Augmented IoT Model for Smart Agriculture and Farm Irrigation Water Conservation

In Northern Nigeria, irrigation systems are operated manually. Agriculture has over the years been practiced primitively by farmers, especially in sub-Saharan Africa. This is due to the absence of intelligent technological know-how where its practice could be leveraged upon. Agricultural practice is constrained by some major challenges ranging from traditional way of farming, understating of concepts, practices, policy, environmental and financial factors. The aim of this study was to optimize an IoT-based model for smart agriculture and irrigation water management. The objectives of the study were to: design, implement, test and evaluate the performance of the optimized IoT-based model for smart agriculture and irrigation water management. The method used in the study was the prototyping model. The system was designed using balsamiq application tools. The system has a login page, dashboard, system USE-CASE diagrams, actuators page, sensor page and application interface design. Justinmind tool was used to show the flow of information in the system, which included data input and output, data stores and all the sub-processes the data moves through. The Optimized IoT model was implemented using four core platforms namely, ReactJS Frontend Application development platform, Amazon web services IoT Core backend, Arduino Development platform for developing sensor nodes and Python programming language for the actuator node based on Raspberry Pi board. When compared with the existing system, the results show that the optimized system is better than the existing system in accuracy of measurement, irrigation water management, operation node, platform access, real-time video, user friendly and efficiency. The study successfully optimized an IoT-based model for smart agriculture and irrigation water management. The study introduced the modern way of irrigation farming in the 21st century against the traditional or primitive way of irrigation farming that involved intensive human participation.

The water-saving potential of using micro-sprinkling irrigation for winter wheat production on the North China Plain

The shortage of groundwater resources is a considerable challenge for winter wheat production on the North China Plain.Water-saving technologies and procedures are thus urgently required.To determine the water-saving potential of using micro-sprinkling irrigation(MSI)for winter wheat production,field experiments were conducted from 2012 to 2015.Compared to traditional flooding irrigation(TFI),micro-sprinkling thrice with 90 mm water(MSI1)and micro-sprinkling four times with 120 mm water(MSI2)increased the water use efficiency by 22.5 and 16.2%,respectively,while reducing evapotranspiration by 17.6 and 10.8%.Regardless of the rainfall pattern,MSI(i.e.,MSI1 or MSI2)either stabilized or significantly increased the grain yield,while reducing irrigation water volumes by 20–40%,compared to TFI.Applying the same volumes of irrigation water,MSI(i.e.,MSI3,micro-sprinkling five times with 150 mm water)increased the grain yield and water use efficiency of winter wheat by 4.6 and 11.7%,respectively,compared to TFI.Because MSI could supply irrigation water more frequently in smaller amounts each time,it reduced soil layer compaction,and may have also resulted in a soil water deficit that promoted the spread of roots into the deep soil layer,which is beneficial to photosynthetic production in the critical period.In conclusion,MSI1 or MSI2 either stabilized or significantly increased grain yield while reducing irrigation water volumes by 20–40%compared to TFI,and should provide water-saving technological support in winter wheat production for smallholders on the North China Plain.

The Potential Contribution of Subsurface Drip Irrigation to Water-Saving Agriculture in the Western USA

Water shortages within the western USA are resulting in the adoption of water-saving agricultural practices within this region. Among the many possible methods for saving water in agriculture, the adoption of subsurface drip irrigation （SDI） provides a potential solution to the problem of low water use efficiency. Other advantages of SDI include reduced NO3 leaching compared to surface irrigation, higher yields, a dry soil surface for improved weed control, better crop health, and harvest flexibility for many specialty crops. The use of SDI also allows the virtual elimination of crop water stress, the ability to apply water and nutrients to the most active part of the root zone, protection of drip lines from damage due to cultivation and tillage, and the ability to irrigate with wastewater while preventing human contact. Yet, SDI is used only on a minority of cropland in the arid western USA. Reasons for the limited adoption of SDI include the high initial capital investment required, the need for intensive management, and the urbanization that is rapidly consuming farmland in parts of the western USA. The contributions of SDI to increasing yield, quality, and water use efficiency have been demonstrated. The two major barriers to SDI sustainability in arid regions are economics （i.e., paying for the SDI system）, including the high cost of installation; and salt accumulation, which requires periodic leaching, specialized tillage methods, or transplanting of seedlings rather than direct-seeding. We will review advances in irrigation management with SDI.

Development Potentials and Benefit Analysis of Efficient Water-saving Irrigation in Lixin County

On the basis of analyzing water resources,crop planning structure,and irrigation mode in Lixin County,potentials and benefits of developing efficient water-saving irrigation in the county were explored to provide references for its future water-saving irrigation.

