Irrigation Operation Research Articles

Real-time flood forecasting and uncertainty analysis: a case study of the Krong H’nang hydropower reservoir

ABSTRACT Early and reliable flood forecasting is crucial for mitigating the impacts of floods and ensuring the safe operation of irrigation and hydroelectric facilities. Traditional methods often use a calibrated event-based rainfall-runoff model with a limited number of simulations, making it difficult to measure parameter uncertainties accurately. This paper introduces a new method for estimating the parameters of the HEC-HMS model and evaluating the uncertainties in real-time flood forecasting. First, the Latin Hypercube Sampling (LHS) procedure is used to efficiently sample the parameter values throughout the feasible parameter space. Next, thousands of parameter sets are simulated using a deterministic rainfall-runoff model, generating numerous initial solutions for the Shuffled Complex Evolution (SCE-UA) algorithm to search and evolve. Additional parameter sets are then generated as the search evolves, leading to new simulations until the algorithm reaches the convergence criteria. The optimal solutions are selected based on predefined multiple objective functions. Uncertainty of the forecast results is also computed using the Generalized Likelihood Uncertainty Estimation (GLUE) method and represented as confidence intervals. The method was applied to forecast flood hydrographs at the Krong H’nang hydropower reservoir in Vietnam, using data from 33 flood events from 2016 to 2021. The results demonstrate that the program can effectively forecast the flood hydrograph up to 4 hours in advance (the Kling-Gupta efficiency KGE > 0.8 and the absolute relative volume error |VE| < 10%). The observed values fall within the uncertainty range of Q5%-Q95%. Compared to the conventional method, which uses a fixed parameter set value, this approach is superior and therefore can assist policymakers and operators in more effectively managing reservoir operations.

Reservoir Irrigation Operation Design Based on Dijkstra Algorithm Combined with ACO Algorithm

Reservoir Irrigation Operation Design Based on Dijkstra Algorithm Combined with ACO Algorithm

Downscaling the MODIS land surface temperature using a trapezial concept applied to the MODIS and sentinel 2 images.

Downscaling methods are crucial for accessing high-resolution thermal data simultaneously. The DisTRAD model is commonly used for downscaling thermal images, but changes in soil moisture, such as those caused by irrigation operations, can lead to errors in the process. This study investigated the potential use of TOTRAM and OPTRAM models to reduce errors in LST downscaling in irrigated fields. Sentinel satellite imagery was utilised to enhance the resolution of MODIS Land Surface Temperature (LST) from 1000 to 20 m in the fields of Megsal and Hezarjolfa agro-industrial company in Qazvin province. Soil moisture was estimated using the OPTRAM model, and the results were compared with observational data. The findings indicated that on days with NDVI greater than 0.6, the R2 value exceeded 0.88 and the RMSE value was less than 0.06 cm3/cm3. Then, MODIS LST images were downscaled to 20 m using codes in Google Earth Engine (GEE). Evaluation was conducted using observational data from collected land surface temperature data for 36 points. Comparison of the downscaled LST data with observational data on days with irrigation revealed a decrease in MAE and RMSE error indices by approximately 0.4 and 1.2 degrees Celsius, respectively, in the OPTRAM-TPTRAM model compared to the DisTRAD model. Consequently, the OPTRAM-TOTRAM model generally outperforms the DisTRAD model in LST downscaling. Lastly, it is recommended to assess the TOTARM and OPTRAM models for downscaling MODIS sensor LST in other irrigated fields.

Optimization of Tertiary Irrigation Network Using A Linear Program at D.I. Panggak Darat in Lingga District

The Panggak Darat Irrigation Area in Lingga Regency can irrigate 134.05 ha of rice fields. The problem that often occurs is that irrigation areas are not fully optimal for agricultural product productivity, so that an effort can be made to minimize the shortage of irrigation water and/or minimize the deviation of water release that can result from an optimal pattern of irrigation operations. The purpose of this study is to obtain an optimal operating pattern with better operating performance than the existing (planning). The analytical method used is an optimization technique using the LINDO program (linear programming). The objective function is to minimize the shortage of irrigation water. Evaluation of the operational performance of the optimized and existing reservoirs is carried out using simulation techniques. The results showed that the optimal operating pattern was obtained from the Objective Function with an average total release of reservoir water of 3.20 million m3, which can meet 100% of the irrigation water Requirements of an area of 134.05 ha of irrigation area that must be irrigated. The reliability of the optimization results is in the form of 100% irrigation reliability with a planting intensity of 300% with a rice-rice cropping pattern where the existing (planning) is a rice-rice-bero cropping pattern or a rice-bero cropping pattern with a cropping intensity of 200%. Optimization results from the objective function of minimizing the shortage of irrigation water provide better operating performance than the existing conditions, so as to increase agricultural productivity.

