Effects of Irrigation Quantity and Term on Waterconsumption and Yield of Winter Wheat by Wide Precision Sowing

[Objective] The aim was to research effects of irrigation quantity and term on winter wheat by wide precision sowing and to provide references and technical supports for water-saving agriculture in North China. [Methed] During 2013-2015, Jimai 22, a winter wheat cultivar, was taken as materials to explore effects of irrigation quantity and term on water consumption characters and yield of winter wheat by wide precision sowing. [Result] As irrigation water increased, water consumption and irrigation water＇s proportions were growing, but quantity and proportion of soil water consumption were both diminishing; seed yields all kept increasing upon irrigation, but water use efficiencies were decreasing. Given the same irrigation conditions, water consumption by wide precision sowing was more, but yield and water use efficiency were higher. [Conclusion] The practice of combining wide precision sowing and irrigation in jointing and flowering stages, based on yield, water use efficiency and economic profits, has the potential to create more yields and higher water use efficiency and suitable to be applied and promtoed in North China.

Advanced Irrigation Engineering： Precision and Precise

Irrigation advances in precision irrigation （PI） or site specific irrigation （SSI） have been considerable in research; however, commercialization lags. SSI/PI has applications when soil texture variability affects soil water holding capacity or when crop yield or biotic stresses （insects or diseases） are spatially variable. SSI/PI uses variable rate application technologies, mainly with center-pivots or lateral-move or linear irrigation machines, to match crop needs or soil water holding constraints. Variable rate applications are achieved by variable nozzle flow rates, pulsing nozzle flows, or multiple nozzles on separate submains. Newer center pivot and linear machines are controlled by on-board microprocessor systems that can be integrated with supervisory control and data acquisition controllers for both communication and control of the variable rate application for specific sets of nozzles or individual nozzles for management zones. Communication for center pivot or linear controllers typically uses radio telemetry, wireless interact links, or cellular telephones. Precision irrigation has limited utility without precise irrigation scheduling （temporally and spatially）. Plant or soil sensors are used to initiate or complete an irrigation event. Automated weather stations provide site information for determining the irrigation requirement using crop models or simpler reference evapotranspiration （ET）, data to be used with crop coefficients. Remote sensing is being used to measure crop water status or crop development from spectral reflectance. Near-surface remote sensing with sensors mounted on moving irrigation systems provide critical spatial integration from point weather networks and feedback on crop ET and irrigation controls in advanced automated systems for SSI/PI.

Design of Machine Learning Based Smart Irrigation System for Precision Agriculture

Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

Layering Precision Land Leveling and Furrow Irrigated Raised Bed Planting: Productivity and Input Use Efficiency of Irrigated Bread Wheat in Indo-Gangetic Plains

Stagnating yield and declining input use efficiency in irrigated wheat of the Indo-Gangetic Plain (IGP) coupled with diminishing availability of water for agriculture is a major concern of food security in South Asia. The objective of our study was to establish an understanding of how wheat yield and input use efficiency can be improved and how land leveling and crop establishment practices can be modified to be more efficient in water use through layering of precision-conservation crop management techniques. The “precision land leveling with raised bed” planting can be used to improve crop yield, water and nutrient use efficiency over the existing “traditional land leveling with flat” planting practices. We conducted a field experiment during 2002-2004 at Modipuram, India to quantify the benefits of alternate land leveling (precision land leveling) and crop establishment (furrow irrigated raised bed planting) techniques alone or in combination (layering precision-conservation) in terms of crop yield, water savings, and nutrient use efficiency of wheat production in IGP. The wheat yield was about 16.6% higher with nearly 50% less irrigation water with layering precision land leveling and raised bed planting compared to traditional practices (traditional land leveling with flat planting). The agronomic (AE) and uptake efficiency (UE) of N, P and K were significantly improved under precision land leveling with raised bed planting technique compared to other practices.

