Traditional Irrigation Methods Research Articles

Exploiting the power of stepwise intraoperative irrigant activation to maximize oval canal disinfection: an ex-vivo investigation

ObjectivesTo evaluate the antibacterial effectiveness of stepwise intraoperative activation of the root canal irrigant, both alone and when combined with conventional ultrasonic activation, compared to the traditional syringe irrigation method in oval root canals.MethodsSixty single-rooted maxillary second premolars were selected according to specific criteria. After creating an access opening and root canal patency in all samples, a cycle of autoclave sterilization was conducted. Enterococcus faecalis suspension was inoculated into the root canals and incubated at 37 °C for 21 days. After bacterial contamination, the specimens were assigned to four experimental groups (n = 10 per group): conventional syringe irrigation (CSI); stepwise intraoperative activation (SIA), consisting of the ultrasonic activation of 2 mL of 5.25% NaOCl solution for 20 s following irrigation between instrumentation files; conventional ultrasonic irrigation (CUI), consisting of the passive intermittent ultrasonic activation of 2 mL of 5.25% NaOCl in three successive cycles of 20 s each at the end of the mechanical instrumentation; and stepwise intraoperative activation plus conventional ultrasonic irrigation (SIA + CUI), consisting of a combination of SIA and CUI; in addition to positive control; and negative control groups (n = 10). At the end of the chemo-mechanical preparation, a bacterial sampling was conducted to determine the number of colony-forming units per mL (CFU//mL), and the outcomes were compared with one-way ANOVA and Games-Howell post hoc test with the significance level set at 5%.ResultsNone of the tested irrigation protocols were able to fully eliminate the intracanal bacterial infection. However, all protocols succeeded in reducing the bacterial burden within the oval root canals considerably (p <.05). The combination of SIA and CUI achieved the greatest bacterial reduction (1.85 ± 0.99), followed by the CUI (3.00 ± 0.13), SIA (3.54 ± 0.26), and CSI (4.29 ± 0.16) groups, respectively, with significant differences between them (p <.05).ConclusionsStepwise intraoperative irrigant activation was able to reduce the bacterial load in the oval root canals significantly compared to the basic chemo-mechanical preparation, with maximal disinfection achieved when combined with conventional ultrasonic irrigation.Clinical relevanceThe complex morphology of oval root canals presents significant challenges for effective disinfection during root canal treatment. To optimize the disinfection process, it would be essential to implement supplementary techniques in conjunction with standard root canal preparation. Employing a stepwise intraoperative irrigation activation method can enhance disinfection efficacy in these convoluted anatomies, particularly when integrated with traditional ultrasonic irrigation, potentially leading to improved treatment outcomes.Clinical trial numberNon-applicable. Conducting the current experiment was limited to the approval of the local Research Ethics Committee at the Faculty of Dentistry, Minia University, Egypt (Committee No. 105, registration No. 903).

Criterion-Based Evaluation of Water Management Performance of Irrigation Cooperatives in Pasinler District, Erzurum

The performance of irrigation cooperatives is pivotal in ensuring sustainable water management in agriculture-dependent regions. Pasinler district, located in the Eastern Anatolia Region of Türkiye, is one of the key agricultural production areas, with the local economy largely reliant on agriculture and livestock. In this context, the efficiency of irrigation systems plays a critical role not only in enhancing agricultural productivity but also in improving the quality of life and socioeconomic well-being of the local population. This study aims to assess the water management performance of irrigation cooperatives in the Pasinler district of Erzurum, Türkiye, based on five critical dimensions: physical and technical infrastructure, land and irrigation area management, irrigation methods and practices, operational maintenance and management, and economic-financial management. Data were collected from nine cooperatives operating in the region through structured evaluations and cooperative records. Findings reveal significant disparities in infrastructure capacity, with large cooperatives such as Pasu owning up to 120 wells and pumps, whereas others, like Alvar and Porsuk, operate with minimal or no infrastructure. Despite differences in scale, all cooperatives rely on traditional surface irrigation methods, indicating low water-use efficiency. Furthermore, none of the cooperatives have undergone land consolidation, leading to inefficient irrigation practices due to fragmented land structures. Most cooperatives depend on electricity for irrigation, yet lack renewable energy integration, creating financial vulnerabilities amid rising energy costs. Additionally, the absence of digital payment systems and structured fee collection mechanisms undermines financial transparency and sustainability, with some cooperatives consistently operating at a loss. This study underscores the necessity of targeted improvements in infrastructure modernization, renewable energy integration, institutional capacity building, and financial governance. Encouraging land consolidation, adopting smart irrigation technologies, and strengthening managerial frameworks are essential steps toward enhancing irrigation efficiency and agricultural sustainability. These findings suggest that future interventions should prioritize integrated policy frameworks that align technical upgrades with institutional and financial reforms to ensure the long-term viability of irrigation cooperatives.