DEVELOPMENT OF A GIS-BASED SYSTEM FOR IRRIGATION MANAGEMENT AND FLOW DISTRIBUTION

Abstract. The systematic management of irrigation operation of medium and large gravity irrigation systems (IS) significantly affects the delivery efficiency of irrigation from the source to the farm level. An interactive spatial database that integrates system data handling and irrigation distribution assessment functionality can be implemented, which has not yet been developed in existing NIS in the Philippines. This study proposes a framework model in developing a geographic information system (GIS) database for irrigation systems that integrates a GIS-based irrigation network distribution analysis model. The creation of a model framework for the development of a functional GIS-based database for IS management and flow distribution is comprised of five stages namely: (1) selection and determination of significant irrigation system data, (2) determination of required estimation parameters, (3) GIS development, (4) irrigation network distribution model development, and (5) selection of applicable water distribution assessment techniques. The GIS-based database and integrated model for irrigation provide essential data records of the irrigation system that could easily be viewed systematically and integrated into any GIS. In addition, data packages can easily be shared and run even on low-end computers. Features of the proposed irrigation model include flow volume simulation, time of delivery simulation, irrigation scheduling, remote sensing model integration for automated evapotranspiration computation, volumetric assessment, assessment of the timing of irrigation, and distribution optimization.

Iot Based Smart Irrigation System using Artificial Intelligence

Sustainable agriculture depends on effective water management, and the Internet of Things (IoT) and artificial intelligence (AI) combine to offer a novel approach to irrigation process optimisation. In order to improve water utilization in agriculture, this research suggests a comprehensive system that includes buzzers, LCD displays, relays, water pumps, soil moisture sensors, AI algorithms, and water pumps. An AI algorithm at the centre of the system evaluates data collected in real time from field-installed soil moisture sensors. These sensors gather information on the soil’s moisture content, which offers important insights into the real water needs of the crops. After analysing this data, the AI programme decides how best to schedule irrigation. The system’s integration with a relay is intended to regulate when a water pump is activated. According to the suggestions of the AI algorithm, the relay provides accurate control over the irrigation operation, enabling on-demand watering. By avoiding over- irrigation and conserving water, this lowers the chance of waterlogging and associated problems. The system has an LCD display to improve user engagement and offer real-time feedback. Important details like irrigation status, soil moisture levels, and AI-driven suggestions are conveyed through the display. With the help of this function, farmers may monitor the system’s performance and make well-informed judgements. Furthermore, a buzzer is incorporated to deliver auditory notifications in the event of crucial occurrences, such low soil moisture levels or system faults.This guarantees timely resolution of problems that might affect crop health and system performance as a whole. A number of benefits are provided by the suggested AI-driven water management system, including improved agricultural output, water conservation, and operational effectiveness. Water scarcity and resource optimisation in irrigated fields are addressed by the system, which offers a comprehensive precision agriculture solution by utilising the capabilities of IoT devices, soil moisture sensors, relays, water pumps, buzzers, and LCD displays

IoT Based Smart Irrigation System

In recent years, the integration of Internet of Things (IoT) technology into agriculture has shown great potential for enhancing irrigation practices, thereby improving crop yield and water efficiency. This paper presents the design, implementation, and performance evaluation of an IoT-based smart irrigation system tailored for agricultural applications. The proposed system employs various sensors to monitor soil moisture levels, weather conditions, and crop water requirements in real time. Data collected from these sensors are transmitted wirelessly to a central control unit, which utilizes an intelligent algorithm to optimize irrigation scheduling and water distribution. The system also incorporates remote monitoring and control capabilities, allowing farmers to access and manage irrigation operations from anywhere via a web or mobile interface. Experimental results demonstrate the effectiveness of the IoT-based smart irrigation system in conserving water, reducing energy consumption, and enhancing crop productivity. Moreover, the scalability, reliability, and cost-effectiveness of the proposed solution make it suitable for deployment in both small-scale and large- scale agricultural settings. Key Words: IoT, smart irrigation, agriculture, soil moisture sensor, wireless communication, optimization, remote monitoring

