计，实验平台搭建，并进行毕业论文撰写。毕 业 设 计（论 文）任 务 书3对本毕业设计（论文）课题成果的要求包括图表、实物等硬件要求： (1) 毕业论文；(2) 可进行系统演示的实体；4主要参考文献： 1 蒋志峰, 徐佩锋, 姚士宇, 苏青峰, 王渲哲, 王永. 城市广场草坪智能型非充分渗灌技术J. 节水灌溉, 2013(7): 76-78.2 于红军. 非充分灌溉研究现状及展望J. 新疆水利, 2009, (2):31-35.3 迟天阳, 杨方, 果莉. 节水灌溉中土壤湿度传感器的应用J. 东北农业大学学报, 2006, 37(1): 135-137.4 戴欣平. 土壤湿度检测与PLC技术在大棚节水灌溉中的应用J. 浙江农业科学, 2011, (6): 1248-1251.5 蒋志峰, 任立涛, 姚士宇. 渗灌技术在坡地草坪上的应用J. 节水灌溉, 2011, (3): 54-56.6 平毅, 郭磊. 低功耗自动灌溉控制器设计J. 现代电子技术, 2014.5, 37(10): 104-106.7 王友贞. 节水灌溉与农业可持续发展M. 北京: 中国机械出版社, 2005.8 王辉, 王圣伟, 王志强, 张丽娇. 基于IoT与WiFi的温室移动智能控制系统J. 农机化研究, 2014.4(4): 187-190.9 李锡文, 杨明金, 杨仁全. 现代温室环境智能控制的发展现状及展望J. 农机化研究, 2008(4): 9-13.10 朱光忠, 陈建明. 基于土壤湿度检测的滴灌控制器设计与研究J. 工业控制计算机, 2014, 27(12): 140-141.11 姚国祥, 章焕庆. 一种用于控制双稳态电磁阀的驱动电路P. 中国: 2011-20569326, 2012.08.15.12 施山菁, 封维忠,韩展燕,申斌. 基于无线传感器网络的土壤湿度监控系统J. 测控技术, 2013, 32(12): 118-121.13 郑闪,张晓凌.基于物联网技术的精细农业信息服务平台的研究J.电脑与信息技术, 2012,20(2):50-55.14 马正华，宋 磊，焦竹青，陈岚萍. 基于无线传感网的蔬菜工厂智能监控系统设计J. 自动化与仪表, 2013(11): 20-24.15 刘艳秋，武佩，张永安，于奎单，苏赫. 农田地滴灌管堵塞快速检测装置的设计J. 农机化研究16 陈燕鹏, 刘祖明, 杨康, 许海园. 一种智能灌溉控制器的研究与设计J. 安徽农业科学, 2015, 43(20): 359-361,382.17 刘永鑫, 洪添胜, 岳学军. 太阳能低功耗滴灌控制装置的设计与实现J. 农业工程学报, 2012, 28(20): 20-26.18 冯丽媛, 姚绪梁. 温室大棚自动灌溉系统设计J. 农机化研究, 2013, 35(6): 113-115.19 岳学军, 刘永鑫, 洪添胜. 基于土壤墒情的自动灌溉控制系统设计与试验J. 农业机械学报, 2013, 44(增刊): 241-250. 毕 业 设 计（论 文）任 务 书5本毕业设计（论文）课题工作进度计划：2015.11.102015.12.13 调研、收集相关资料、对学生进行初步辅导，拟题、选题、填写任务书；2015.12.152015.12.31 学生查看任务书，为毕业设计的顺利完成，进行前期准备。12月31日前正式下发任务书；2016.01.092016.04.05 学生在指导教师的具体指导下进行毕业设计创作；拟定论文提纲或设计说明书（下称文档）提纲；撰写及提交开题报告、外文参考资料及译文、论文大纲；在2016年4月5日前学生要提交基本完成的毕业设计创作成果以及文档的撰写提纲，作为中期检查的依据。指导教师指导、审阅，定稿由指导教师给出评语，对论文主要工作未通过的学生下发整改通知；2016.04.062016.04.10 提交中期课题完成情况报告给指导教师审阅；各专业组织中期检查（含毕业设计成果验收检查）；2016.04.112016.05.10 进行毕业设计文档撰写；2016年5月8日为学生毕业设计文档定稿截止日；2016年5月9日-13日，指导教师和评阅教师通过毕业设计（论文）管理系统对学生的毕业设计以及文档进行评阅，包括打分和评语。5月1日前，做好答辩安排，通知学生回校进行答辨；2016.05.142016.05.15 查看答辩安排，毕业设计（论文）小组答辩；2016.05.162016.05.29 对未通过答辨的学生进行二次答辨，完成毕业设计的成绩录入；2016.05.302016.06.07 根据答辩情况修改毕业设计（论文）的相关材料，并在毕业设计（论文）管理系统中上传最终稿，并且上交纸质稿。2016年6月7日为学生毕业设计文档最终稿提交截止日；2016.06.072016.06.30 各系提交本届毕业设计（论文）的工作书面总结及相关材料。所在专业审查意见：通过负责人： 2015 年 12 月17 日 毕 业 设 计（论文） 开 题 报 告 1结合毕业设计（论文）课题情况，根据所查阅的文献资料，每人撰写不少于1000字左右的文献综述： 本课题研究的题目是基于土壤湿度控制的灌溉控制器设计。本课题的研究采用土壤湿度为控制对象，实现自动农业灌溉，防止过灌或欠灌的现象产生。通过单片机进行土壤湿度传感器的信号采集，并以土壤湿度为控制对象来进行控制算法的编写。通过“自学习”功能，对灌溉控制器进行参数设置，并在灌溉过程中进行自动配置与控制优化。我国是农业大国，农作物的优质髙产对国家的经济发展意义重大。为了提高农作物的产量必须使农作物持续生长在适宜的环境中。现代农业及生态研究中，快速、准确地测定农田的土壤湿度对于发展精细农业具有重要意义。传统的土壤湿度监控系统造价昂贵，通常要在大片农作物生长区域内布线而且布线后就很难再根据新需求灵活改变布局。而采用无线传感器网络就可以克服上述缺点。由于无线传感器网络拓扑结构易变化具有自我组织能力数据采集点可以广泛地分布在不同区域可以灵活改变监控布局，从而使人们在任何时间、地点和环境条件下可以获取大量详实而可靠的信息非常适合于精细农业对于土壤湿度测量的要求。在设计时，应综合考虑系统的通信距离、载波频率、功率消耗、开发难易和模块成本几方面因素。当前灌溉模式不当，水资源利用率不高，以喷灌模式为主，还有软管式人工浇灌、洒水车浇灌、漫灌、喷灌机喷灌等。灌溉特点体现智能化程度低、喷洒漂移、灌溉不均匀、灌溉量不当，致使地表积水、渗漏或干旱，致使水分损失多达，影响草坪生长，也导致土壤板结，水源浪费。实施土壤水分检测后，可以实时监测土壤水分含量，节约水资源的利用，避免水资源的浪费，将数据反馈给计算机后还能实施自动灌溉，既节约水资源又省力。节水灌溉系统的设计思路是以单片机控制为核心，通过编程和调试，利用温度传感器识别湿度，并通过不同的湿度改变灌溉的水量。灌溉系统采用的单片机有STC89C52，STC90C58AD，AT89C52，CC430系列单片机，C8051F020单片机，集成了AD转换器的单片机，增强型51单片机等。STC89C52单片机：利用湿度传感器采集土壤中的离子，所得离子正负结合形成电流，收集到的电流信号经处理可获得可用的电压信号，并经 A/D 转换器 ADC0809 转化成数字信号，发送给单片机 STC89C52，再由单片机处理此信号，系统将得到土壤的湿度值，并由 LCD 液晶显示器显示，同时，发送指令给驱动，经驱动放大下达至控制端控制灌溉水量，从而达到节水灌溉的效果。