Evaluating Farmers’ Groundwater Conservation Constraints Using Quantitative Ranking Tools

Despite possessing substantial freshwater reserves, India is facing a severe and escalating water crisis, particularly in the agricultural sector, which consumes nearly 85% of the nation’s total water resources. This study systematically investigates the barriers to the adoption of groundwater conservation practices among farmers. Employing a mixed-methods approach, data were gathered from 180 farmers in water-intensive regions using structured questionnaires, focus group discussions, and key informant interviews. Two analytical tools: Garrett’s Ranking Technique and the Rank-Based Quotient (RBQ) method, were used to prioritize the constraints as perceived by the respondents. The analysis revealed critical challenges across five domains: demographic and livelihood-related factors, technological accessibility, ecological and resource constraints, economic limitations, and governance support. Key barriers included inter-generational knowledge gaps (RBQ 93.19), dependence on traditional irrigation methods (Garrett score 71.50), market volatility (Garrett score 68.50), erratic climatic conditions, and insufficient policy incentives (RBQ 91.60). The findings underscore the urgent need for integrated policy reforms, strengthened agricultural extension services, and farmer-centric support systems to facilitate sustainable groundwater management in Indian agriculture.

Design of an improved graph-based model integrating LSTM, LoRaWAN, and blockchain for smart agriculture

This research is anchored on the burning need for irrigation optimization and crop water use efficiency improvement, which remains a challenge in smart agriculture processes. Traditional irrigation methods normally lead to inefficiency, resulting in wasted water and non-maximum crops. These traditional ways normally lack attributes of real-time adaptability and secure data management—things that are very key to modernizing agricultural practices. In this work, artificial intelligence (AI), Internet of Things (IoT), and blockchain techniques will be integrated to design a comprehensive system for monitoring and predicting soil moisture levels. In the proposed model, long short-term memory (LSTM) networks are considered for soil moisture level prediction, taking into consideration past data, weather, and crop type. LSTM networks are chosen here for their high performance in timestamp series prediction tasks with an mean average error (MAE) of 0.02 m3/m3 over a 7-day forecast horizon. For real-time monitoring, IoT sensors based on long range wide area network (LoRaWAN) technology are field-deployed for conducting long-range communications while consuming very limited energy to extend the sensor battery life over 5 years and bring down the data transmission latency below 5 s. It has an inbuilt permissioned blockchain framework—Hyperledger Fabric—which offers a secure and transparent system for data management and maintaining a record of soil moisture data, irrigation events, and metadata from sensors. This ensures the immutability and integrity of sets of data. Smart contracts automate irrigation upon reaching preconfigured soil moisture thresholds, and hence zero data integrity breaches occur with a transaction throughput of 1,000 transactions per second, taken into view with smart contract execution latency of less than 2 s. Moreover, it utilizes reinforcement learning with Deep Q-Learning to derive an optimized irrigation schedule. In this regard, it enables learning optimal irrigation policies and implements them to improve efficiency in the usage of water by 25% and increases crop yield by 15% compared to the traditional methods. Clearly from field trials, results indicate evident efficiency of the integrated system: a 20% water usage reduction and a 12% increase in crop yield within one growing season. This is rather an innovative take on irrigation practices, increasing a great deal of accuracy and sustainability for such and providing a really strong solution toward better agricultural productivity and resource management.