Energy Budgeting and Efficiency Analysis of Organic Cotton: A DEA Approach

Background: Cotton (Gossypium hirsutum L.) is crop of economic importance in global and national scale. India is the greatest producer of quality cotton, the third largest exporter and the second largest consumer. Conventional cotton farming creates environmental pressures and negative impact on public health. Organic farming is the sustainability option in cotton production. Cotton is the energy intensive crop, which uses high amount of input energy to produce seed cotton. Hence, energy budgeting is highly essential for better energy utilization and resource conservation and also measuring the input use efficiency of a farm/agronomic practices is vitally important in present day Indian agriculture. Methods: Sensing the economic importance of cotton, ten number of eco-friendly, ecologically safe organic nutrient management (ONM) practices were framed and field experiment was conducted in split plot design with two main plots (M) and five subplots (S) and replicated thrice. The direct and indirect energy used in different ONM practices were computed and energy coefficients were computed from energy equivalents and that has been used as inputs to generate efficiency coefficients by using an input oriented DEA approach. Result: Energy budgeting on field operation basis reported that, the field preparation and irrigation operation uses most of the energy (in non-renewable forms) which needs attention and there is the scope to find alternative energy conservation systems. Results on input use efficiency reported that the ONM practice, double green manuring followed by cotton and application of well decomposed poultry manure and foliar application of fermented fish extract at 5% concentration at 25 and 35 DAS are 100% efficient in terms of technical CRS, technical VRS, scale, allocative and cost efficiencies. This organic nutrient management practice would produce optimum output from the least amount of input and would be ideal for sustainable cotton production.

Community-based irrigation management in Indonesia (Case study: Musi Rawas Regency, Bandung Regency, and Soppeng Regency)

Indonesia is an agricultural country that relies on the food sector.Irrigation infrastructure plays an important role to support the sector. It is therefore the sustainability of irrigation is vital. Community-based management of irrigation has been developed in Indonesia to support the development, operation, and and maintenance of irrigation. The aim of this study is to explore community-based irrigation management to support the sustainability of irrigation infrastructure. Three locations were choosed as case study; Musi Rawas, Bandung, and Soppeng Regency. The data analysis method used is qualitative analysis. The results of this study indicate that community groups are involved in determining irrigation development, such as holding public consultations, site selection, surveys, land acquisition, cooperation, operation, and maintenance. However, this community group does not yet have a robust organizational role because it has not provided rules, sanctions, or solutions to solve problems in the field. In addition, there is a lack of government support for community groups. Realizing a sustainable community-based irrigation system requires a high role of the community in irrigation management and support from the government in supporting the capacity of community groups and financing participatory activities.

Study of some fertility characteristics of soils with different agricultural uses in the Aqra Region/Duhok

A study was conducted in the Aqra region to investigate the fertility characteristics of soils with different agricultural uses. The study included (40) sites, representing surface soils (0-30 cm). Soil samples were transported to the laboratory for physical and chemical analyses. The results showed that the majority of the studied soils were heavy soils with clayey texture (C) to clay loam texture (CL) and sandy clay loam texture (SCL). The pH values were mostly neutral in specific sites and slightly alkaline in most of the studied sites, Nitrogen values ranged between (6.27 – 43.90) ppm. Phosphorus values ranged between (1.05– 30.56) ppm, and it is natural for phosphorus dissolution to decrease due to plant absorption, adsorption, and fixation over time. This indicates a significant consumption of phosphorus in the soil, possibly attributed to elevated temperatures at the end of the season and reduced irrigation operations before harvest, leading to decreased phosphorus dissolution and increased fixation. The ratios of N in Kriging ranged between (27.44 - 32.61) represented the majority of the studied area, while he P for the first five categories ranged through the program between (1.05-9.70). and K for rest of study sites had low and variable values, as they appeared in red and light yellow, and their values ranged between (190 - 374).