基于单片机的节水灌溉系统设计_陈公兴AT89C52单片机对土壤湿度参数进行检测，将土壤湿度传感器检测的结果送人转换电路，进行处理后，输入单片机，将监测到的湿度值进行显示，数据处理过程中采用模糊控制算法，输出控制信号对现场土壤湿度进行实时控制，能在土壤湿度异常情况实现报警等功能。基于单片机的农业智能节水灌溉系统设计_翟红我所采用的方案是：由无线传感器节点作为前端测量单元，进行土壤湿度的信号采集，然后通过无线网络将数据传送至主控节点，主控节点再将数据传送至数据监控中心，从而实施远程监控功能。监控中心收到数据后进行判断，从而实施农田的灌溉，决定农田灌溉量的多少。1 蒋志峰, 徐佩锋, 姚士宇, 苏青峰, 王渲哲, 王永. 城市广场草坪智能型非充分渗灌技术J. 节水灌溉, 2013(7): 76-78.2 于红军. 非充分灌溉研究现状及展望J. 新疆水利, 2009, (2):31-35.3 迟天阳, 杨方, 果莉. 节水灌溉中土壤湿度传感器的应用J. 东北农业大学学报, 2006, 37(1): 135-137.4 戴欣平. 土壤湿度检测与PLC技术在大棚节水灌溉中的应用J. 浙江农业科学, 2011, (6): 1248-1251.5 蒋志峰, 任立涛, 姚士宇. 渗灌技术在坡地草坪上的应用J. 节水灌溉, 2011, (3): 54-56.6 平毅, 郭磊. 低功耗自动灌溉控制器设计J. 现代电子技术, 2014.5, 37(10): 104-106.7 王友贞. 节水灌溉与农业可持续发展M. 北京: 中国机械出版社, 2005.8 王辉, 王圣伟, 王志强, 张丽娇. 基于IoT与WiFi的温室移动智能控制系统J. 农机化研究, 2014.4(4): 187-190.9 李锡文, 杨明金, 杨仁全. 现代温室环境智能控制的发展现状及展望J. 农机化研究, 2008(4): 9-13.10 朱光忠, 陈建明. 基于土壤湿度检测的滴灌控制器设计与研究J. 工业控制计算机, 2014, 27(12): 140-141.11 姚国祥, 章焕庆. 一种用于控制双稳态电磁阀的驱动电路P. 中国: 2011-20569326, 2012.08.15.12 施山菁, 封维忠,韩展燕,申斌. 基于无线传感器网络的土壤湿度监控系统J. 测控技术, 2013, 32(12): 118-121.13 郑闪,张晓凌.基于物联网技术的精细农业信息服务平台的研究J.电脑与信息技术, 2012,20(2):50-55.14 马正华，宋 磊，焦竹青，陈岚萍. 基于无线传感网的蔬菜工厂智能监控系统设计J. 自动化与仪表, 2013(11): 20-24.15 刘艳秋，武佩，张永安，于奎单，苏赫. 农田地滴灌管堵塞快速检测装置的设计J. 农机化研究16 陈燕鹏, 刘祖明, 杨康, 许海园. 一种智能灌溉控制器的研究与设计J. 安徽农业科学, 2015, 43(20): 359-361,382.17 刘永鑫, 洪添胜, 岳学军. 太阳能低功耗滴灌控制装置的设计与实现J. 农业工程学报, 2012, 28(20): 20-26.18 冯丽媛, 姚绪梁. 温室大棚自动灌溉系统设计J. 农机化研究, 2013, 35(6): 113-115.19 岳学军, 刘永鑫, 洪添胜. 基于土壤墒情的自动灌溉控制系统设计与试验J. 农业机械学报, 2013, 44(增刊): 241-250.毕 业 设 计（论文） 开 题 报 告 2本课题要研究或解决的问题和拟采用的研究手段（途径）： 1、本课题要解决的问题（1）了解如何PIC单片机的使用（2）分析光伏电池的基本原理和特性，理解最大功率点的存在（3）分析光伏发电的制约条件与其发展方向（4）分析铅酸蓄电池充电和放电的控制方法和特性2、设计途径（1）从书上找到型号为PIC的单片机，了解和熟悉PIC单片机（2）对太阳能光伏电池的基本原理和特性进行分析寻找最大功率点（3）通过网站和书本了解光伏发电的制约条件和发展方向并且寻找铅酸蓄电池充电和放电的控制方法和特性毕 业 设 计（论文） 开 题 报 告 指导教师意见：1对“文献综述”的评语：对课题的应用背景、意义以及存在的问题进行论述说明；对课题所应用的相关技术进行了论述。提出了课题的基本方案。基本合格。 2对本课题的深度、广度及工作量的意见和对设计（论文）结果的预测：课题有一定的应用广度，研究深度适中，工作量适中。预计成果：可演示的硬件，以及毕业论文。3.是否同意开题： 同意 不同意 指导教师： 2015 年 12 月 30 日所在专业审查意见：同意 负责人： 2016 年 04 月 22 日原 文RAPID CHARGE SYSTEM FOR LEAD-ACID BATTERY OF SOLAR ENERGY STREET LIGHT BASED ON SINGLE-CHIP MICROCOMPUTERG. Vellidis1, M. Tucker1, C. Perry1, D. Reckford2, C. Butts3, H. Henry1, V. Liakos1, R.W. Hill1 and W. Edwards31University of Georgia, USA; yiorgos2Flint River Soil and Water Conservation District, USA3United States Department of Agriculture, Agricultural Research Service, USAAbstract: To assess the potential of precision irrigation, a research and demonstration project whose goal is to develop a soil moisture sensor-based variable rate irrigation (VRI) control system was begun. The control system consists of a wireless soil moisture sensing array with a high density of sensor nodes, a VRI enabled center pivot irrigation system, and a web-based user interface with an integrated irrigation scheduling decision support system. This paper describes the system in detail providing some results from the components which have been completed and are operational and a detailed description of the components under development.Keywords: decision support systems, Watermark, mesh networksIntroductionIrrigation has become essential to crop production in many agricultural areas of the United