Smart Irrigation System Using IoT

Agriculture plays a crucial role in global food security and economic stability. However, traditional irrigation methods often result in water wastage, inefficient crop hydration, and high labour costs. As water scarcity continues to rise, it is essential to adopt sustainable solutions that optimize irrigation practices. This paper presents an IoT-based Smart Irrigation System, designed to automate and enhance water management in agriculture. The system integrates soil moisture sensors, temperature sensors, humidity sensors, and rain sensors, feeding real-time environmental data to an Arduino UNO microcontroller. Based on preset thresholds, the system activates the water pump only when required, reducing excessive water consumption. Additionally, a timer-based scheduling feature ensures irrigation continuity even when sensor data is unavailable. To protect crops from damage, an ultrasonic animal repellent buzzer is incorporated into the system. This IoT-driven approach successfully minimizes manual intervention, improves agricultural efficiency, and supports sustainable farming practices. The implementation of cloud-based monitoring allows farmers to remotely control irrigation, ensuring transparency and control over water distribution.

Determination of water quality characteristics of Süphan and Hıdırmenteş Lakes (Van-Eastern Türkiye)

This study investigated the physical and chemical properties of the waters of Süphan and Hıdırmenteş Lakes in Van province, Eastern Turkey. Water samples were collected between November 2022 and August 2023 covering three seasons: autumn, spring, and summer. A total of 38 parameters from each lake were analyzed. The average parameter values were evaluated according to water quality classes. For Hıdırmenteş Lake, the average water temperature was 12.74°C, salinity was 0.40 mg L-1, electrical conductivity (EC) was 785.12 μS/cm, suspended solids (SS) was 181.01 mg L-1, turbidity was 152.33 NTU, pH was 8.17, dissolved oxygen (DO) was 8.14 mg L-1, nitrite (NO2–) was 0.01 mg L-1, nitrate (NO3–) was 6.23 mg L-1, ammonia (NH3) was 0.57 mg L-1, ammonium (NH4) was 0.60 mg L-1, phosphate (PO4-3) was 1.22 mg L-1, phosphorus (P) was 0.41 mg L-1, and iron (Fe+2) was 2.877 mg L-1. For Süphan Lake, the average water temperature was 14.57°C, salinity was 0.23 mg L-1, EC was 453.13 μS/cm, SS was 156.00 mg L-1, turbidity was 125.33 NTU, pH was 8.35, DO was 8.19 mg L-1, NO2- was 0.12 mg L-1, NO3– was 8.03 mg L-1, NH3 was 0.96 mg L-1, NH4 was 1.01 mg L-1, PO4-3 was 0.03 mg L-1, P was 0.01 mg L-1, and Fe+2 was 0.377 mg L-1. The water parameters, except for suspended solids, were suitable for agricultural use. According to the Regulation on the Protection and Improvement of waters where trout and carp fish species live, the phosphate, ammonia, and SS in Hıdırmenteş Lake, and the ammonia, SS, and nitrite in Süphan Lake, were not suitable for trout and carp production. To prevent water waste, modern irrigation systems should be used instead of traditional flood irrigation methods. Additionally, farmers should be educated about this issue and encouraged to adopt modern irrigation techniques.

AI-Driven Predictive Irrigation: Revolutionizing Agricultural Water Management

Water scarcity and inefficient irrigation practices pose significant challenges to modern agriculture, especially in arid regions. Traditional irrigation methods often result in excessive water consumption, higher operational costs, and lower crop yields. This research paper investigates the potential of Artificial Intelligence (AI) in predictive irrigation systems, emphasizing how AI algorithms can optimize irrigation schedules by analyzing factors such as weather forecasts, soil moisture levels, and crop water requirements. By leveraging real-time and historical data, AI-driven irrigation systems improve water efficiency, conserve resources, and support sustainable farming practices. This paper reviews existing AI techniques in predictive irrigation, discusses their advantages and challenges, explores future developments, and examines the broader impact of AI on agricultural water management.

OPTIMALISASI IRIGASI SPRINKLER UNTUK MENINGKATKAN PERTUMBUHAN DAN HASIL BAWANG MERAH (Allium cepa L.)