The effect of irrigation water quality on the growth of maize plants, electric conductivity and pH of the soil

The research aims to determine the response of the Maize crop to irrigation with salt water during the growth stages, and to study the accumulation of salts in the soil, the degree of their interaction, and the efficiency of water use. Three types of irrigation water with salt concentrations (1.5, 4.5, 6.5) dSm-1 with three replicates for every kind water. The Randomized Complete Blocks Design was used in the experiment, and it was statistically analyzed using SPSS. Statistically significant differences were found at the 5% level according to Duncan's method. Statistical analysis showed that there were significant differences attributed to the salinity of irrigation water at the level of 4.5 dsm-1 (T2), which caused about 50% damage in plant height, root growth, leaf area per cob length, weight of 500 seeds, grain yield, and soil. Compared to salinity if irrigated with 1.5 dsm-1 (T1) salinity of river water. Using water with a salinity of 6.5 dsm-1 (T3) as wastewater resulted in a reduction of all apparent plant characteristics by 75%. An increase in soil salinity was also observed at the end of the experiment compared to its salinity at the beginning of the experiment, and this damage increases with an increase in the electrical conductivity of the water used in irrigation operations. It has been observed that soil PH decreases with increasing soil salinity.

Proposal of a Model of Irrigation Operations Management for Exploring the Factors That Can Affect the Adoption of Precision Agriculture in the Context of Agriculture 4.0

Agriculture is undergoing a profound change related to Agriculture 4.0 development and Precision Agriculture adoption, which is occurring at a slower pace than expected despite the abundant literature on the factors explaining this adoption. This work explores the factors related to agricultural Operations Management, farmer behavior, and the farmer mental model, topics little explored in the literature, by applying the Theory of Planned Behavior. Considering the exploratory nature of this work, an exploratory multi-method is applied, consisting of expert interviews, case studies, and modeling. This study’s contributions are a list of factors that can affect this adoption, which complements previous studies, theoretical propositions on the relationships between these factors and this adoption, and a model of irrigation Operations Management built based on these factors and these propositions. This model provides a theoretical framework to study the identified factors, the relationships between them, the theoretical propositions, and the adoption of Precision Agriculture. Furthermore, the results of case studies allow us to explore the relationships between adoption, educational level, and training. The identified factors and the model contribute to broadening the understanding of Precision Agriculture adoption, adding Operations Management and the farmer mental model to previous studies. A future research agenda is formulated to direct future studies.

Smart Agriculture – Automatic Monitoring of Soil Moisture and Irrigation Control for Farming Land

In this article, we proposed an integrated application of automatic moisture and Irrigation control using Real-Time Clock (RTC) and Light-Dependent Resistor (LDR). The main idea of this paper is predicated on using as little energy as possible. In this work, we use the DS1307 real-time clock module to automatically switch on or off motor dependent on the time of day. The sensors are used for monitoring the soil conditions in agriculture field. Programming controls the timing of when the device is active. The most significant benefit of the proposed design is the reduction of risk associated with potential crashes and save water. Motors on the farm are often switched on at dry time and left OFF on wet conditions till necessity. Their operation is entirely automatic. Because of the protection of the farmers from the electrical shock during the rainy season, this proposed design intends to automate the operation of soil monitoring and irrigation in order to reduce water, power consumption and advance technological progress in agriculture farms and support in smart India. A significant amount of electricity is lost in the typical lighting system; however, this may be prevented with automatic control employing LDR.

Современные экологические проблемы эксплуатации коллекторно-дренажной сети и пути их решения

Purpose: to determine the environmental issues that arose during the operation of irrigation and drainage systems, and the basic principles of managing the quality of drainage water from irrigated lands. Discussion. Analytical data on the condition of reclaimed lands, changes in groundwater levels during the operation of irrigation systems, the use of water resources, the results of theoretical and experimental studies of the collector-drainage flow quality in the irrigation system diversion collectors in the south of Russia are considered. The main problems are identified: land resources degradation, rising groundwater levels, resalinization and salt accumulation during irrigation, especially with irrigation water with increased mineralization, changes in the hydrological and hydrochemical regime of water bodies – collector-drainage runoff intakes. Legal problems associated with the classification of irrigation and drainage systems as objects of category III are highlighted. Ways to solve environmental problems, methods for managing the quality of drainage runoff, including the creation of local treatment facilities based on routing the collector-drainage network to irrigated areas suspended from the drainage system, in the range of 10–200 ha with construction of storage drainage sumps as places for metering flow and regulating the chemical composition of drainage flow. Conclusions. Pollution problems are regional in nature and are determined not only by economic activities, but also by natural factors. The set of irrigation canals, collector-drainage networks and water intakes of drainage-discharge waters participates in the inter-basin redistribution of surface and underground runoff. In the process of managing the quality of drainage water from irrigated lands and the environmentally safe operation of hydro-reclamation systems, three parties must certainly participate: reclamation departments, agricultural producers and government agencies.