States. As a result, the competition for available fresh water supplies is increasing. If irrigated agriculture is to survive in this competitive environment, irrigation water must be used efficiently. A large number of techniques and tools have been developed to assist irrigation system users (irrigators), and especially producers, to estimate when and how much water to apply to crops. Yet, despite the availability of these techniques and tools, the vast majority of irrigators still rely either on a fixed schedule or on visual cues of plant stress such as wilting. Typically, irrigators will apply a standard amount (for example 2.5 cm) at each irrigation event. As a result, both the timing and depths of irrigation may be inappropriate and may lead to yield, nutrient, and soil losses. Vories et al. (2006) found that improper timing of irrigation on cotton can result in yield losses of between USD 370/ ha to USD 1,850/ha.Cheap, reliable, and wireless soil moisture sensing systems with a high density of sensor nodes are needed to account for soil variability and enable precision irrigation. To address this issue, a research and demonstration project whose goal is to develop a soil moisture sensor-based variable rate irrigation (VRI) control system. The control system consists of a wireless soil moisture sensing array with a high density of sensor nodes, a VRI-enabled center pivot irrigation system, and a web-based user interface with an integrated irrigation scheduling decision support system. This paper describes the system in detail.MethodsThe operational paradigm for the system is that the field is divided into irrigation management zones (MZs), the soil moisture sensing array is installed to monitor soil condition within the zones and provides hourly soil moisture measurements to the web-based user interface. At the interface, the soil moisture data are used by an irrigation scheduling model running in the background to develop irrigation scheduling recommendations by MZ. The recommendations are then approved by the user (farmer) and downloaded to the VRI controller on the center pivot as a precision irrigation prescription. When the center pivot irrigation system is engaged by the farmer, the pivot applies the recommended rates.The University of Georgia smart sensor arrayThe University of Georgia smart sensor array (UGA SSA) consists of smart sensor nodes and a gateway. A smart sensor node is defined as the combination of electronics and sensors installed at each location in the field. A UGA SSA node consists of a circuit board, a radio frequency (RF) transmitter, soil moisture sensors and temperature sensors. Each sensor node accommodates up to 3 Watermark soil moisture sensors and 2 thermocouples for measuring temperature (Figure 1a). The RF transmitter (RF200P81, Synapse, Huntsville, Alabama, USA) is a postage stamp-sized intelligent low-cost, low-power, 2.4 GHz radio module capable of acquiring, analyzing and transmitting sensor data (Figure 1b). Data from all the nodes are routed to a centrally located node known as the gateway at 5 minute intervals. At the gateway, data are stored