Smart Irrigation Research Articles

Edge IoT Prototyping Using Model-Driven Representations: A Use Case for Smart Agriculture.

Industry 4.0 is positioned at the junction of different disciplines, aiming to re-engineer processes and improve effectiveness and efficiency. It is taking over many industries whose traditional practices are being disrupted by advances in technology and inter-connectivity. In this context, enhanced agriculture systems incorporate new components that are capable of generating better decision making (humidity/temperature/soil sensors, drones for plague detection, smart irrigation, etc.) and also include novel processes for crop control (reproducible environmental conditions, proven strategies for water stress, etc.). At the same time, advances in model-driven development (MDD) simplify software development by introducing domain-specific abstractions of the code that makes application development feasible for domain experts who cannot code. XMDD (eXtreme MDD) makes this way to assemble software even more user-friendly and enables application domain experts who are not programmers to create complex solutions in a more straightforward way. Key to this approach is the introduction of high-level representations of domain-specific functionalities (called SIBs, service-independent building blocks) that encapsulate the programming code and their organisation in reusable libraries, and they are made available in the application development environment. This way, new domain-specific abstractions of the code become easily comprehensible and composable by domain experts. In this paper, we apply these concepts to a smart agriculture solution, producing a proof of concept for the new methodology in this application domain to be used as a portable demonstrator for MDD in IoT and agriculture in the Confirm Research Centre for Smart Manufacturing. Together with model-driven development tools, we leverage here the capabilities of the Nordic Thingy:53 as a multi-protocol IoT prototyping platform. It is an advanced sensing device that handles the data collection and distribution for decision making in the context of the agricultural system and supports edge computing. We demonstrate the importance of high-level abstraction when adopting a complex software development cycle within a multilayered heterogeneous IT ecosystem.

Harnessing the power of transformers and data fusion in smart irrigation

In recent years, IoT sensors have enabled smart agriculture to grow rapidly with many compelling real-world applications. One such application is the case of smart irrigation. A particular interest exists in forecasting soil water potential to allow for the establishment of more efficient irrigation systems. Nonetheless, forecasting soil moisture is a complex task and depends on various information sources. Most existing work relies on local approaches, which are less effective at leveraging shared information across different data sources. Therefore, this paper presents a robust global approach for soil water potential forecasting, combining various environmental factors through the cutting-edge Temporal Fusion Transformer. Our proposed approach outperforms established baselines for forecasting soil water potential. As such, this work contributes to the growing body of research on data fusion in real-world applications.

Internet of things sensors and support vector machine integrated intelligent irrigation system for agriculture industry

Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.

Automation Watering System Berbasis IoT Cerdas pada Bawang Merah

In the current era of rapid technological development, smart irrigation systems are increasingly needed in cultivating crops, including shallots. This system was built to increase plant productivity by using smart irrigation to anticipate climate changes that occur. Through the use of IoT technology and expert systems, this system can provide effective solutions in optimizing the growth and quality of shallots. This Automation Watering System uses a microcontroller as the brain of the system which is connected to various sensors and actuators. The microcontroller takes data from sensors and sends it to the server via a WiFi network or other wireless protocol. The server then analyzes the data using an expert system that has been programmed with expert knowledge in the field of shallot cultivation. This Automation Watering System is able to optimize water use. and avoid excess or lack of watering. The results showed that with the integration of the Internet of Things (IoT) and expert systems, this system can provide smart solutions in shallot cultivation, ensure optimal growth, and produce better harvest results.

Smart Irrigation Systems: Overview

Countries are collaborating to make agriculture more efficient by combining new technologies to improve its procedure. Improving irrigation efficiency in agriculture is thus critical for the survival of sustainable agricultural production. Smart irrigation methods can enhance irrigation efficiency, specially with the introduction of wireless communication systems, monitoring devices, and enhanced control techniques for efficient irrigation scheduling. The study compared on a wide range of study subjects to investigate scientific approaches for smart irrigation. As a result, this project included a wide range of topics related to irrigation methods, decision-making, and technology used. Information was gathered from a variety of scientific papers. So, our research relied on several published documents, the majority of which were published during the last four years, and authors from all over the world. In the meantime, various irrigation initiatives were given special attention. Following that, the evaluation focuses on the key components of smart irrigation, such as real-time irrigation scheduling, IoT, the importance of an internet connection, smart sensing, and energy harvesting.