IoT and Cloud Based Sustainable Smart Irrigation System

This paper addresses the concerns in irrigation mechanisms and employs a Sensors and cloud-based platform to monitor and control an irrigation system. In order to monitor the soil’s moisture level in real time, moisture sensors are placed in the field. These sensors wirelessly provide data to the NodeMCU, which processes and relays the information. The NodeMCU gathers the sensor data and processes it, considering standard deviations and crop-specific parameters. Using this information, the system starts the water pump and opens the solenoid valves to begin the irrigation operation. The NodeMCU coordinates with these valve-opening devices via wireless communication. In addition, the system uses online weather prediction data to provide more precise watering schedule optimisation. The system dynamically modifies the irrigation schedule based on analysis of weather patterns, evapotranspiration rates, and crop water needs to save water during times of rain or high humidity. The solution makes use of cloud-based platforms to improve scalability and accessibility. The gathered sensor data and command instructions are safely transferred to the cloud server. This allows farmers to remotely check on and adjust the irrigation system by means of the web or mobile apps. In addition, data analytics methods may be used to infer information and provide suggestions for improved methods of water management and crop care. In conclusion, the Internet of Things (IoT) NodeMCU smart irrigation system provides an automated and intelligent approach to water management in agriculture. Water is saved, crop yields are boosted, and sustainability is enhanced thanks to the capacity to irrigate precisely based on real-time soil moisture data, weather predictions, and crop needs.

IOT BASED SOLAR POWERED PUMP FOR AGRICULTURAL IRRIGATION AND CONTROL SYSTEM

This paper focuses on the implementation of a solar-powered pump system integrated with IoT technology for agricultural irrigation control. By leveraging the properties of the system, such as humidity, temperature, soil moisture, time collection based on solar panel activity, and peculiarities like using a mobile app and automated control, the project aims to provide an efficient and sustainable solution for irrigation.The system utilizes humidity sensors strategically placed in the soil to monitor moisture levels, enabling precise irrigation control. To enhance user experience and accessibility, a user-friendly mobile app is developed. This app allows farmers to remotely monitor and control the irrigation process, providing real-time data, irrigation scheduling options, and the ability to adjust system settings. Through IoT integration, the system enables automated control of the pump based on predefined parameters. By analyzing sensor data, including humidity levels and solar panel activity, the system autonomously adjusts irrigation operations, reducing manual intervention and improving overall efficiency. From the acquired results, at a temperature of 37.2 0C, the humidity level is low at 49.6%, thereby activating the irrigation pump which resulted to decrease in temperature at 26.70C after a period of 1hr 48mins with the humidity, and soil moisture levels at 73.9%, and 88% respectively. The developed system monitors and balance the soil moisture level through automated irrigation process which is solar powered and remotely managed.

IOT BASED WATER QUALITY MONITORING SYSTEM

Water is one of the most vital resources for life, and protecting its quality is crucial for both the environment's health and the well-being of people. Monitoring and preserving the quality of the water is now essential given the mounting problems caused by pollution, climate change, and expanding populations. In this environment, the Internet of Things (IoT) has emerged as a ground-breaking innovation that has the potential to revolutionize the way we manage and monitor water quality. An IoT-based water quality monitoring system's relevance, components, advantages, and prospective applications are highlighted. It offers a chance to proactively address problems with water quality, protecting both the environment and the general people. Sensors used in IoT-based water quality monitoring systems can measure a wide range of factors, including temperature, dissolved oxygen, turbidity, pH levels, and chemical concentrations. These sensors are interfaced with the microcontroller unit. The sensors are positioned in pipelines, treatment facilities, or water bodies. Through wireless communication protocols like Wi-Fi or cellular networks, the data gathered by sensors is sent to a central server or cloud platform. This makes it possible to access and analyses data in real time. Users can use online or mobile applications to get the data on the quality of the water. These intuitive user interfaces offer up-to-the-minute data, historical trends, and alarms if water quality measurements differ from predetermined criteria. Urban populations can have access to safe drinking water by using IoT systems at water treatment facilities to monitor and manage water quality indicators. Aquaculture farms' production is increased by IoT- based systems' assistance in maintaining ideal water conditions for fish and other aquatic species. Monitoring soil and water quality helps farmers improve irrigation operations, which increases agricultural yields and decreases water waste. IoT is an excellent instrument for preserving water resources, defending the environment, and delivering real-time data, affordable solutions, and data-driven decision-making. Key Words: Microcontroller, Pumping station, Sensor, IoT, data, aquaculture.