Water is an important component in shallot cultivation activities, optimal water availability is very necessary in shallot cultivation activities, especially in the early stages of growth and bulb formation. Traditional irrigation methods tend to be wasteful and unpublished in air distribution which can result in decreased yields, especially in cultivation activities carried out in areas with limited air. Sprinkler irrigation appears as a potential solution to optimize air distribution through a spraying mechanism that allows for more even air distribution. The research design used was a one-factorial Completely Randomized Design with five replications. Irrigation carried out in this study was in the form of traditional control 100% manual irrigation needs (I0), sprinkler 75% manual irrigation volume (I1), sprinkler 100% manual irrigation volume (I2), sprinkler 150% manual irrigation volume (I3) and sprinkler 200% manual irrigation volume (I4) which were given once in the morning at 06.00 -07.00. This study aims to analyze the effect of the use of a sprinkler irrigation system on shallot productivity. The results showed that sprinkler irrigation with a volume of 200% of the manual method gave the best results on plant height, number of tillers, dry weight of the stump, and diameter of the shallot bulb. In contrast, the traditional irrigation method with 100% manual volume produced the lowest bulb weight loss. Sprinkler application with 200% manual irrigation volume can significantly increase shallot production.

Evaluation of Pomegranate Production with Advanced Irrigation Techniques

Recently, pomegranate has become a popular crop in tropical and subtropical areas. Due to a lack of knowledge of irrigation, farmers cannot decide what is best suitable for pomegranate. Aim of this study is to find out the appropriate method of pomegranate irrigation to achieve maximum yield and water productivity. An experimental study was carried out for three crop years (2020-2022) on five irrigation methods, such as free flooding (FF), drip irrigation with two emitters (DI2), drip irrigation with three emitters (DI3), micro irrigation with one lateral (MI1), and micro-irrigation with two laterals (MI2). Climatic data is collected from nearby weather stations, and using CROPWAT-8.0 models, the crop water requirement is determined. Soil moisture is measured using sensors, and soil biochemical properties are checked in the laboratory. The suitability of the irrigation method has checked using parameters such as yield per plant, fruit weight, water productivity, and benefit-cost analysis. From the results, it has been found that drip irrigation with three emitters shows 55% more yield, 65% more fruit weight, and 64% more water productivity as compared to traditional irrigation methods. The benefit-cost ratio is highest in the DI3 method. Hence, this method is recommended for pomegranate crops.

IOT-Based Smart Irrigation System: A Sustainable Solution for Modern Agriculture

ABSTRACT Agriculture plays a vital role in the economies of many nations. However, traditional irrigation methods often waste water and lack precision[1]. To address these challenges, this project introduces a smart irrigation system powered by IoT technology[14]. The system uses a network of sensors and actuators to monitor key factors like soil moisture, temperature, humidity, and weather conditions. This data is sent to a central control unit, where intelligent algorithms determine the optimal timing and amount of water needed for irrigation. Designed to be energy-efficient and cost-effective, the system operates on a standard power supply while offering significant advantages over conventional methods. By minimizing water waste, it helps conserve valuable water resources, boosts crop yields, and lowers operational costs. The system is also user-friendly, with simple installation, operation, and maintenance[2]. It can be customized for different crops and soil types and scaled up to cover larger fields or multiple farming zones. In summary, this IoT-based smart irrigation system is a sustainable and efficient upgrade from traditional methods. Its scalability, energy efficiency[8], and adaptability make it an excellent choice for farmers seeking modern, technology-driven solutions to enhance their irrigation practices[5]. Keywords: agriculture, irrigation, IoT, sensors, actuators, temperature, humidity, weather conditions, energy-efficient, cost-effective, water conservation, crop yields, scalability, customization.