Cotton Response to Variable Nitrogen Rate Fertigation through an Overhead Irrigation System

Recent increases in irrigated hectares in the Southeastern US have enabled growers to obtain higher yields through applying nutrients through irrigation water. Therefore, many growers apply nutrients through irrigation systems, known as fertigation. Currently, there are no practical decision-making tools available for variable-rate application of nitrogen (N) through overhead sprinkler irrigation systems. Therefore, field tests were conducted on cotton (Gossypium hirsutum L.) during the 2016 and 2017 growing seasons to 1) adapt the Clemson sensor-based N recommendation algorithms from a single side-dress application to multiple applications through an overhead irrigation system;and 2) to compare sensor-based VRFS with conventional nutrient management methods in terms of N use efficiency (NUE) and crop responses on three soil types. Two seasons of testing Clemson N prediction algorithms to apply multiple applications of N were very promising. The multiple applications of N compared to the grower’s conventional methods (even though less N was applied) had no impact on yields in either growing season. There was no difference in cotton yields between 101 and 135 kg/ha N applications in either management zone. Also, there were no differences in yield between sensor-based, multiple N applications and conventional N management techniques. In relation to comparisons of the sensor methods only applying N in three or four applications, statistically increased yields compared to single or split applications in 2016. Applying N in four applications, statistically increased yields compared to single, split or triple applications in 2017. When the sensor-based methods were compared to the grower’s conventional methods averaged over four treatments, the sensor-based N applications reduced fertilizer requirement by 69% in 2016 and 57% in 2017 compared to grower’s conventional methods. When comparing N rates among the four sensor-based methods (three or four) applications, increased N rates by 22 kg/ha in 2016 and 26 kg/ha in 2017 compared to single or split applications but increased the cotton lint yields by 272 and 139 kg/ha, for 2016 and 2017, respectively.

IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation process.Since the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.

Development and Testing of a Variable Rate Nitrogen Application System through an Overhead Irrigation System

Nutrients are injected through overhead irrigation systems at a uniform rate in a process known as fertigation. The highly variable soils in the Southeastern US pose challenges for effective fertigation. Currently, there is no variable-rate fertigation system available to apply the correct amount of N within a field through an overhead irrigation system. Therefore, the objective of this study was to develop and test a variable-rate N application system that works independently of irrigation water flow for site-specific N application. The variable-rate fertigation system (VRFS) was designed to apply different rates N using a pulse width modulation technique. The VRFS utilized the Clemson Lateral Irrigation Control software which controlled the solenoids in each zone by turning the N supply on and off (pulsing) for each zone. In this study, four tests were conducted to determine the uniformity of the VRFS. In test # 1, the pump output showed a linear slope relationship and was the same for water and N. In test # 2, nozzle flow and uniformity were determined using four different irrigation system travel speeds at N application rates of 31, 59, 88, and 113 kg/ha. There was a strong correlation (R2 = 0.9998) between irrigation system speed and N rate. In test # 3, the uniformity across the length of the irrigation system was determined. The nozzles produced an average flow of 31.1, 58.7, 87.6, and 112.7 kg N/ha with an overall average error of 0.1% across all N rates. Results also showed the system was capable of accurately applying N based on prescription maps with an error of less than 1.8%. Test # 4 was conducted to determine the accuracy of the map-based controller system for applying variable rate N. There was a strong correlation between target N and actual N rates (R2 = 0.9999). In summary, the VRFS applied the correct amounts of N within each zone by either manually controlling the pulsing mechanism or utilizing a prescription map to apply different rates throughout the field.

Automated Irrigation System Using Improved Fuzzy Neural Network in Wireless Sensor Networks

Irrigation plays a significant role in various agricultural cropping methods deployed in semiarid and arid regions where valuable water applications and managing are considered crucial concerns.Multiple factors such as weather,soil,water,and crop data need to be considered for irrigation maintenance in an efficient besides uniform manner from multifaceted and different information-based systems.A Multi-Agent System(MAS)has been proposed recently based on diverse agent subsystems with definite objectives for attaining global MAS objective and is deployed on Cloud Computing paradigm capable of gathering information from Wireless Sensor Networks(WSNs)positioned in rice,cotton,cassava crops for knowledge discovery and decision making.The radial basis function network has been used for irrigation prediction.However,in recent work,the security of data has not focused on where intruder involvement might corrupt the data at the time of data transferring to the cloud,which would affect the accuracy of decision making.To handle the above mentioned issues,an efficient method for irrigation prediction is used in this work.The factors considered for decision making are soil moisture,temperature,plant height,root depth.The above-mentioned data will be gathered from the sensors that are attached to the cropfield.Sensed data will be forwarded to the local server,where data encryption will be performed using Adaptive Elliptic Curve Cryptography(AECC).After the encryption process,the data will be forwarded to the cloud.Then the data stored in the cloud will be decrypted key before being given to the deci-sion-making module.Finally,the uniform distribution-based fuzzy neural network is formulated based on the received data information in the decisionmaking module.Thefinal decision regarding the level of water required for cropfields would be taken.Based on this outcome,the water volve opening duration and the level of fertilizers required will be considered.Experimental results demonstrate the effectiveness of the proposed model for the United States Geological Survey(USGS)database in terms of precision,accuracy,recall,and packet delivery ratio.