on a solar-powered net-book computer (Figure 1c) and transmitted via cellular modem to an FTP server hourly.One unique characteristic of the UGA SSA is that it uses wireless mesh networks to communicate between irrigation sensor nodes. As the name implies, mesh networks create a wireless network between the nodes. The RF transmitters act as a repeater to pass along data from other nodes to form a meshed network of nodes. If any of the nodes in the network stop transmitting or receiving or if signal pathways become blocked, the operating software re-configures signal routes in order to Figure 1. A UGA SSA sensor node consists of the sensors which are installed in the soil (a) and the electronic components (b). The three Watermark sensors are integrated into a shaft (a) which can be easily installed after planting and extracted prior to harvest for agronomic crops. The photo at top right shows a sensor node circuit board pulled part-way out of its PVC enclosure. The node circuitry is powered by two alkaline AA batteries mounted to the back side of the sensor acquisition board. The photo at bottom right shows the gateway for UGA-SSA system. The enclosure houses the net-book computer. The solar panel above recharges a 12VDC battery,maintain data acquisition from the network. To overcome the attenuating effect of the plant canopy on radio transmissions, the RF transmitter antenna is mounted on spring-loaded, hollow, 6 mm diameter, flexible fiberglass rods approximately 2.5 m above ground level (Figure 2). This design allows field equipment such as tractors and sprayers to pass over the sensors something which no other wireless system offers. The published range of the RF transmitter is 500 m although its effective range has been observed to exceed 750 m.Figure 1An important characteristic of our system is its affordable cost a 12-node system can be installed for a onetime cost of USD 5200. Installing irrigation sensors throughout an irrigated field is critical to understanding and managing the soil moisture variability which exists in all fields. Another important characteristic of the UGA SSA is that it reports soil moisture condition in terms of soil water potential (soil water tension) in units of kPa. This allows the system to be installed in any soil type without calibration. This is in contrast to capacitance-type soil moisture sensors which require calibration. Although Watermarks respond more slowly to soil moisture changes than capacitance sensors, their response time is adequate for scheduling irrigation in agronomic crops.The UGA SSA has been tested in its current configuration under field conditions for two entire cropping seasons. During the 2012 cropping season, the UGA SSA was deployed in eight of ten demonstration fields with a maximum of ten sensor nodes per field. Field size averaged 80 ha. The fields were delineated into irrigation MZs based on soil survey maps, topography, aerial photographs and visual inspection of the fields. At least one node was installed in each MZ. In subsequent years, the MZ boundaries will be refined by using apparent soil electrically