Implementation of a wireless sensor network for irrigation management in drip irrigation systems

Water scarcity and inefficient irrigation practices pose significant challenges to modern agriculture, particularly in semi-arid regions. Traditional irrigation methods often lead to excessive water consumption and uneven water distribution, reducing overall efficiency and sustainability. Effective water management in irrigated agriculture is crucial for maintaining high crop productivity and alleviating water scarcity. To improve water management techniques, the integration of drip irrigation systems with wireless sensor networks (WSNs) was investigated herein. In the proposed system, valve control and soil moisture monitoring nodes were added to obtain real-time data on irrigation water volume (IWV), flowrate, pipe inlet pressure, and soil moisture within the WSN. The hardware and software designs of the soil moisture monitoring and valve control nodes were created, including the selection of components, specific functionalities of software algorithms, and communication protocols used for data transmission. A field test was performed in an olive orchard to assess the performance of the proposed system during the irrigation season. The results showed that soil moisture content varied between 42.0% and 24.4%, 44.6% and 25.9%, and 46.7% and 30.2% at different depths, validating the system’s capability to optimize irrigation scheduling. The system’s automated valve control capability enabled the precise application of irrigation water volume of 2,209 m3 throughout the irrigation season. Flowrate stabilization at approximately 2.8 m3/h and real-time pressure monitoring enabled early detection of anomalies such as clogging or low water supply, enhancing irrigation efficiency. The results of this study highlight the potential of WSNs in improving water management practices for irrigation. By providing real-time data and remote-control capabilities, the integrated system offers a tool for optimizing water usage and conserving water resources.

IoT: Automatic Plant Watering System using Android

Water scarcity and inefficient irrigation practices pose significant challenges in agriculture and home gardening. Traditional irrigation methods often lead to overwatering or under watering, affecting plant health and wasting resources. This research presents an IoT-based Automatic Plant Watering System, integrating NodeMCU ESP8266, multiple sensors, and a Flutter-based mobile application to optimize irrigation. The system uses a soil moisture sensor to determine water needs, a DHT11 sensor for temperature and humidity monitoring, an ultrasonic sensor for water level detection, and a relay module to control the water pump. Sensor data is stored in an SQL database and displayed in real-time on the mobile app, allowing users to monitor conditions remotely. The proposed system enhances water efficiency, reduces manual effort, and promotes smart gardening solutions. Experimental results show that this system reduces water usage by 30-50%, making it a sustainable and cost-effective approach to irrigation

Integration of IoT-based, sprinklers, embedded systems, data and cloud computing for smart irrigation management

The increasing demand for sustainable water management in agriculture has led to the adoption of Internet of Things (IoT)-based smart irrigation systems that integrate sprinklers, embedded systems, data analytics, and cloud computing. Traditional irrigation methods often result in water wastage, inefficient resource allocation, and limited real-time monitoring capabilities. The integration of IoT sensors, automated sprinklers, and real-time data processing allows for precise irrigation scheduling, adaptive water distribution, and improved crop yield optimization. This study explores a multi-layered smart irrigation framework, incorporating sensor networks, embedded controllers, cloud-based analytics, and decision support systems for optimized water management. The proposed system leverages IoT-enabled soil moisture sensors, weather data integration, and AI-driven predictive analytics to enhance real-time decision-making. Cloud computing facilitates remote monitoring, data aggregation, and automated irrigation control, reducing manual intervention while ensuring efficient water usage. Embedded systems act as intermediaries between IoT devices and cloud platforms, executing real-time control logic based on environmental conditions. Machine learning models further enhance predictive irrigation, analyzing historical and real-time data to optimize water consumption patterns. This research evaluates the effectiveness of IoT-based irrigation systems through case studies and performance benchmarking, highlighting improvements in water efficiency, energy savings, and operational scalability. Findings indicate that integrating IoT, cloud computing, and embedded control mechanisms in irrigation management enhances precision agriculture and sustainability. Future advancements in 5G connectivity, blockchain security, and federated learning for decentralized IoT networks are also discussed to ensure secure and scalable implementations.

Major Crops Water Requirements and Automated Irrigation Scheduling System

Agriculture is a critical factor that impacts a country's economy. The agriculture sector uses 70% of the available fresh water. There are challenges in water management and irrigation scheduling that require resolution. Farmers are using traditional irrigation methods that use a lot of water with low water efficiency. Smart irrigation and farm management technology is crucial to sustainable agriculture, as it saves water and provides farmers with more information about crop water requirements. However, managing irrigation water is a complex task that depends on factors such as soil, weather, and environment. Robust modeling is necessary to accurately estimate the water requirements of a crop. In this we developed a smart irrigation model to automate the irrigation system according to water requirements of crops. To estimate the water requirements of crops a review was done on different crop water requirements and crops features. To develop the automated irrigation system an analysis is done on different irrigation methods, irrigation scheduling and requirements of irrigation scheduling. The proposed system is used to automated irrigation system and real time data is sent to think speak server for regular monitoring. The developed automated irrigation system is working up to expectations and help farmers to control the irrigation and conserve water by avoiding over irrigation.