Remote Scheduling System for Drip Irrigation System Using Geographic Information System

The Internet is widely accessible in Tanzania. Most of the technologies used in different organizations have changed to address their functions using web based information systems. In this paper, attempt is made to design software system using geographical information system (GIS) for the spatial and temporal distribution of irrigation supply for large-scale drip irrigation systems in Tanzania. Map based information system has gained popularity after evolution of simple tools to present spatial information using Internet. Due to water scarcity, it is envisioned that by 2050 the world won’t have enough water for communities, industries and agriculture. Web based precision irrigation system refers to deployment of remotely precision irrigation services using the application interface that connects to the Internet. Hence, this study presents the GIS in the context of precision farming to achieve precision irrigation strategy with special reference to precision farming of tea in Tanzania. The GIS-based irrigation scheduling system was designed for the scheduling daily drip irrigation water deliveries and regular monitoring of irrigation delivery performance for maximum yield. The “Scheduling” program computes the right amount of irrigation deliveries based on tea water requirements. The “Monitoring” program gives information on the uniformity of water distribution and the shortfall or excess.

精密平台磁敏智能隔振系统自适应PI控制研究

为抑制精密平台存在的时变振动,提出一种基于磁流变弹性体(magneto rheological elastomer,MRE)半主动自适应比例积分(proportional integral,PI)控制方法。首先针对MRE隔振系统,建立其动力学模型,并根据MRE变刚度隔振原理,推导半主动控制条件;其次以响应加速度和目标加速度偏差作为PI控制器输入,通过遗传算法获得不同频率、幅值激励下的最优PI控制参数,并基于被动参考模型获得频率幅值依赖自适应律。该方法无须通过复杂的傅里叶变换辨识频率,实时性高;最后通过仿真和试验验证了所设计控制器的有效性;结果表明,与传统PI控制器相比,自适应PI控制器对40~50 Hz、1.0~3.0 m/s^(2)时变振动具有更大的抑制。

基于BP神经网络PID的节水灌溉施肥系统研究

中国的化肥使用率常年居世界首位,且农业用水利用率较低,依靠个人经验的方法不仅造成了肥料和水资源的浪费,而且使当地生态环境也受到污染。由于管路运输等原因,节水灌溉施肥系统具有模型的时变性、非线性与时滞性的特点,普通控制器很难对节水灌溉施肥系统的流量进行精准控制。针对上述问题,设计了一种基于BP神经网络PID的控制器,以期实现节水灌溉施肥系统对液体肥流量的精准控制;同时,与传统PID控制器进行对比,用MatLab软件进行仿真分析,得到阶跃响应曲线。研究结果表明:基于BP神经网络PID的控制器具有优异的控制效果,可以满足节水灌溉施肥系统精准控制的实际要求。