conductivity maps, yield maps and additional information as it becomes available. The number of sensor nodes will be adjusted accordingly with the goal of installing at least three nodes in each MZ.The eight instrumented fields which were located in the Lower Flint River Basin (LFRB) of southwest Georgia, USA, were planted to cotton or peanuts. Soil moisture data were collected hourly for the entire growing season and streamed to an FTP server where they were stored. All ten of the fields were equipped with VRI center pivots.Figure 2Figure 2. To increase transmittance range and to allow for farm vehicles to pass over the node, the electronics are kept at the soil surface and the antenna enclosed in a 2.5 m, 60 mm diameter hollow, flexible, spring-loaded fiberglass rod.Variable rate irrigation for center pivotsFields everywhere contain variability in soil type and texture, moisture holding capacity, and slope. In addition, many fields have irregular shapes and may contain non-farmed areas, waterways, small wetlands, and other features which should not be irrigated (Figure 3). Conventional center pivot irrigation systems apply the same rate of water along the entire length of the pivot and cannot account for these features.VRI is a patented and commercialized technology developed by the University of Georgia (UGA) in partnership with FarmScan (Perth, Australia), an Australian electronics company (Perry et al., 2002; Perry and Pocknee, 2003). Advanced Ag Systems (Dothan, AL, USA ), co-ordinates sales, installation and service of the FarmScan VRI system and has installed 47 systems in LFRB. Most of the large pivot suppliers began offering VRI systems of their own during 2012.The UGA/FarmScan VRI allows center pivots to vary water application rates along the length of the pivot by using electronic controls to cycle sprinklers and control pivot speed. Sprinklers are grouped together in banks of 3 to 10 depending on the level of resolution desired by the farmer. Each group or bank of sprinklers represents a grid with a 2 to 10 degree arc in which the irrigation water application rate can be set as percentage of the normal application rate ranging from 0% to 200% (Figure 3). The number of degrees in the arc is determined by the level of resolution desired. A detailed description of VRI is available at /vri.html.A 50% application rate is half the normal rate and is achieved by cycling the sprinklers on and off every 30 seconds. A 150% application rate is achieved by leaving the sprinklers on continuously while decreasing the travel speed of the pivot by 50%. If other grids along the length of the pivot require lower application rates, the VRI controller adjusts the sprinkler cycling pattern within those grids accordingly. An irrigation application map for each field is developed jointly by the farmer and VRI dealer on desktop software (Figure 3) and then downloaded to the VRI controller on the pivot. A detailed description of VRI is available at /vri.html. The UGA/FarmScan VRI system can be installed retroactively on most existing pivots. Installations costs range from about USD 5,500 for a limited