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

Disordered blood glucose metabolism is associated with poor cardiovascular disease outcomes. Relationship between casual blood glucose (CBG) and blood pressure indices among rural dwellers in Nigeria has been less studied. We measured CBG, anthropometry, systolic blood pressure (SBP) and diastolic blood pressure (DBP) pulse pressure (PP) and mean arterial pressure (MAP) and life-style cardiovascular risk factors. The mean (standard deviation) of CBG was 6.2 (1.9) and values were similar in men and women. Age, SBP, DBP PP, MAP, body mass index (BMI) and waist circumference were positively correlated with CBG; r = 0.23, 0.30, 0.24, 0.28, 0.28, 0.24 and 0.22 respectively. In a multivariate analysis, only PP and BMI predicted CBG. A 5 mmHg increase in PP or a 2 kg/m 2 increase in BMI increased CBG by 0.15 or 0.18 mmol respectively. PP and BMI in combination explained 13% of the variation in CBG (P < 0.001). PP and BMI are associated with CBG among rural dwellers in South-East Nigeria.

The development of Internet of Things (IoT) technology has had a major impact on various aspects of life, including agriculture and plant care. This research aims to design a solution that can overcome the challenges of irrigating ornamental plants by utilizing soil moisture sensors and IoT technology. The approach used in this study is Hardware Programming. This approach involves leveraging hardware and programming to design and implement an IoT-based automatic watering system for ornamental plants. In its implementation, the NodeMCU ESP8266 acts as the control center, while the soil moisture sensor is used to measure soil moisture levels, the DHT11 sensor is used to measure ambient temperature, and the ultrasonic sensor is used to measure the volume of water in the container. The data obtained from these sensors is integrated with the Blynk application. The test results show that the system is able to provide an accurate and fast response regarding soil moisture conditions, as well as making it easier for users to monitor watering through mobile devices. This research contributes to overcoming plant care challenges by utilizing IoT technology and integration with the Blynk platform. It is hoped that this solution will provide benefits, especially for Tangerang Express Park rangers, so that they can monitor plant conditions in real-time.

The integration of the Internet of Things (IoT) is changing modern agriculture by allowing real-time data collection and automated decision-making. Smart irrigation systems have improved water management by optimizing schedules based on soil moisture levels and environmental conditions. However, most current systems focus only on monitoring before irrigation and often overlook key post-irrigation factors like soil moisture retention, crop stress, and water distribution efficiency. This limitation can result in inefficient water use, increased crop stress, and potential soil degradation, which can affect long-term agricultural sustainability. To tackle these challenges, we suggest an IoT-based post-irrigation monitoring system that uses soil moisture sensors, temperature sensors, and real-time analytics. Unlike traditional systems, this approach gives continuous feedback on soil moisture and crop stress. This allows for adjustments to irrigation based on the current field conditions. By including machine learning-driven predictive analytics, the system can improve irrigation schedules, reduce water waste, and boost agricultural productivity. Additionally, it helps crops become more resilient to bad weather, leading to steadier yields in different environmental conditions. The proposed solution improves existing smart irrigation technologies by ensuring precise water management, promoting long-term crop health, and fostering sustainable farming practices. Real-world testing indicates that our system achieves a 30% reduction in water wastage and a 20% increase in agricultural productivity. This research contributes to the advancement of intelligent, resource-efficient irrigation management, supporting sustainable water conservation and long-term agricultural viability. Furthermore, our solution demonstrates scalability, making it applicable to both small-scale farms and large agricultural enterprises.

Objective of the study to find out irrigation management in conserving the water resources with irrigation system to increase crop productivity. In this paper, this has designed and implemented an IoT-based smart irrigation system using soil moisture, pH, and NPK sensors for remote monitoring and control of soil parameters, by interfacing them with the NodeMCU ESP8266 microcontroller. The output values obtained from the sensors were displayed using an LCD and values were then sent to a cloud server for remote access. Research also interfaced a relay module with a water pump to water the plants automatically when moisture content is lower than a set threshold. The results indicate that the soil moisture sensor's values, NPK sensors and pH sensor are well correlated with the lab values with an error of less than 3%, 5% and 2% respectively. The comparison of the results with the manual practice of irrigation demonstrated a considerable difference in the water and fertilizer consumption with the implementation of IoT assistance in the irrigation system. Therefore, this study confirms that the proposed model is precise, accurate, cost-efficient, and easily scalable for practical applications in smart agriculture by eliminating wastage and real-time monitoring of required inputs.

Agriculture is a vital sector in the global economy but also a major contributor to water consumption and wastage due to inefficient irrigation techniques. To address this issue, smart irrigation systems using the Internet of Things (IoT) have emerged as a solution for optimizing water use. These systems utilize real-time sensor data to improve irrigation efficiency and agricultural productivity. This paper presents an automatic, low-cost intelligent irrigation system based on a fuzzy rule-based inference approach and an energy-aware routing algorithm. The proposed system determines the optimal irrigation method using sensor data and ensures efficient information transmission through a fast fuzzy-based routing mechanism. Additionally, it enables remote monitoring and control via mobile devices, enhancing user convenience. The system is designed to be intelligent, cost-effective, and portable, making it suitable for various agricultural applications such as greenhouses and farms. Simulation results demonstrate that the proposed method outperforms existing algorithms, including DLQR, SPIS, and FWIS, in terms of network lifetime and power consumption. By improving water efficiency and reducing resource wastage, this research contributes to sustainable agriculture. The proposed system provides a scalable and adaptable solution for modern farming, ensuring better irrigation management while conserving essential resources.