模糊与PI分段调控肥液EC的优化设计与试验

设施蔬菜生产中对肥液电导率(EC)的精量调控是实现智能灌溉的关键技术之一。肥液混合过程中,由于母液浓度、吸肥方式和灌溉流量等存在差异,试验检测到的EC值会产生滞后和不稳定现象。针对上述问题,本研究设计了一种通过测量频率间接得到肥液电导率的传感器,并以MSP430单片机为主控制器构建了一套水肥调控试验系统。同时,在肥液调控方法方面设计了粗细分段控制策略,控制方法均为以输出相应的占空比来控制吸肥电磁阀的开闭。当实测EC值与目标EC值相差较大时,采用PI控制方法,能够快速缩小差距;而当两者偏差较小时,采用模糊控制可以使混肥EC值逼近设定值。此外,试验利用多管路复合式文丘里,设计了4组不同的EC目标值和4种不同的吸肥管路组合进行吸肥测试。结果表明,EC传感器间接测量到的频率与实际肥液EC值有显著的线性关系,决定系数为0.999 5,保证了测试精度。当目标EC值设定为2 m S/cm时,分段控制情况下实测EC达到稳态的用时为122 s,最大EC值为2.34 m S/cm,优于采用单PI控制的180 s和2.62 m S/cm。且目标EC值越大,稳态EC值越精确,但是稳态时延和过量超调现象更明显。本研究表明分段调控能够较好地克服EC值的过量超调,同时混肥时间和实测EC值能够满足实际需求。

PI控制在空间相机精密控温上的应用

以某空间相机镜筒作为控制对象,建立了热分析模型,提出了基于积分分离式比例积分(PI)主动热控制的温度控制方法。积分分离式PI算法采用增量式计算、位置式输出,将输出的加热功率值转换为一个控温周期内的加热时间。以相机镜筒的热分析模型为基础,通过Ziegler-nichols参数整定方法获取积分分离式PI算法的比例参数和积分参数,利用热分析软件Thermal Desktop对空间相机镜筒进行了热分析,获取控温算法的控温性能。结果表明:积分分离式PI控制方法响应快,消除了稳态误差,较比例(P)控制具有更好的动、静态特性,适用于有高精度控温需求的主动控温设计。

基于Raspberry Pi的室内智能灌溉系统设计与研究

为提高灌溉的精确度与精细化种植的用水效率,研究设计了一种基于 Raspberry 的精准灌溉系统。采用Raspberry Pi 3b与相关传感器设计了系统的硬件,应用pyhton语言编写系统相关程序,并搭建数据库,编写了二维模糊控制器的模糊算法控制程序,其输入量为土壤湿度和土壤湿度的变化值,输出量为直流电机的 PWM 值。最后通过黄瓜幼苗的种植进行试验,达到了预期的节水目标。为智能灌溉的全面实现提供理论基础与参考依据。

基于数字PI算法的精准施肥控制系统设计及仿真

为了使施肥速度与机器行进速度同步,提出了一种基于数字PI算法的闭环施肥系统,主要由传感器、PLC、脉宽调制电机驱动器(PWM驱动器)、GPRS模块及直流无刷电机组成。通过检测拖拉机的速度信号及外槽轮转速,PLC根据所输入决策经PWM驱动器对外槽轮下料速度进行闭环控制,从而实现精准施肥。仿真实验结果表明:基于数字PI算法的闭环控制系统提高了施肥精度和响应速度,机具操作简单,在田间工作性能稳定。

基于PSO优化Fuzzy-PID精量灌溉控制系统设计

精确控制农业灌溉中的灌溉精度和水肥比例,能够大大提高水肥的利用率。因此,基于传统PID控制、Fuzzy-PID控制两种控制算法的优缺点,进行PSO优化Fuzzy-PID控制。该控制算法能够有效解决大棚灌溉控制中的非线性、时变性和滞后性等问题。实验结果表明:该系统与传统PID控制相比,上升时间减少了4.10 s,超调量降低了14.57%,调节时间减少了27.4 s;相比于Fuzzy-PID控制,上升时间减少了4.30 s,超调量降低了0.37%,调节时间减少了20 s。该系统响应速度快、配比精度高、稳定,具有一定的应用价值。

基于P-Fuzzy-PI控制的煤气加热炉控制系统

基于现有加热炉控制系统中煤气与空气的比率难以控制,从而导致煤气不能充分燃烧或燃烧效率过低而造成浪费.针对现状,提出一种新的控制方法:P—Fuzzy—PI控制,分不同阶段加以不同的控制方式.P控制主要是提高系统的快速性,PI控制主要是为了提高系统的稳态精度,Fuzzy控制主要是在难以获得被控对象的精确数学模型时提高系统对过程参数的变化的适应性.在Fuzzy控制中,采用插值计算法来提高模糊控制器的控制精度.仿真结果表明,这种控制方法具有提高系统的快速性,消除系统的稳态误差的作用,提高了系统的控制精度,从而解决了加热炉控制中煤气与空气的比率调节不及时而造成其热值不稳定的难题,具有一定的实用价值.