installation on a small pivot to USD 18,000 for full installation on a large pivot. Costs vary depending on the length of the pivot and the level of resolution desired by the farmer to address the variability of the field. Water savings from VRI range from 7.5% to 19% of conventional application rates (Perry, unpublished data).Figure 3Irrigation application rates assigned to different areas under a 48 ha center pivot irrigation system (left) and variable rate irrigation implementation of the application map (right).The Flint Irrigation Scheduling ToolWe are developing a web-based irrigation scheduling tool called the Flint Irrigation Scheduling Tool (FIST) which will allow farmers to remotely check soil moisture of fields but will also provide irrigation scheduling recommendations. With FIST, farmers will be able to check soil moisture status from any device with access to the internet (mobile phone, iPod, office or home computer, internet caf). If multiple fields are equipped with the UGA SSA, farmers will be able to check soil moisture status of all fields form one portal. FIST will provide irrigation scheduling recommendations for conventional irrigation systems as well as precision irrigation systems. For precision irrigation systems, FIST will provide MZ-based recommendations (Figure 4, top). Farmers could then make a fully-informed decision about initiating irrigation.FIST collects data from the field by dialing up the cell modem of the UGA SSA gateway computers at hourly intervals, downloads the data stored by the gateways, and then incorporates the data into a database. The FIST website makes this information available to users through a dashboard-style display (Figure 4, bottom).Figure 4.The Irrigator Pro suite of models will be used to develop irrigation scheduling recommendations.Irrigator Pro is a decision support system developed by the United States Department of Agriculture Agricultural Research Service National Peanut Research Lab for irrigation scheduling in peanuts,cotton, and corn. The models are public domain and available online (/ Research/docs.htm?docid=16805).Irrigator Pro irrigation scheduling recommendations are made to maintain soil water in the optimum ranges. The algorithms used to make these recommendations are based on 25 years of irrigation research. The most recent release of Irrigator Pro uses daily soil water potential (soil water tension) data in units of kPa.data. Because the UGA SSA also measures soil water tension, it is ideally suited to provide data that drive Irrigator Pro.As a deliverable for this project, we are integrating the Irrigator Pro peanut, cotton, and corn models into FIST and will use Irrigator Pro to drive irrigation scheduling decisions. Data from the UGA SSA will stream hourly to the FIST database and will be drawn from there by Irrigator Proand used to develop daily irrigation scheduling recommendations for the projects demonstration fields. High resolution precipitation forecasts will be incorporated into the irrigation scheduling recommendations providing farmers with additional irrigation management options.Fields equipped