Real-Time Modelling and Optimisation for Water and Energy Efficient Surface Irrigation

The viability and sustainability of crop production is currently threatened by increasing water scarcity. Water scarcity problems can be addressed through improved water productivity and the options usually presumed in this context are efficient water use and conversion of surface irrigation to pressurised systems. By replacing furrow irrigation with drip or centre pivot systems, the water efficiency can be improved by up to 30% to 45%. However, the installation and application of pumps and pipes, and the associated fuels needed for these alternatives increase energy consumption. A balance between the improvement in water use and the potential increase in energy consumption is required. When surface water is used, pressurised irrigation systems increase energy consumption substantially, by between 65% to 75%, and produce greenhouse gas emissions around 1.75 times higher than that of gravity based irrigation systems so their use should be carefully planned keeping in view adverse impact of carbon emissions on the environment and threat of increasing energy prices. With gravity-fed surface irrigation methods, the energy consumption is assumed to be negligible. This study has shown that a novel real-time infiltration model REIP has enabled implementation of real-time optimisation and gravity fed surface irrigation with real-time optimisation has potential to bring significant improvements in irrigation performance along with substantial water savings of 2.92 ML/ha which is equivalent to that given by pressurised systems. The real-time optimisation and control thus offers a modern, environment friendly and water efficient system with close to zero increase in energy consumption and minimal greenhouse gas emissions.

Performance analysis of real-time and post-mission kinematic precise point positioning in marine environments

This article focuses on the performance analysis of both real-time and post-mission kinematic precise point positioning(PPP)in challenging marine environments.For this purpose,a real dynamic experiment lasting 6 h was carried out on a lake dam in?orum City of Turkey.While the kinematic test was continuing,the real-time PPP coordinates were obtained for each measurement epoch with a commercial real-time PPP(RT-PPP)service,namely the Trimble Center Point RTX.Then the post-mission PPP(PM-PPP)coordinates were calculated by using Multi-GNSS data and the Multi-GNSS Experiment(MGEX)precise products.The kinematic RT-PPP and PM-PPP results showed that the PPP coordinates were consistent with the relative solution at centimetre and decimetre level in horizontal and height components,respectively.This study implies that PPP technique is a powerful tool for highly accurate positioning in both real-time and post-mission modes,even for dynamic applications in harsh environments.

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.

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC),a novel real-time iterative compensation(RIC)control framework is proposed for precision motion systems without changing the inner closed-loop controller.Specifically,the RIC method can be divided into two parts,i.e.,accurate model prediction and real-time iterative compensation.An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times.In light of predicted errors,a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation.Both the prediction and compen-sation processes are finished in a real-time motion control sampling period.The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions.Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability,which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.

The Real-Time,High Precision Phase Difference Measurement of Electron Density in HL-2A Tokamak

This paper introduces a real-time high precision measurement of phase difference based on Field Programmable Gate Array（FPGA） technology,which has been successfully applied to laser grating interference measurement and real-time feedback of plasma electron density in HL-2A tokamak.It can track the changes of electron density while setting the starting point of the density curve to zero.In a laboratory test,the measuring accuracy of phase difference is less than 0.1°,the time resolution is 80 ns,and the feedback delay is 180 μs.

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.

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.

Efficient Harmonic Analysis Technique for Prediction of IGS Real-Time Satellite Clock Corrections

Real-time satellite orbit and clock corrections obtained from the broadcast ephemerides can be improved using IGS real-time service (RTS) products. Recent research showed that applying such corrections for broadcast ephemerides can significantly improve the RMS of the estimated coordinates. However, unintentional streaming interruption may happen for many reasons such as software or hardware failure. Streaming interruption, if happened, will cause sudden degradation of the obtained solution if only the broadcast ephemerides are used. A better solution can be obtained in real-time if the predicted part of the ultra-rapid products is used. In this paper, Harmonic analysis technique is used to predict the IGS RTS corrections using historical broadcasted data. It is shown that using the predicted clock corrections improves the RMS of the estimated coordinates by about 72%, 58%, and 72% in latitude, longitude, and height directions, respectively and reduces the 2D and 3D errors by about 80% compared with the predicted part of the IGS ultra-rapid clock corrections.

基于物联网农业灌溉系统精准控制模型的研究

为进一步提升我国农业灌溉系统的综合作业效率,体现灌溉的先进性与科学性,提出以精准控制模型为切入点,以物联网技术为主体支撑平台,针对其作业系统展开设计与优化。通过引入物联网强大的系统性架构,规范与强化各网络节点的部署与协作关系,科学搭建用于实现精准灌溉控制的数学模型,同时导入合理的系统软件设计程序与硬件功能组件配置,形成完整集成式的农业智能灌溉系统。结果表明:基于物联网平台技术的精准控制模型应用后,整体的作业效率得到显著提升,系统精准度、系统响应率分别相对提高了6.25%和7.68%,动作延迟率相对降低了2.72%,灌溉节水效率提升至91.50%。物联网平台下的精准控制模型应用效果良好,设计理念正确,可为灌溉领域实施节能降耗提供参考。

