Smart Irrigation Research Articles

Towards a modelling, optimization and predictive control framework for smart irrigation

Smart irrigation scheduling is a promising approach for improving the efficiency and sustainability of agricultural water use, especially in arid and semi-arid lands. In this paper, we present a design and simulation of a model predictive controller for smart irrigation scheduling. The proposed controller is based on a mathematical model of the irrigation system, which is used to predict the future states of the system and determine the optimal irrigation schedule for a given crop and field. This study further evaluates the impact of the predictive control framework on crop yield, water use efficiency (WUE), and water savings in tomato production. Three control strategies, including manual control, open-loop control, and model predictive control (MPC), were compared using a Completely Randomized Design (CRD) methodology. Results indicate that MPC outperforms the other strategies, yielding the highest average crop yield of 20 t/ha and demonstrating superior WUE at 10.4 kg/m3. Additionally, MPC significantly reduces water consumption, achieving a 29 % and 8 % savings compared to manual and open-loop control, respectively. These findings underscore the efficacy of MPC in optimizing crop yield, conserving water resources, and promoting sustainable agriculture practices. The proposed controller has the potential to address the global water scarcity challenge and contribute to the sustainability of agriculture in arid and semi-arid lands.

Internet of Thing (IOT) Based Sensor Technologies and Smart Irrigation System: An Analysis of Critical Success Factors in Emerging Markets

ABSTRACT: This study explores how smart irrigation systems can be improved by introducing advanced technologies and reducing water consumption to fight climate change. This study aims to identify the factors affecting the success of smart irrigation systems based on the Internet of Things with the mediation of optimal consumption management. This is an applied research employing mixed methodologies (qualitative-quantitative). Initially, the semi-structured interviews were carried out to obtain the effective success factors of the smart irrigation system. Analysis in the qualitative section has been conducted using the Maxqda software. For the quantitative study, a 25-statement questionnaire was applied according to the selected codes obtained with a five-point Likert scale to validate the obtained model. The questionnaire was distributed online in January 2023, and analysis was conducted using SmartPLS3 software. The technical aspects of the questionnaire were assessed using validity and reliability criteria, which can guarantee the precision of the research findings. Construct and content validity were employed in this study to examine the questionnaire’s validity. The researchers employed Cronbach’s alpha coefficient to evaluate the reliability of the measurement instrument. Besides, it can be said that the research instrument has acceptable reliability because obtained Cronbach’s alpha for all variables was greater than 0.70 and the questionnaire’s overall alpha was determined to be 0.810. Furthermore, the t-statistics for all research hypotheses is higher than 1.96 at the 95% confidence level. In this sense, the hypotheses of the research were confirmed. The Internet of Things and will change the nature of activities and services in the market. In fact, for emerging markets, the untapped and growing investment opportunities and the economic leap against the Internet of Things are so enticing and facilitating that it is impossible to resist. and neglecting it will cause irreparable damage to agility. Therefore, the success of the Internet of Things for emerging markets depends on the preconditions of integration of activities carried out by decision-making organizations, facilitation of regulations, and communication and interaction between relevant sectors, which should be considered a priority through cross-sectoral research.

IoT-based smart irrigation management system to enhance agricultural water security using embedded systems, telemetry data, and cloud computing

Agriculture, the key sector for food production, faces challenges exacerbated by the growing global demand for food and the scarcity of water resources. Traditional irrigation methods often lead to inefficient use of water, resulting in wastage. Moreover, unpredictable weather conditions and the need to adopt sustainable farming practices are driving us to develop advanced irrigation systems. This paper proposed a smart irrigation management system using new technology like 1) embedded systems, 2) Internet of Things (IoT), 3) telemetry data, 4) cloud computing, communication protocol, and 5) sensors to collect and process real-time data of the smart agriculture. This new technology and intelligent algorithm are used in this paper to improve agricultural practices. The architecture of the proposed scheme comprises three layers: 1) IoT devices, 2) ThingsBoard cloud, and 3) the dashboard, each playing a pivotal role in ensuring seamless data flow, secure communication, and enhanced user interaction. The algorithm orchestrates system operations, incorporating sensor data reading, JavaScript Object Syntax (JSON) payload creation, and secure telemetry data transmission via Hypertext Transfer Protocol (HTTP) protocol to the ThingsBoard cloud. Pump activation decisions are governed by predefined thresholds, preventing an over-irrigation system. The evaluation of system performance involves deploying temperature, humidity, moisture, and water level sensors in a testing field. Further, before data is sent to the cloud, sensor values are calibrated using a functional map. Tests carried out with temperature, humidity, and water level sensors demonstrate the system's dynamic efficiency. By visualizing and analyzing environmental information via ThingsBoard, the results provide real-time data on environmental parameters of the system proposed, improving the efficiency of water use and the sustainability of farming practices. In addition, the system features e-mail notifications for alerts. The integration of e-mail notifications reinforces monitoring and management practices by alerting farm owners and users. This study showcases the efficacy of the proposed paper in enhancing sustainability, water usage, and supporting smart agriculture practices. Further, this paper integrates embedded systems, IoT, cloud computing, and advanced algorithms to optimize and enhance crop productivity and contribute to global food and water security.