with VRI-enabled pivots will be divided into a maximum of five irrigation MZs. Depending on size, each zone will be equipped with three or more UGA SSA sensor nodes. Multiple nodes within each zone will provide some redundancy in case of failures but will primarily be used to account for within-zone soil variability. The data from the nodes within each MZ will be used to provide summary statistics (max, min, average) of soil water tension to drive Irrigator Pro.Irrigator Pro will use the UGA SSA data from each MZ to develop irrigation schedulingrecommendations specific to each zone. FIST will in turn create irrigation prescription maps forthe VRI controllers. Participating farmers will then be able to download these maps to their pivotsand irrigate using the Irrigator Pro-generated irrigation recommendations. Fields equipped withconventional irrigation systems will be considered to have one irrigation management zone. For these fields, Irrigator Pro will develop one daily irrigation scheduling recommendation for the entire field.The daily irrigation scheduling recommendations will be available to users through traditional internet access and smartphones. Coupling a state-of-the-art remote soil moisture sensing system with a highly sophisticated irrigation scheduling model and high resolution precipitation data and providing farmers with unparalleled access to irrigation scheduling information in real time is a unique contribution.ResultsThe UGA SSA performed well during the 2012 growing season. A total of 75 sensor nodes were installed. Each node contained 3 Watermark soil moisture sensors and two thermocouples for a total of 375 individual sensors. Of these, only one individual soil moisture sensor did not perform properly. This particular sensor never responded to soil water moisture changes showing dry conditions continuously.The soil water tension graphs created from the UGA SSA data collected during the 2012 growing season show very different soil water moisture conditions within fields again confirming that there is tremendous in-field variability. During 2012, the demonstration fields were irrigated using conventional practices and the data clearly show that VRI can improve irrigation efficiency. ConclusionsField studies in many parts of the world have shown that water is often the limiting factor in crop production. Increasing competition for water resources will likely result in less water available for agricultural production. Precision irrigation promises to optimize the use of this precious resource.The project described here is the first attempt to implement precision irrigation in large-scale agronomic crop production. The study will provide valuable information for future implementation Efforts.AcknowledgementsThe authors wish to acknowledge the Flint River Soil and Water Conservation District for promoting state-of-the-art conservation practices in the LFRB and for its farmer members who provided their time, fields, and knowledge to make this project a success.This work was funded by a grant from the United States Department of Agriculture Natural Resources Conservation