Impact of smart irrigation systems and water management on climate change

As a result of the rapid and unconscious consumption of existing natural resources in the world, climate change is accelerating negatively. It should be remembered that changing of climate has the potential to threaten the security of water, food and energy systems. One of the main problems causing this is improper water management. In this article, the effects of smart irrigation systems, which are a product of climate-smart agriculture and water management for the changing climate in the world, are reviewed in the literature and the effects of these systems on climate change are discussed. The aim of the study is to provide appropriate solutions against climate change. Water management is basically about being conscious of saving by making more use of irregularly used water resources and reducing wastage to zero in both agriculture and drinking water. Climate-friendly agriculture is practices that ensure sustainability in agriculture with climate resilience. Smart irrigation is a technological irrigation system applied to remotely control irrigation with artificial intelligence and minimize water waste. Considering that water resources will deplete more rapidly over time in the fight against climate change, necessary measures should be taken to minimize this loss.

Fuzzy rules based smart irrigation system using adaptive bacterial foraging optimization

The practice of agriculture is heavily reliant on the implementation of irrigation systems. Technology improvements have made it unnecessary to rely on someone else to perform irrigation when we are out and whenever crops need to be watered. Many researchers have attempted to autonomously irrigate crops, but difficulties with accuracy, timing, and cost are rarely addressed and given top priority. The proposed approach employs a real-time sensor, wireless sensor network, the adaptive bacterial foraging optimization (ABFO) algorithm, and a fuzzy irrigation system control to achieve autonomous watering, thereby enabling smart irrigation. This method reduces waste while preserving the container’s water supply. Automated irrigation determines whether crops need to be watered by considering the type of crop, the weather, and the soil moisture and not soil moisture alone. The need for water is calculated using the three aforementioned criteria and fuzzy control drives the automation. Using an arduino-based IoT circuitry, the bioinspired model with algorithm adaptive bacterial foraging optimization, generates the optimized values for three parameters, which are then used by fuzzy logic control to predict the watering requirements of the plants. In terms of accuracy, timeliness, and cost, the suggested approach is advantageous. With this model, it is now possible to completely automate the irrigation system.

Assessing the Water-Energy-Food-Ecosystems Nexus in Smart Irrigation: A Potato Farming Case Study in Lebanon

Assessing the Water-Energy-Food-Ecosystems Nexus in Smart Irrigation: A Potato Farming Case Study in Lebanon

The use of geosynthetics for a “Green Desert” in Expo 2023 Doha, Qatar

Expo 2023 is an International Horticultural Expo to be hosted by Doha, Qatar, for a period of 6 months between 2023 and 2024. Spanning across 1.7 million square meters, the event takes place in the scenic ‘Al Bidda’ park in Doha. The ‘Expo House’ building is planned as a key attraction within the ‘Al Bidda’ park as part of this exposition. The theme of EXPO 2023 is “Green Desert, Better Environment”, with an aim to encourage, inspire, and educate people about cutting-edge methods to lessen desertification, and this aim is clearly reflected in the design of the ‘Expo House’ building. The Expo House is designed in the shape of a semicircle, with a vast open space in front and a fountain as a centerpiece. The benches along the sides of the pavilion form a natural amphitheater, offering stunning views and a perfect spot for the expo’s events and gatherings. One of the challenges was to adopt the right solution that performs the functions of soil retention, stabilization, plantation, and smart irrigation needs of the project. Maccaferri Middle East was chosen as the technology provider for the GREEN COVER SYSTEM; thanks to the wide range of geosynthetic products in our portfolio that can achieve the varying slope requirements of the artificial green mount in the project.

Assessing the Impact of Water Conservation on Cooling Potential of Two Turfgrass Species