Cotton's Water Demand and Water-Saving Benefits under Drip Irrigation with Plastic Film Mulch

The primary purpose of this research was to give suitable irrigation program according to the growth period and water requirement.A cotton field experiment with mulched drip irrigation was conducted at the National Field Observation and Research Station for Oasis Farmland Ecosystem in Aksu of Xinjiang in 2008.Water balance method was adopted to study the water requirement and water consumption law of cotton under mulched drip irrigation in Tarim Irrigated Area.Statistical analysis of experimental data of irrigation indicates that the relationship between yield of cotton and irrigation presents a quadratic parabola.We fit the model of cotton water production on the basis of field experimental data of cotton.And the analysis on water saving benefit of cotton under mulched drip irrigation was done.Results indicate that water requirements for the irrigated cotton are 543 mm in Tarim Irrigated Area.The water requirements of seedling stage is 252 mm,budding stage is 186 mm,bolling stage is 316 mm and wadding stage is 139 mm.the irrigation amount determines the spatial distribution of soil moisture and water consumption during cotton life cycle.However,water consumption at different growth stages was inconsistent with irrigation.Quantitatively,the water consumed by cotton decreases upon the increase of irrigation amount.From the perspective of water saving,the maximal water use efficiency can reach 3 091 m3/ha.But the highest cotton yield needs 3464 m3/ha irrigation water.In summary,compared to the conventional drip irrigation,a number of benefits in water saving and yield increase were observed when using plastic mulch.At the same amount of irrigation,the cotton yield with plastic mulch was 30.2% higher than conventional approaches,and the efficiency of water utilization increased by30.2%.While at the same yield level,29.3% water was saved by using plastic mulch,and the efficiency increased by 41.5%.

Study on the Suitable Water-Saving Irrigation Technology for Mining Areas in the Northwestern Arid Desert Regions in China

Water is the key factor to ensure plant survival in the process of ecological restoration in the coal base of China northwest deserts. On the premise of meeting the mine production and living water demands, we should take measures such as dirt wastewater treatment and water-saving irrigation to increase income and reduce expenditure and allocate limited water re-sources rationally, to provide mining area ecological restoration maximum usable water resources. The mining dump has large slope and thin soil layer and it is easy to produce surface runoff. So it is particularly important to study the irrigation technology needed to satisfy vegetation restoration, on the premise of guaranteeing not to produce surface runoff and the slope stability. In this paper, through field plot test, the suitable irrigation method for mine slope, slope surface soil moisture migration characteristics and slope stability analysis were studied. Results show that three slope ir-rigation technologies have their own advantages and disadvantages. On the whole, the effect of drip irrigation is the best, micro spray irrigation is the second, infiltrating irrigation is not ideal. The permeability of mine soil slope is very strong, the infiltration rate of the slope direction is the high-est, inverse slope infiltration rate is lowest. In the process of irrigation, with the increase of soil moisture content, slope safety factor is the decreased obviously, the whole slope surface soil moisture content is 14% for the slope stability safety threshold.

Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual agriculture.Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture.Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques.Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist.With this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent agriculture.The MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination purposes.Besides,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not needed.The MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

IoT-Solar Energy Powered Smart Farm Irrigation System

As the Internet of things(IoT)technology is evolving,distributed solar energy resources can be operated,monitored,and controlled remotely.The design of an IoT based solar energy system for smart irrigation is essential for regions around the world,which face water scarcity and power shortage.Thus,such a system is designed in this paper.The proposed system utilizes a single board system-on-a-chip controller(the controller hereafter),which has built-in WiFi connectivity,and connections to a solar cell to provide the required operating power.The controller reads the field soil moisture,humidity,and temperature sensors,and outputs appropriate actuation command signals to operate irrigation pumps.The controller also monitors the underground water level,which is essential to prevent the pump motors from burning due to the level in the water well.The proposed system has three modes of operations,i.e.the local control mode,mobile monitoring-control mode,and fuzzy logic-based control mode.For the purpose of the proposed system validation,a prototype was designed,built,and tested.

Smart Irrigation System: A Water Management Procedure

This paper presents a smart irrigation system suitable for use in places where water scarcity is a challenge. In many parts of Africa, even when irrigation is practiced, it is manually operated. Smart irrigation system is thereby believed to be a major solution. The paper therefore presents a smart irrigation system that optimizes the available water in the water reservoir thus providing an efficient and effective water usage solution for the irrigation system. The irrigation system is able to automatically start/stop water pumps on the irrigation site based on the soil moisture content acquired from the moisture content sensor as well as the ultrasonic sensor measuring the water level in the reservoir. The measured sensor values are sent to the Arduino microcontroller for configuring the control algorithm. The system prioritizes irrigation operation by determining the number of pumps to be operated at any instance as well as their locations. In this way, different crops can be watered depending on their varying water requirements. In order to implement the design, a laboratory scale architectural model depicting a farm setting with reservoir, direct current (DC) pumps and the control unit was constructed. Experimental results revealed good performance which makes the developed system a suitable tool for studies on irrigation.