A Comprehensive IoT edge based smart irrigation system for tomato cultivation

Agriculture industry is the primary engine for a country's economic development. Growing crops using minimum irrigation water is a major challenge for farmers. In conventional farming, crops may be affected by various diseases due to inadequate irrigation scheduling. Recent proposals have suggested using Edge-IoT, AI, and distributed computing to accelerate the inference procedure utilized in smart irrigation applications. The use of resource-constrained edge servers and edge devices used to deliver smart agriculture applications can cause latency-sensitive workloads to interfere with one another. To address this issue, we design a long-range (LoRa) edge IoT computing-based sustainable and customized smart irrigation framework to capture the real-time data of tomato plants. This helps in automatic underground drip irrigation scheduling. This also predicts total water demand and usage, and measure plant growth status. The edge-IoT cloud data transmission control and optimization has been enforced using Smart irrigation data optimization and robust transmission (SIDORT) Message Queuing Telemetry Transport (MQTT) system. We develop a hybrid algorithm named Linked least traversal (LLT) for machine-to-machine communication (M2M). Also, a Reinforcement learning (RL) based Optimal Soil Wetness Closeness Policy (OSWCP) for irrigation scheduling has been proposed. The performance of the proposed smart irrigation models has been validated through extensive experiments using real-time data in which OSWCP performance has been measured at a 97.88 % accuracy rate. Additionally, a comparison of our proposed architecture has been accomplished by resolving the existing smart irrigation system challenges.

IoTDeploy: Deployment of IoT Smart Applications over the Computing Continuum

IoT Smart Applications have created a demand for architectures, infrastructure, platforms, orchestration, and service deployment strategies. They are deployed from sensors to the cloud over a geographical and computing continuum, which is challenging for service orchestration and DevOps strategies. The distributed infrastructure to implement the end-to-end data path may vary, even for similar applications, concerning the services deployed over the mist, fog, edge, and cloud stages. This paper proposes and evaluates IoTDeploy, a solution for streamlining and scaling static and dynamic IoT service deployment over the continuum. IoTDeploy implements a CI/CD tool plugin for deploying applications running on the continuum and supports dynamic service migration. We evaluated the service migration from cloud to fog and fog to cloud with a case study on smart irrigation in agriculture. The experiments reveal that deployment in IoT-distributed environments is reliable and resilient, enabling migration without interrupting the application and losing data.

Smart Irrigation System with Arduino Automation

Smart Irrigation System with Arduino Automation

Berries of Low-Cost Smart Irrigation Systems for Water Management an IoT Approach

Berries of Low-Cost Smart Irrigation Systems for Water Management an IoT Approach

Assessing the efficiency of using precision farming technology and remote monitoring of weather conditions in the activities of agricultural enterprises

The object of this study was the technology of precision agriculture and remote monitoring of weather conditions. The task to evaluate the effectiveness of using precision farming and precision irrigation technologies in the activities of agricultural enterprises under different conditions, in particular, different climatic and weather conditions, has been considered. To solve the task, a hardware-software system for smart irrigation and remote monitoring of weather conditions in the activities of agricultural enterprises during the cultivation of agricultural crops was designed and described. Results of the system's performance were analyzed in the activities of the Ukrainian agricultural company, which grew potatoes of various varieties in the Kyiv oblast (Ukraine) from 2021 to 2023. The results show that the average yield of potatoes of different varieties without irrigation for three years of observation was 29.74 t/ha, with irrigation – 48.99 t/ha, and with smart irrigation – 55.26 t/ha. At the same time, in the latter case, water, human, and financial resources were saved. The increase in yield with smart irrigation compared to yield with conventional irrigation over the three years of observation was on average 12.8 %. According to the results of the implementation of the hardware-software system for smart irrigation and remote monitoring of weather conditions in Ukraine, the effect of the possible implementation of this system by agricultural companies in the Republic of Kazakhstan was analyzed. The forecast of the average yield of potatoes for the period from 2024 to 2026 was built based on the model of linear weighted moving average, taking into account corrections in the case of using smart irrigation. Data on potato yield from 1990 to 2023 were chosen as the basis. The use of smart irrigation according to the described technology could increase the yield of potatoes of various varieties on average from 31.71 t/ha to 35.78 t/ha in comparison with the forecast values of yield without irrigation at the level of 19.25 t/ha. This confirms the need to apply the transfer of precision farming technologies to increase the yield of agricultural crops, in particular potatoes, and the productivity of agricultural companies