Smart Agriculture Irrigation System using IoT

Employment of efficient water management is a very critical component of modern agriculture, particularly in regions evidencing acute water scarcity and unpredictable climatic conditions. Traditional methods of irrigation result in the wastage of water, with nearly unachievable crop yields due to the lack of preciseness in water application. This paper, therefore, presents the design and development of a Smart Agriculture Irrigation. It automatically perceives the soil moisture, weather conditions, and crop water requirements for the scheduling and control of irrigation accordingly. Applying water in amounts and at times correct for a specific need, the system minimizes water usage while maintaining optimum soil moisture for crop growth. Experimental results have indicated significant improvements in water efficiency and crop productivity compared to conventional methods of irrigation. The paper acts like a stepping stone toward the use of smart irrigation systems in sustainable agriculture, conservation of water resources, and better efficiency in agricultural output. Further work may consider scaling up this system to larger agricultural scales and integrating other environmental factors to enhance accuracy in water management.

Development and Application of a Solar-Powered Timer Pump for Irrigation Purpose

frequent power outages and expensive electricity bills in developing countries have driven the development of alternative solutions to electricity from the grid to power pumps for irrigation purposes. In line with this, the study aims to design, construct, fabricate and test a solar powdered timer pump for irrigation that successfully mitigates the concerns of unreliable power supply, inefficient water management, over-irrigation, and raised operational expenses in agricultural activities. The system was developed by designing the various components through design calculations of the pump, solar panel, inverter, charge controller, microcontroller, moisture sensor, sprinkler, and various pipe connections. After designing, the fully assembled system was tested under controlled field conditions. The system was programmed to activate the pump at predetermined times and operate in short cycles typically 4-second intervals until the soil moisture sensor indicated that the desired moisture level had been reached. The soil moisture levels varied from 0 to 150 cl and the corresponding pumping duration was determined. The results revealed that when the level of moisture in the soil was high (1400%) compared to when 10 cl of water was added, there was a 100% reduction in the pumping duration to 0 s. As the percentage amount of water increased from 10 cl, there was a corresponding percentage increase in the duration of pumping. However, at a very high percentage increase of about 1150%, the corresponding level of percentage increase in pumping duration was reduced due to high levels of moisture content in the soil. This study demonstrates that the integration of solar energy with automated control systems can successfully resolve the issues associated with traditional irrigation methods.

Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system.

As the world population is increasing day by day, so is the need for more advanced automated precision agriculture to meet the increasing demands for food while decreasing labor work and saving water for crops. Recently, there have been many studies done in this field, but very few discuss implementing smart technologies to present a combined sustainable farming system. In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. The LSTM model is used for healthy vegetation area forecasting highlighting the changes of the vegetation area over time. Such analysis helps to decide whether that land is suitable for farming or not. Multiple soil-parameter measuring sensors are used to identify suitable crop and fertilizer requirements for that land using IoT and machine learning. The ML model-based crop prediction showed 97.35% accuracy utilizing random forest algorithm. Finally, a fuzzy logic-based solar-powered irrigation system is used to monitor the water requirements of those crops and irrigate them according to their needs. The experimental results demonstrated that fuzzy logic has faster calibration rate of 66.23% and helps to save around 61% water in comparison to average logic algorithm. The implementation of a fuzzy logic algorithm significantly optimized water usage compared to traditional manual irrigation methods. These findings highlight the effectiveness of advanced computational techniques in enhancing agricultural practices and resource management.