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.

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.

What determines irrigation efficiency when farmers face extreme weather events? A field survey of the major wheat producing regions in China

Water availability is a major constraint on grain production in China, therefore, improving irrigation efficiency is particularly important when agriculture faces extreme weather events. This paper first calculates irrigation efficiency with a translog stochastic frontier production function and then investigates what happens when extreme weather events occur via a Tobit model. The estimated results reveal several important features of irrigation practices: i) irrigation efficiency is lower when extreme weather events occur; ii) large variations in irrigation efficiency occur across irrigation facilities; iii) the farm plots exhibit an extreme distribution across efficiency levels; and iv) water-saving techniques, technology adoption, and the maintenance of farmers’ economic resilience are major determinants of irrigation efficiency. Based on these results we propose the following recommendations: i) farmers should balance crop yield and water use; undertake relevant training programs and adopt water-saving techniques; ii) local governments and researchers should help farmers to find the optimal level of irrigation water use based on their own circumstances and provide better water-saving techniques and training programs rather than simply encouraging farmers to invest in irrigation facilities in the most extreme weather years; and iii) the income level of farm households should be increased so as to improve their resilience to natural disasters.

Non-negligible factors in low-pressure sprinkler irrigation:droplet impact angle and shear stress

Droplet shear stress is considered as an important indicator that reflects soil erosion in sprinkler irrigation more accurately than kinetic energy,and the effect of droplet impact angle on the shear stress cannot be ignored.In this study,radial distribution of droplet impact angles,velocities,and shear stresses were investigated using a two-dimensional video disdrometer with three types of low-pressure sprinkler(Nelson D3000,R3000,and Komet KPT)under two operating pressures(103 and 138 kPa)and three nozzle diameters(3.97,5.95,and 7.94 mm).Furthermore,the relationships among these characteristical parameters of droplet were analyzed,and their influencing factors were comprehensively evaluated.For various types of sprinkler,operating pressures,and nozzle diameters,the smaller impact angles and larger velocities of droplets were found to occur closer to the sprinkler,resulting in relatively low droplet shear stresses.The increase in distance from the sprinkler caused the droplet impact angle to decrease and velocity to increase,which contributed to a significant increase in the shear stress that reached the peak value at the end of the jet.Therefore,the end of the jet was the most prone to soil erosion in the radial direction,and the soil erosion in sprinkler irrigation could not only be attributed to the droplet kinetic energy,but also needed to be combined with the analysis of its shear stress.Through comparing the radial distributions of average droplet shear stresses among the three types of sprinklers,D3000 exhibited the largest value(26.94-3313.51 N/m^(2)),followed by R3000(33.34-2650.80 N/m^(2)),and KPT(16.15-2485.69 N/m^(2)).From the perspective of minimizing the risk of soil erosion,KPT sprinkler was more suitable for low-pressure sprinkler irrigation than D3000 and R3000 sprinklers.In addition to selecting the appropriate sprinkler type to reduce the droplet shear stress,a suitable sprinkler spacing could also provide acceptable results,because the distance from the sprinkler exhibited a highly significant(P<0.01)effect on the shear stress.This study results provide a new reference for the design of low-pressure sprinkler irrigation system.

Effects of Intelligent Irrigation on Photosynthetic Characteristics of Citrus Leaves and Fruit Quality

[Objectives]This study was conducted to improve the water use efficiency and fruit quality of citrus,and realize the automatic irrigation and standardized production in citrus orchards.[Methods]With Orah as the research object,the effects of intelligent irrigation management in citrus orchards on citrus leaf chlorophyll content,photosynthetic characteristics and fruit quality were investigated by comparing with conventional farmer management.[Results]The smart irrigation measure in citrus orchards significantly increased the SPAD value of leaves at the maturation stage of citrus,and simultaneously significantly improved the photosynthetic rate,transpiration rate and stomatal conductance at the flower bud differentiation stage,as well as the photosynthetic rate,transpiration rate,stomatal conductance and intercellular CO_(2) concentration at the maturation stage.However,the effects on the photosynthetic characteristic indexes in the rainy season were not significant.Compared with conventional experience management,the smart irrigation management measure of citrus orchards increased the edible rate and juice rate of citrus by 4.53 and 3.69 percentage points,respectively,and increased soluble solids,total sugar,vitamin C and sugar-acid ratio by 16.75%,20.86%,24.10%and 13.17%,respectively.[Conclusions]The smart irrigation management fully met the water demand for citrus growth due to timely irrigation,significantly improved the photosynthesis indicators of citrus leaves during drought,and significantly improved the quality of citrus.