Smart Agriculture Application in Rice Cultivation

Rice is a staple food crop that feeds a significant portion of the world's population. Ensuring sustainable and efficient rice production is crucial for global food security. In recent years, the integration of smart agriculture technologies has shown promise in improving various aspects of rice cultivation. This paper provides an overview of the key smart agriculture applications that can enhance rice cultivation practices. The review discusses the use of precision farming techniques, such as GPS-guided tractors and drones for site-specific management of inputs like fertilizers and pesticides. It also examines how sensor networks and Internet of Things (IoT) devices can monitor environmental conditions, soil fertility, and plant health to enable data-driven decision-making. Smart irrigation systems leveraging soil moisture sensors and weather forecasts are highlighted as a means to optimize water usage. Furthermore, the paper explores the role of automation and robotics in automating labor-intensive tasks like transplanting, weeding, and harvesting. It also discusses the potential of machine learning and predictive analytics to improve crop yield forecasting, pest and disease management, and supply chain logistics. The adoption of these smart agriculture technologies in rice farming has demonstrated improvements in productivity, resource efficiency, and environmental sustainability. However, challenges remain in scaling up these solutions and ensuring their accessibility to smallholder farmers. The paper concludes by outlining future research directions and policy considerations to further advance smart agriculture in the rice cultivation sector.

Internet-of-Things for smart irrigation control and crop recommendation using interactive guide-deep model in Agriculture 4.0 applications

ABSTRACT The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model.

Smart Irrigation Technique in the Fixed Irrigation System Based on Soil Moisture Content

Smart Irrigation Technique in the Fixed Irrigation System Based on Soil Moisture Content

Water neutrality: Concept, challenges, policies, and recommendations

Agriculture, urbanisation, and industrial growth have significantly increased water demand, leading to water pollution, necessitating the protection of rivers, wetlands, and ecosystems. Hence, a strategy is needed that is aligned with Sustainable Development Goal (SDG) 6, which includes ensuring safe and affordable drinking water (SDG 6.1), implementing integrated water resources management (SDG 6.5), and protecting and restoring water-related ecosystems (SDG 6.6). This review article introduces the concept of water neutrality (WN), a planning approach for new urban developments aimed at minimising impacts on water security. The peer-reviewed articles and governmental reports published between 2014 and 2024 provide an overview of the current water situation. It also explores the potential benefits of WN, evaluates existing policies and case studies, and offers actionable recommendations. The study reveals that implementing water-related policies often faces challenges, such as political commitment, insufficient funding, and weak monitoring systems, particularly in developing countries. In India, water policies primarily focus on irrigation, drinking water supply, and pollution control. However, these policies struggle due to weak enforcement and conflicting stakeholder interests. The study recommends significant policy reforms and the adoption of new approaches to achieve WN, which promote integrated water resource management, strengthen governance, pricing mechanisms, advanced pollution control technologies, climate-resilient infrastructure, and public awareness, all while aligning with SDG 6. Innovative solutions, such as smart irrigation systems and water recycling programs, offer promising paths to address water scarcity. Additionally, international collaborations can further support these goals. Integrating WN into SDG 6 has the potential to significantly enhance global water security and sustainability.

Strategi Keberlanjutan Bisnis Untuk Usaha Kecil Di Sektor Pertanian

This study aims to analyze sustainability strategies for small businesses in the agricultural sector, focusing on identifying factors that influence productivity and business sustainability. Key strategies that can help enhance the sustainability of small agricultural businesses include product diversification. The research was conducted over three days on May 19, 20, and 21, 2024, at the Women's Farming Group (KWT) in Ciputat, led by Mrs. Sri Agustin, who is also the neighborhood head. Data were collected from 30 members of the Garuda 12 Women's Farming Group in Cipayung, Ciputat, South Tangerang, ensuring that the selected respondents have high relevance to the research topic. The method used is descriptive qualitative. Data collection techniques included literature study, observation, and interviews with ten KWT members. The results highlight the importance of efficient food resource management, adoption of agricultural technology, market development and marketing, education and training, policy support, and access to financing. Financing for small businesses in the agricultural sector requires sustained support from various parties, including the government, financial institutions, and the private sector. With these various sources of financing, it is expected that small and medium-sized agricultural enterprises can grow better and contribute more significantly to the national economy. By implementing these strategies, small businesses in the agricultural sector can enhance their resilience and contribution to the economy. Effective strategies are necessary to ensure the continuity and growth of these small businesses. This study aims to identify and analyze strategies that can improve productivity, profitability, and sustainability of small businesses in the agricultural sector. The approaches include product diversification, adoption of modern technology, supply chain optimization, and strengthened market access. Product diversification helps farmers reduce risk and maximize income. Adoption of modern technology, such as smart irrigation and drone use for land monitoring, increases efficiency and production yields. Supply chain optimization includes improving logistics and distribution, which can reduce costs and speed up product delivery times