Service Conservation Innovation Grants (CIG) Program titled Irrigation Automation to Improve the Efficiency of Water Resource Management in Row Crop Production in the Lower Flint River Basin of Georgia.ReferencesPerry, C., Pocknee, S., Hansen, O., Kvien, C., Vellidis, G. and Hart, E. 2002. Development and Testing of a Variable Rate Irrigation Control System. ASAE Paper No. 02-2290. St. Joseph, MI, USA: ASAE. Perry, C. and Pocknee, S. 2003. Development of a Variable-Rate Pivot Irrigation Control System. In: (Ed.) K J Hatcher, Proceedings of the 2003 Georgia Water Resources Conference at the University of Georgia, April 23-24.Vories, E.D., Teague, T., Greene, J., Stewart, J., Clawson, E., Pringle, L., Phipps, B. 2006. Determining the optimum timing for the final irrigation on mid-south cotton. In: Proceedings National Cotton Council Beltwide Cotton Conference, January 3-6, San Antonio, Texas. 516-521: CDROM.译 文土壤湿度传感器为基础的可变速率灌溉调度系统G. Vellidis1, M. Tucker1, C. Perry1, D. Reckford2, C. Butts3, H. Henry1, V. Liakos1, R.W. Hill1 and W. Edwards31.美国佐治亚大学; yiorgos2.弗林特河水土保持区，美国3.美国农业部农业研究服务中心，美国摘要:为了评估精确灌溉的潜力（一项研究和示范项目），其目标是开发一种以土壤水分传感器为基础的可变速率灌溉（VRI）控制系统。该控制系统由一个无线土壤湿度感应阵列与传感器节点的高密度的，一个VRI启用中心枢轴灌溉系统，以及具有集成灌溉调度决策支持系统基于web的用户界面组成。本文介绍的系统详细提供了一定的成果，包括已完成的，可操作的组件和正在开发中的组件。关键词：决策支持系统，灌溉，网状网络介绍在美国的许多农业领域中灌溉已经成为至关重要的作物生产方式。其结果是，可用淡水供应量的需求正在急速增加。如果灌溉农业要在这个竞争激烈的环境中生存，那么灌溉用水必须得到有效利用。现如今大量的技术和工具已经被开发，以协助灌溉系统的用户，尤其是生产者，来判断何时以及要用多少水灌溉给作物。然而，尽管这些技术和工具的具有可用性，但绝大多数灌溉仍然没有一个固定的时间表，仍然依靠对植物的视觉观察，如萎蔫，来决定何时灌溉。通常情况下，在每次灌溉事件中灌水量都有相应的应用标准量（例如2.5厘米）。但其结果是，无论是时间和深度灌溉都有可能是不合适的，并可能会导致产率，养分和土壤损失。Vories等人（2006）发现，灌溉对棉花时机不当可能导致的产量损失为370美元/公顷到1,850美元/公顷之间。因此，需要廉价、可靠和高密度传感器节点的无线土壤湿度传感系统来检测土壤变异，使灌溉更精确。为了解决这个问题，一种以土壤水分传感器为基础的可变速率灌溉（VRI）控制系统正在开发。该控制系统由一个具有高密度的传感器节点的无线土壤水分传感器阵列，一个有VRI功能的中心枢纽灌溉系统，还有基于Web用户界面集成的灌溉决策支持系统组成。本文详细介绍了该系统。方法基于web的用户界面的该系统的运行模式是，场分割为灌溉管理区（MZS），土壤湿度传感阵列安装到监视区域内的监视土壤状况，并提供每小时土壤湿度测量。在接口方面，土壤湿度数据所使用的灌溉调度模型运行在后台MZ制定灌溉制度提出的建议。这些建议，然后由用户（农民）批准，并下载到VRI控制器中心作为精准灌溉方法。然后中心枢轴灌溉系统发送给农民，农民再应用枢轴建议的速率。乔治亚大学智能传感器阵列乔治亚大学智能传感器阵列由智能传感器节点和网关组成。“智能传感节点”被定义为安装在现场的每个位置电子元件和传感器的结合。UGA SSA节点包括一电路板，一射频（RF）发射器，一土壤湿度传感器和一温度传感器。每个传感器节点容纳3个土壤湿度传感器和2热电偶来测量温度（图1a）。RF发射器（RF200P81，突触，亚拉巴马州亨茨维尔，美国）是一个邮票大小的智能低成本，低功耗，2.4 GHz无线电能够获取，分析和传输传感器数据的模块（图1b）。数据以5分钟的间隔从所有的节点被路由到一个位于中心的节点被称为网关。在网关，数据存储在一个太阳能网图书计算机内（图1c），并通过蜂窝调制解调器每小时发送到FTP服务器。UGA SSA的一个独特的特点是，它是采用无线网状网灌溉传感器节点之间的通信。顾名思义，网状网创建了节点之间的无线网络。该RF发射机充当中继器一起传递从其它节点传来的的数据，从而形成节点间的网状网络。如果任何在网络站发射或接收的节点，或者如果信号通路被堵塞，那么操作软件重新提供配置，以便发射信号路由。UGA SSA传感器节点由安装在土壤（a）和电子部件（b）中的传感器组成。三个传感器被集成到一个轴（一），这样可以在种植后很容易地安装和收获农作物之前的萃取。照片右上角显示一个被部分拉出其PVC外壳的传感器节点电路板。节点电路由安装到传感器采集板的背面侧上的两个碱性AA电池供电。右下角照片给UGA-SSA系统显示网关。上述太阳能充电电池板包含一个12VDC电池，用来维持来自网络的数据采集。为了克服上无线电传输的植物冠层的衰减效果，所述RF发射器天线被安装在弹簧加载的，中空的，直径6毫米，挠性玻璃纤维棒约2.5米的地面以上（图2）。这样的设计可以让其他没有无线系统提供的现场设备，如拖拉机和喷雾器来越过传感器。RF发射器的公布范围为500米，尽管它的有效范围已观察到超过750个微米。图1我们的系统的一个重要特征是它的可承受成本，安装一个12节点系统一次性只要5200美元的费用。在整个田间灌水区安装传感器是监测和管理土壤水分变异存在于各个领域的关键。UGA SSA的另一个重要特点是，它报告的土壤水分条件下土壤水势（土壤水分张力）是以kPa为单位表示的。这使得该系统可以安装在没有任何校准土壤类型中。这是相对于需要校准电容式土壤湿度传感器来讲的。虽然对比电容式传感器而言，UGA SSA对土壤水分变化的反应比较慢，但其反应时间是足够调度农作物灌溉的。UGA的SSA在其目前的配置下已经在野外条件下测试了两个完整的种植季节了。在201
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