Optimization of Irrigation Water Allocation Framework Based on Genetic Algorithm Approach

In a world where excessive use and degradation of water resources are threatening the sustainability of livelihoods dependent on water and agriculture, increased food production will have to be done in the face of a changing climate and climate variability. There is a need to make optimal use of the available water resource to maximize productivity. Climate-smart irrigation is aimed at increasing per unit production and income from irrigated cropping systems without having negative impacts on the environment or other water users and uses. This paper developed a water allocation model using Genetic Algorithm to equitably allocation available water to the various sectors in Kano River Irrigation Scheme yielding an optimal as well as equitable water release with a 96.44% demand met. An average relative supply of 0.94 was obtained indicating the there was even supply of water to all the sectors. The model is robust and relatively easy to apply and can be employed by farm managers to achieve equity and optimal use of the available water resource.

Efficient Water-Saving Irrigation,Space Efficiency and Agricultural Development——Study Based on Spatial Stochastic Frontier Model

Xinjiang's agriculture is a typical irrigated agriculture for its agriculture water consumption accounts for 96%of the total water use.As a typical resource-deficient area,the key to Xinjiang's agricultural development is saving water.This paper takes the high-efficient water-saving irrigation technology of 41 regions along the Tarim River from 2002 to 2013 as the research object,adopts spatial stochastic frontier model to measure the space efficiency of high-efficient water-saving irrigation technology,and analyzes the effect of water-saving irrigation technology on agricultural development.Results show that the water-saving irrigation technology has a spatial effect,if neglecting it,the error of missing variables will occur,and the average loss will be 6.98 percentage points.The spatial correlation effect promotes the improvement of the efficiency of water-saving irrigation technology.The spatial heterogeneity leads to the spatial imbalance of the efficiency of water-saving irrigation technology.The promotion of agricultural water-saving irrigation technology can increase production and the efficiency of agricultural development.Due to the technical heterogeneity of different types of water-saving irrigation technology,the contribution to the development of agriculture is also different.The study finds that water-saving irrigation technology of drip irrigation in the Tarim River contributes more to agricultural development.

Optimizing water-saving irrigation schemes for rice(Oryza sativa L.)using DSSAT-CERES-Rice model

Rice is one of the major crops in China,and enhancing the rice yield and water use efficiency is critical to ensuring food security in China.Determining how to optimize a scientific and efficient irrigation and drainage scheme by combining existing technology is currently a hot topic.Crop growth models can be used to assess actual or proposed water management regimes intended to increase water use efficiency and mitigate water shortages.In this study,a CERES-Rice model was calibrated and validated using a two-year field experiment.Four irrigation and drainage treatments were designed for the experiment:alternate wetting and drying(AWD),controlled drainage(CD),controlled irrigation and drainage for a low water level(CID1),and controlled irrigation and drainage for a high water level(CID2).According to the indicators normalized root mean square error(NRMSE)and index of agreement(d),the calibrated CERES-Rice model accurately predicted grain yield(NRMSE=6.67%,d=0.77),,shoot biomass(NRMSE=3.37%,d=0.77),actual evapotranspiration(ETa)(NRMSE=3.83%,d=0.74),irrigation volume(NRMSE=15.56%,d=0.94),and leaf area index(NRMSE=9.69%,d=0.98)over 2 a.The calibrated model was subsequently used to evaluate rice production in response to the four treatments(AWD,CD,CID1,and CID2)under 60 meteorological scenarios which were divided into wet years(22 a),normal years(16 a),and dry years(22 a).Results showed that the yield of AWD was the largest among four treatments in different hydrological years.Relative to that of AWD,the yield of CD,CID1,and CID2 were respectively reduced by 5.7%,2.6%,8.7%in wet years,9.2%,2.3%,8.6% in normal years,and 9.2%,3.8%,3.9% in dry years.However,rainwater use efficiency and irrigation water use efficiency were the greatest for CID2 in different hydrological years.The entropy-weighting TOPSIS model was used to optimize the four water-saving irrigation schemes in terms of water-saving,labor-saving and high-yield,based on the simulation results of the CERES-Rice model in the past 60 a.These results showed that CID1 and AWD were optimal in the wet years,CID1 and CID2 were optimal in the normal and dry years.These results may provide a strong scientific basis for the optimization of water-saving irrigation technology for rice.

Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water shortages.This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers.The proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and bean.The proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real time.This helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop failure.Our dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research centers.The experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,respectively.The new smart greenhouse automation system was also evaluated on four crops with a high turnover rate.The system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.