Optimizing Irrigation with Solar Energy: Implementation of the Photovoltaic Integrated Control System (PICS)

In Bangladesh, agricultural yields heavily depend on groundwater and traditional irrigation methods, creating dire water scarcity problems and energy inefficiency. The Photovoltaic Integrated Control System (PICS) has been developed as part of this research work to address this issue. As a sustainable irrigation model, the PICS optimizes resource consumption via renewable energy integration and intelligent automation using a quantitative and qualitative approach. The design of PICS is based on photovoltaic panels, Maximum Power Point Tracking (MPPT) technology, and fuzzy logic controllers, which means adjusting directly to weather conditions, soil moisture, temperature, and reservoir levels. Experimental results presented in the paper show that the PICS system saves up to 28.4 percent of water during operation and 53.56 percent in motor power consumption compared with the existing alternatives. These results underscore the potential for the model to aid in resolving groundwater depletion and enhancing energy efficiency. Additionally, integrating solar energy into the PICS enhances sustainability by reducing greenhouse gas emissions. By combining renewable energy with automation, PICS offers farmers an innovative and transformative approach to irrigation, enabling more efficient and environmentally friendly agricultural practices. Its scalability and adaptability across diverse environments, along with its ability to mitigate climate-related challenges and optimize resource utilization, position it as a highly promising solution for advancing agricultural sustainability on a global scale.

Energy-Efficient Smart Irrigation Technologies: A Pathway to Water and Energy Sustainability in Agriculture

The agricultural sector faces challenges such as water scarcity, energy inefficiency, and declining productivity, particularly in arid regions. Traditional irrigation methods contribute to resource depletion and environmental impacts. Solar-powered smart irrigation systems integrate precision irrigation with renewable energy, improving water use and productivity. In Pakistan, where agriculture contributes 19% of gross domestic product and employs 40% of the workforce, these challenges are severe, especially in water-scarce areas like the Cholistan Desert. This study examines the impact of solar-powered smart irrigation on agricultural productivity, water conservation, and energy efficiency in the Cholistan Desert. Using a quantitative cross-sectional design, data were collected from 384 farmers via structured questionnaires. Statistical analyses, including multiple linear regression, paired sample t-tests, and Structural Equation Modeling (SEM), were conducted. Findings show significant improvements in crop yield (from 3.0 to 4.8 tons/hectare) and reductions in water and energy consumption. Regression analysis highlighted strong positive effects on yield and efficiency, while SEM confirmed reduced environmental impact and operational costs. The study concludes that solar-powered irrigation enhances productivity, conserves resources, and promotes sustainability. Policymakers should provide financial incentives, invest in renewable infrastructure, and implement training programs to support adoption. Collaborative efforts are essential for sustainable agriculture in arid regions.

Development of IoT-Based Smart Irrigation System for Farmland Preparation: A Case Study

Agriculture accounts for approximately 24.23% of total GDP and 22% of total exports in Nigeria. It is the most important occupation, providing a source of income for the majority of rural dwellers in Nigeria. Water is the most important resource for agriculture, and irrigation is still one of the most basic wateracquisition processes. Previously, irrigation procedures were carried out manually, resulting in a significant waste of time and an inaccurate volume of water deployed at any given time. In recent times, an artificial intelligence (AI)-based irrigation management method using Internet of Things (IoT) has gained prominence. The irrigation industry has taken advantage of this new technology called Internet of Things to improves irrigation management and drastically reduces operating expenses related to infrastructure maintenance. One of the most serious issues confronting smart irrigation systems is stakeholders' inability to convert available data into detailed and accurate information that can be used in decision making, particularly when it comes to water waste due to improper timing and the proportion.With water scarcity and inefficient traditional irrigation methods posing significant challenges to agricultural productivity,IoT-based technology offers a sustainable solution to manually operated irrigation systems. IoT-based smart irrigation system is an efficient farmland preparationtool and to managing scarce resources such as water and time.Based on the Internet of Things technology, the study analysed key parameterssuch as soil moisture, temperature, and water management using IoT-based technique that work with various sensors. The temperature, humidity, plant and soil moisture sensors are used in the design to detect different soil conditions and automatically irrigate the farmland based on the soil moisture contentand the plant requirements. The results demonstrated the effectiveness of the IoT-based smart irrigation system in optimizing water use in scarcity and enhancing crop yields.