Highlights Empirical CWSI was developed for 'Westcoaster' tall fescue and ‘Tifgreen’ hybrid bermudagrass. The canopy temperature of both species was monitored under multiple irrigation scenarios. Deficit irrigation might reduce the cooling benefits of irrigated urban lawns. Abstract. Canopy temperature provides valuable information for efficient irrigation management and for detecting drought injury in a fast and non-destructive way. It also helps quantify the trade-offs between water conservation and the cooling benefit of the irrigated urban landscape, a critical issue in semiarid inland southern California with limited available water resources. Two adjacent hybrid bermudagrass and tall fescue irrigation trials were conducted to determine canopy temperature changes under a wide range of irrigation treatments from 2017 to 2019 in Riverside, California, USA. The canopy temperature data were also used to develop an empirical crop water stress index (CWSI) for each species. The CWSI values ranged from -0.15 to 0.71 for tall fescue and from -0.28 to 0.54 for hybrid bermudagrass. We observed a moderate correlation between visual rating scores (VR) and CWSI values for tall fescue (r = -0.68) and hybrid bermudagrass (r = -0.61). The fitted linear regression between VR and CWSI data suggested CWSI thresholds close to zero for both species to maintain their acceptable visual quality. The irrigation level consistently showed a significant effect on canopy temperature for both species. On average, a 10% decrease in irrigation application increased the canopy temperature of tall fescue and hybrid bermudagrass by 1.3°C and 0.9°C, respectively. Therefore, deficit irrigation might reduce the cooling benefits of irrigated urban lawns in the semiarid climate of inland southern California. Keywords: Canopy temperature, Evapotranspiration, Smart irrigation controller, Urban heat island, Urban irrigation, Water conservation.

Enhancing Crop Water Management: A Logistic Regression Approach Integrated with IoT for Smart Irrigation

This study explores the integration of logistic regression with Internet of Things (IoT) technology to optimize water management in agriculture. Efficient irrigation systems are vital for boosting crop yields while preserving water resources, which is becoming more important due to the growing demand for food and the challenges caused by climate change. The suggested method makes use of Internet of Things (IoT) sensors to gather weather predictions, soil moisture levels, humidity, and temperature readings in real time. In order to determine how much water crops will use, this data is utilized to train a logistic regression model. Supervised learning is made possible by generating labelled data through the analysis of expert knowledge and past irrigation practices. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Once validated, the logistic regression model is deployed within the IoT system to provide real-time predictions of crop water requirements. Through automation and data-driven decision-making, farmers can optimize irrigation schedules, minimize water wastage, and enhance crop productivity. This integrated approach represents a significant step towards sustainable agriculture and resource-efficient farming practices.

An Internet of Things (IoT)-based smart irrigation and crop suggestion platform for enhanced precision agriculture

An Internet of Things (IoT)-based smart irrigation and crop suggestion platform for enhanced precision agriculture

Research on the application of Internet of Things (IoT) for water and fertilizer integration and smart irrigation system in cotton production

Research on the application of Internet of Things (IoT) for water and fertilizer integration and smart irrigation system in cotton production

Farming in the Digital Age: Smart Agriculture with AI and IoT

This review article titled “Revolutionizing Agriculture: The Impact of AI and IoT” delves into the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in modern agriculture. It encapsulates the burgeoning wave of digitalization and its implications for data-driven farming practices. The paper comprehensively reviews the advancements in digital agriculture management, highlighting the integration of AI, IoT devices, machine learning, and sensor technology in enhancing farming efficiency and productivity. Emphasis is placed on the use of robotics, drones for crop monitoring, and smart irrigation systems, which leverage real-time data for optimized agricultural operations. The role of high-speed 5G networks in enabling these smart farming solutions is also discussed, particularly their impact on data transmission and connectivity in rural settings. Challenges and opportunities in implementing Smart Decision Support Systems (SDSS) in developing countries are explored, focusing on real-time soil analysis and decision-making aids for farmers. The review further discusses a proposed cloud-based system for remote farm monitoring, employing AI-based models for efficient data processing and improving farming practices. This article aims to provide a nuanced understanding of the current and future potential of AI and IoT in reshaping agricultural practices, addressing global food security challenges, and supporting sustainable development.

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms.

Smart irrigation planning in water deficit area (case study: Pench Irrigation Project, India)

Smart irrigation planning in water deficit area (case study: Pench Irrigation Project, India)

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.

Smart Irrigation Controllers in Residential Applications and the Potential of Integrated Water Distribution Systems

Smart Irrigation Controllers in Residential Applications and the Potential of Integrated Water Distribution Systems

Smart irrigation systems enabled with internet of things: a bibliometric review

Smart irrigation systems enabled with internet of things: a bibliometric review

Cheap and basic solar powered smart irrigation system proposal for medium and smal scale farming

Cheap and basic solar powered smart irrigation system proposal for medium and smal scale farming