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

A Design and implementation of a sustainable microcontroller based solar power automatic water irrigation control and monitoring system

The major cause of the shortage of land reserve water owing to the lack of rainfall makes the proper operation of irrigation systems extremely important. A major concern for farmers these days is water scarcity. Pakistan is an important agricultural nation and major part of economy depend on agriculture. This study analyses the soil moisture monitoring and automatic plant watering system since it is really helpful in all kinds of weather. In this study a prototype is design and implement using Arduino-based automation sensors are employed for the control and observation of a solar powered smart irrigation system. An Arduino based remote irrigation system developed for the agricultural plantation, which is placed at a remote location and requires water, provides for plantation when the moisture of the earth drives under the set-point rate which turns on the pump on autopilot. An estimation was carried for solar power energy potential agriculture system condition using soil and humidity sensors. The measurements and mathematical formulation were observed to monitor effectivity of solar based power generation. This analysis is conducted for 20 Watt power panel integrated with 5-watt electrical drive with battery capacity 1200 m-Ah. A modified energy model was used to determine the agricultural load for a 5-watt dc motor. The results showed a cumulative analysis to implement this system inspired by smart sustainable clean renewable energy technologies which meet the farmer’s demands significantly increase the productivity of farming and reduce wastage of water and energy.

Transforming an irrigation system to a smart irrigation system: A case study from Türkiye (Turkey)

Transforming an irrigation system to a smart irrigation system: A case study from Türkiye (Turkey)

IoT Based Irrigation and Fertigation System for Smart Smallholder Farming Application

Integrating Internet of Things (IoT) technology with irrigation and fertigation systems has transformed traditional farming methods by enabling reliable monitoring and control of critical environmental parameters. This project aims to design an automated irrigation and fertigation system as a prototype to reduce water wastage and monitor the pH level in the soil, temperature, soil dryness and detection of intruders. Sensors strategically placed throughout the system monitor and control these elements. This system has several important features, including the ability to automatically control water pumps to maintain proper hydration levels, detect intruders within a range of one to six meters of the plant by using the Blynk app, and monitor pH levels through the same app to ensure appropriate neutral levels. Automation and control features are also assessed to confirm that sensors, pumps, and other components precisely respond in line with predetermined requirements. This evaluation helps to determine the overall health of the farmed plants as well as the effectiveness of the system. Finally, the irrigation and fertilizing system for the targeted smallholder farming area has been developed. The resulting smart irrigation and fertigation system is not only a practical solution but also an effective alternative to modern agricultural methods.

Smart Irrigation Systems from Cyber–Physical Perspective: State of Art and Future Directions

Irrigation refers to supplying water to soil through pipes, pumps, and spraying systems to ensure even distribution across the field. In traditional farming or gardening, the setup and usage of an agricultural irrigation system solely rely on the personal experience of farmers. The Food and Agriculture Organization of the United Nations (UN) has projected that by 2030, developing countries will expand their irrigated areas by 34%, while water consumption will only be up 14%. This discrepancy highlights the importance of accurately monitoring water flow and volume rather than people’s rough estimations. The smart irrigation systems, a key subsystem of smart agriculture known as the cyber–physical system (CPS) in the agriculture domain, automate the administration of water flow, volume, and timing via using cutting-edge technologies, especially the Internet of Things (IoT) technology, to solve the challenges. This study explores a comprehensive three-dimensional problem space to thoroughly analyze the IoT’s applications in irrigation systems. Our framework encompasses several critical domains in smart irrigation systems. These domains include soil science, sensor technology, communication protocols, data analysis techniques, and the practical implementations of automated irrigation systems, such as remote monitoring, autonomous operation, and intelligent decision-making processes. Finally, we discuss a few challenges and outline future research directions in this promising field.

Iot Based Smart Renewable Energy Generation and Irrigation System with Moisture Detection

This research introduces an innovative IoT-based system combining renewable energy with smart irrigation. It uses solar panels and wind turbines to power an irrigation setup while feeding excess energy back to the grid. Soil moisture sensors and machine learning algorithms dynamically adjust watering schedules, optimizing water use and preventing over-irrigation. The system offers remote monitoring and control via mobile devices, enhancing resource efficiency and sustainability. Real-world tests show improved crop yields, reduced resource consumption, and increased energy sustainability, presenting a scalable solution for sustainable agriculture.

Smart Irrigation System using IoT based Control Valve

Smart Irrigation System using IoT based Control Valve