地下渗灌研究进展

地下渗灌是一种地下微灌方法,通过埋设于地下的渗灌管将灌溉水引至地面下一定深度的土壤中,再利用土壤毛细管作用实现对作物根区直接供水,该灌水方法与“地下滴灌”的灌水过程实质相同。地下渗灌可有效改善土壤环境质量,利于作物生长,是一项发展前景广阔的高效节水灌溉技术,开展地下渗灌的研究对于推动高效节水农业发展具有重要意义。从地下渗灌条件下土壤水分运移规律、灌水技术参数、灌溉制度、渗灌管堵塞的影响及调控等方面,对相关研究进展和存在问题进行综述,提出了今后的研究方向,供节水灌溉研究领域的学者参考。综述认为,与地下渗灌技术的生产实践相比,对其机理方面的研究相对滞后,限制了该技术的深入推广应用,主要表现在:地下渗灌条件下土壤水分运移规律尚不够清晰;堵塞问题依然是目前阻碍地下渗灌技术应用与发展的限制性因素;地下渗灌配水系统优化设计问题尚需进一步研究。建议今后开展各种不同渗灌条件下的水分入渗数值模拟分析;选取合适的处理水平,研究渗灌灌水效果的主要影响因素,寻求最优灌水技术参数组合;针对地下渗灌条件下作物灌溉制度开展研究,形成一套合理完善的灌溉制度与试验方法;对于渗灌管出流规律、堵塞机理以及进入地下渗灌系统时灌溉水源水质关键参数的有效调控阈值等进行深入研究。

基于DTN路由的多通路精准灌溉系统布局设计

为进一步提升我国智能灌溉装备的综合作业效率,从多通路实施与精准化控制角度展开布局设计。以灌溉系统的作业原理及田间信息获取特征为出发点,建立灌溉网络的DTN路由算法模型,实施网络节点的规划分配与通道布置,形成精准性的逻辑控制流程及节点网络相互执行关系。进行系统性的灌溉试验,结果表明:基于DTN路由算法的多通路精准灌溉系统布局正确可行,信息投递成功率可达94.13%,节点转发率可达96.07%,各信息传递的实时准确,各阀门组件的动作协调,综合灌溉效率实现了高于90%的优化目标,是DTN路由控制理念在灌溉装备领域的应用创新思路之一,具有较好的推广意义。

“精准滴灌式”党员教育管理模式在高校学生社区中的实践运用

当前,高校要推进“一站式”学生社区综合管理工作必须紧紧围绕党建引领,将党建文化、专业学习、校园文化等内容充分融入到社区管理和活动之中,使学生社区切实成为融思想教育、行为导向、生活服务、文化熏陶为一体的“第二课堂”主阵地和学校立德树人的新高地。近年来,韶关学院积极探索“精准滴灌式”党员教育管理模式在高校学生社区的实践运用,尤其在强化党建引领、优化社区软硬件设施建设、凸显学生自我管理的主体作用与持续推进和完善制度建设等方面,为“一站式”学生社区综合管理的深入开展提供强有力的平台与保障,确保其在立德树人根本任务上发挥实效。

基于元分析的新疆膜下滴灌棉田精量施氮研究

【目的】明确施氮对棉花产量及其构成因子的影响,并为氮肥的精量施用及棉花高产提供理论借鉴。【方法】以新疆膜下滴灌棉田为研究对象,采用元分析(meta-analysis,meta分析)和通径分析,研究不同施氮量、施氮方案、气候条件等对棉花产量的综合效应及影响机制。【结果】与不施氮相比,施氮能显著提高棉花产量,增产效应为43.38%。施氮量为360~480 kg·hm^(-2)时,对棉花的增产效应最大;施氮量超过此范围,棉花产量不再显著增加,本研究推荐的经济施氮量为360~420 kg·hm^(-2)。基肥20%,追肥80%且按照6%、8%、22%、25%、12%、7%的比例随水滴施6次的施氮方案对棉花的增产效应最大。对于年蒸发量>2000 mm、年降水量<60 mm、年日照时间<2864 h、年有效积温>4000℃、无霜期>200 d的地区,且土壤为砂质土、土壤初始有机碳含量<5.8g·kg^(-1)、初始速效氮含量≤60 mg·kg^(-1)的棉田,施氮的增产效应最明显。通径分析结果表明,施氮通过提高土壤硝态氮含量,从而增加棉花叶面积指数,对棉花产量的提升贡献最显著。【结论】建议新疆植棉区施氮量为360~420 kg·hm^(-2),采用上述优化方案合理施氮,可以实现膜下滴灌棉田的高产并降低环境风险。

地方高校课程思政建设探究——以《互联网金融》课程为例

课程思政建设是教学改革的重要组成部分,但目前专业课的教学内容多为理论授课,对思想政治教育理论与方法方面关注较少。以地方高校《互联网金融》课程为例,基于现代化技术,从教学大纲、教学设计、教学方法和课程评价体系4个方面构建“课程思政”与专业教育协同育人的精准融合模式,发挥专业课程的育人作用,使价值观教育与知识传授同时达到精准教学的效果。

文丘里施肥器在水肥一体化精准灌溉中的应用

文丘里施肥器具有便宜、耐用、应用广泛等优点。通过对比几种施肥器施肥方式,分析了文丘里施肥器应用上的局限性,提出了适宜的解决方法,为精准控制施肥,进一步推广水肥一体化精准灌溉技术模式的应用奠定基础。