基于PI滑模变结构的步进电机位置控制研究

随着步进电机在各行各业中的应用日益广泛,传统的开环控制方法在实际工程应用中已难以满足更高的精度要求。该文建立了一类三相混合式步进电机的机电耦合模型和精确控制模型。提出了一种基于PI并联的滑模变结构控制方法。该方法将PI控制与滑膜变结构控制相结合,合理选择滑膜控制器的切换函数和趋近律。利用李雅普诺夫稳定性理论证明了所设计控制系统的稳定性。MATLAB/Simulink仿真系统验证了控制方法的有效性和正确性,并搭建了实验平台。结果表明,基于并联PI反馈的滑膜变结构控制策略具有更快的响应速度和更高的控制精度。

GIS-based irrigation water management for precision farming of rice

Precision farming aims to manage production inputs over many small management zones rather than on large zones.It is difficult to manage inputs at extremely fine scales,especially in the case of the rice irrigation system.However,site-specific irrigation management can potentially improve the overall water management in comparison to irrigated areas of hundreds of hectares.A critical element of the irrigation scheduling and management is the accurate estimation of irrigation supplies and its proper allocation for the irrigation offtake structures based on the actual planted areas.All irrigation scheduling procedures consist of monitoring indicators that determine the need for irrigation.The final decision depends on the irrigation criterion,strategy and goal.Irrigation scheduling is the decision of when and how much water to apply to a field.The amount of water applied is determined by using a criterion to determine irrigation need and a strategy to prescribe how much water to apply in any situation.The right amount of daily irrigation supply and monitoring at the right time within the discrete irrigation unit is essential to improve the irrigation water management of a scheme.This paper presents the GIS capability to achieve the goal in the view of irrigation strategy and goal with special reference to precision farming of rice.The GIS-based water management model was developed for the scheduling daily irrigation water deliveries and regular monitoring of irrigation delivery performance.The“Scheduling”program computes the right amount of irrigation deliveries based on crop water requirements.The“Monitoring”program gives information on the uniformity of water distribution and the shortfall or excess.The displayed results allow the manager to view maps,tables and graphs in a comprehensible form to ease decision making that where the irrigation amount will be delivered as the season progresses.GIS was used as a useful tool to assist the irrigation water management program in the context of precision farming.

Review of conceptual and systematic progress of precision irrigation

Precision irrigation,defined as accurate and appropriate agricultural techniques characterized by optimal management and best collaboration of various irrigation factors,attracts great attention and obtains wide employments in different irrigation conditions or cultivation processes.Moreover,it becomes well-established in major areas of agricultural researches and across the broad spectrum of agricultural techniques especially in specific sectors of scientific frontiers,including soil quality,irrigation scheduling,water resource distribution,crop productivity,tillage management,climate adaptation,and environment monitoring,etc.This paper reviews the research developments and integrated applications of precision irrigation in typical domains of mechanism and performance,covering key aspects such as process optimization,schedule modelling,and effectiveness evaluation,indicating that advanced irrigation optimization methods support higher productivity of crop field and better environmental conditions of soil;Current schedule modelling techniques provide a set of instructive demonstrations and heuristic descriptions for the working principles of precision irrigation and the quantitative assessments of irrigation productivity;The novel investigation on effectiveness evaluation is extremely significant to obtain higher infiltration efficiency,simultaneously to achieve the optimized irrigation qualities for water balance condition,soil water redistribution,and soil moisture uniformity so that the effectiveness quality of irrigation infiltration could be improved remarkably.It is concluded that precision irrigation owns an outstanding collaborating capability and possesses much better working advancement in typical calibration indexes of cultivation accuracy and infiltration efficiency,meanwhile,a high agreement between the predicted and actual irrigation effectiveness could be expected.This novel irrigation review concentrating on the conceptual and systematic progress should be promoted constructively to improve the quality uniformity for precision irrigation and its constructive influences in different applications,and to facilitate the integrated management of agricultural production by higher irrigation efficiency consequently.