IOT-Based Smart Irrigation System using DTMF and 8051 MCU

Abstract: In almost every facet of agriculture and gardening, irrigation is the cornerstone activity, with the regular watering of plants and crops paramount to their growth and well-being. However, this essential task often proves burdensome for individuals preoccupied with daily commitments. For farmers, the significance of water supply is further emphasized, as rainwater assumes a critical role in sustaining their crops. While relying on the unpredictability of monsoon rains, canals, and groundwater resources, farmers face the challenges of ensuring a consistent and appropriate water supply, as each crop type necessitates distinct watering regimens. Any insufficiency in providing water to crops can lead to devastating consequences, resulting in the potential destruction of crops and plants. Addressing these challenges and aiming to revolutionize irrigation practices, this paper proposes an IOT-based Smart Irrigation System using DTMF (Dual Tone Multi-Frequency) and 8051 MCU (Microcontroller Unit) as an innovative and transformative solution. This cutting-edge system combines the power of DTMF technology with the versatility of the 8051 microcontrollers, enabling remote control and monitoring of the irrigation process. By leveraging the interactive capabilities of mobile or landline phones, the system allows users, including farmers and gardeners, to conveniently manage irrigation operations from a distance, reducing the need for constant physical presence. This intelligent irrigation system exhibits applicability in automating the irrigation processes of various settings, including gardens, nurseries, and greenhouses. By carefully regulating the water supply to plants based on specific predefined conditions, the system ensures the provision of an optimal and essential amount of water. As a result, the system achieves energy conservation while effectively mitigating any potential water scarcity issues.

Using the SWAT+ model to assess the conditions of water inflow to a reservoir in an uncontrolled agricultural catchment. Case Study of the Nanan Reservoir in the Lake Taabo catchment (Côte d'Ivoire)

In this study, the agro-hydrological model Soil and Water Assessment Tool + (SWAT+) is used to simulate runoff in an uncontrolled agricultural catchment with paddy fields irrigated by plots submersion. The objectives are to evaluate the ability of this new, improved version of the SWAT model to reproduce the flows in a complex catchment and to analyse the evolution of these flows over the historical period 1986-2020 (35 years). Sensitivity analysis, calibration and validation were carried out with monthly flow data. The decision tables for irrigation operations and reservoir water release were filled in the SWAT+ Editor to establish the reservoir water balance. The Mann-Kendall trend test with the threshold of the standard normal statistic = 1.96, was applied to the time series of flows to the Nanan Reservoir. The results reveal that SWAT+ is more sensitive to eight parameters, including the new CN3_SWF parameter (-0.04329), which gave the user control of the soil saturation level. Good performances were obtained during calibration and validation, respectively: NSE (0.78; 0.69); R² (0.81; 0.70); PBIAS (-18.58; -23.9) and RSR (0.47; 0.56), with a tendency to slightly overestimate flows. The analysis shows that flows to the Nanan Reservoir are highly variable yearly, with a non-significant upward trend (|Z|=1.3<1.96) from 1986 to 2020. The water balance reveals that the runoff inflow generated will compensate for water withdrawals for irrigation of the Nanan paddy scheme during this period. Furthermore, this study provides a methodological framework for SWAT+ reservoir model calibration in an uncontrolled catchment.

Adopting Smart Integrated AgriAquaculture IAA Techniques: A Sustainable Approach by Al-Furat Al-Awsat Technical University

In light of recent developments in urban agriculture and the ongoing effects of climate change, there is a growing need for innovative irrigation practices that do not require potable water. On the other hand, significant environmental hazards are associated with the effective usage, treatment, and disposal of ever-increasing quantities of wastewater and sewage sludge in our modern times. Therefore, wastewater usage in irrigation might provide a potential alternative approach. In this regard, the water drained from fish farms is one type of wastewater capable of being employed in irrigation operations in a way that is both effective and helpful.This article highlights the efforts made by Al-Furat Al-Awsat Technical University to establish intelligent integrated AgriAquaculture (IAA) systems on its campuses as part of its strategies to achieve sustainable goals. These efforts are part of Al-Furat Al-Awsat Technical University’s more enormous efforts to achieve its goals.