In today's world, Internet of Things (IoT) technology has revolutionized various fields – including agriculture – by offering smart solutions that enhance crop yield and quality. One crucial aspect of smart farming is the application of IoT for disease detection and prevention. Crop diseases caused by bacteria and viruses can have devastating effects, leading to significant financial losses for farmers. Early detection plays a crucial role in managing these diseases, and IoT technology provides reliable and efficient methods to achieve this goal. To aid in the research on disease recognition in corn or maize plants, a large-scale dataset comprising 4188 images across four disease categories – namely, healthy, common rust, gray-leaf spot, and blight – is utilized. This dataset is used to train various machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), convolutional neural network (CNN), naïve Bayes (NB), gradient boosting trees (GBT), long short-term memory (LSTM), and the algorithms from recent literature such as genetic algorithm-based ensemble classifier, XGBoost, AdaBoost and XGBoost + KNN to accurately identify disease patterns in plants and is loaded into the Raspberry Pi. By interfacing a camera with a Raspberry Pi, leaf diseases can be detected, and the farm’s status can be sent to the farmer’s webpage using a Wi- Fi server. This information empowers farmers to take immediate action to control the disease such as activating sprinklers or applying pesticides. Through manually controllable on/off mechanisms, farmers can remotely manage these actions, ensuring the health and disease-free state of their crops. Among the selected algorithms, the XGBoost + KNN algorithm demonstrates the highest accuracy, reaching an impressive 98.77%.

The design and evaluation of a smart irrigation system leveraging Internet of Things (IoT) technology is presented to address Jordan’s severe water scarcity challenges. The system integrates soil moisture and temperature sensors, the Arduino MKR WiFi 1010 microcontroller for real-time data processing, and a cloud platform for data management and remote control. Optimizing water delivery to crops based on real-time environmental data, the system reduces water waste and enhances agricultural productivity. Field tests in controlled and real-world settings confirmed its efficacy, highlighting IoT’s transformative role in achieving sustainable irrigation practices and addressing global water challenges.

This community service initiative aimed to improve agricultural practices in rural smallholder farming through the adoption of Internet of Things (IoT) technology for smart farming. The challenge in Desa Sukamaju, a region reliant on traditional farming methods, was the inefficient use of water and suboptimal crop yields, which were exacerbated by unpredictable weather patterns. The goal of this intervention was to empower local farmers by providing them with real-time data on soil moisture, temperature, and humidity to optimize irrigation and improve crop productivity. The approach adopted was Participatory Rural Appraisal (PRA), ensuring active farmer involvement in every phase of the program. The project utilized hands-on training to install and use IoT sensors, followed by continuous monitoring and support. Results showed a 23% reduction in water usage and an 18% increase in crop yields, particularly in vegetable crops. Additionally, qualitative feedback indicated a significant increase in farmer confidence, with 85% reporting greater technological competence. This indicates that IoT can significantly enhance resource management and crop productivity, even for farmers with limited digital experience. However, challenges such as the sustainability of technology maintenance and scalability were identified. The findings highlight the potential of IoT to modernize smallholder farming and underscore the need for continuous support and cost-effective solutions for long-term impact. The contribution of this work lies in demonstrating the practical, scalable application of IoT technology in rural farming settings, with recommendations for future projects to ensure sustainability and broader adoption.

Major advances in the Internet of Things (IoT) technologies are offering unprecedented automated services to various sectors. IoT has shown great promise in transforming buildings and institutions into smart buildings and institutions, making traditional task management more efficient. In particular, there has been remarkable progress in successfully integrating IoT into various contexts, such as smart homes, businesses and cities. Success in such technological transformations is not only due to the proper deployment and implementation of IoT but also to the effective employment of related technologies, such as big data and cloud computing technologies, and making informed decisions on the inclusion of the required hardware and software for all the elements involved in the IoT environment. While a significant amount of IoT research in various contexts has been proliferated in recent years, attention given to its deployment for linguistic landscapes (LLs) is still very rare. The aim of this paper is to propose a smart Al-Noor Mall LL which takes into account the appropriate deployment of IoT tools and relevant technologies. The paper also thoroughly discusses the background of the field, covering IoT technologies, cloud computing, and big data studies, followed by a review of the recent publications involving the successful deployment of IoT technologies combined with big data and cloud computing in various contexts. The literature also covers the success factors that contribute to the robustness of the required real-time operating system (RTOS) in an IoT-based environment in order to enable IoT devices to manage their resources efficiently. Based on a detailed analysis of the literature, a three-level framework (i.e. the object, communications and application levels) together with the elements that are required for IoT-based smart LL solutions is proposed. A case study of Al-Noor Mall is used to validate the proposed framework and the results indicate that IBM Bluemix can help Al-Noor Mall management and customers to locate any particular shop in the huge shopping mall, which thereby makes the customers’ shopping experience more satisfying. Finally, the challenges (i.e. energy efficiency, security, and intelligent data analysis) facing the successful deployment of IoT technologies are discussed with proposals for future research directions in the IoT research field.

The idea to connect everything to anything and at any point of time is what vaguely defines the concept of the Internet of Things (IoT). The IoT is not only about providing connectivity but also facilitating interaction among these connected things. One of the most significant concerns of IoT is to provide security assurance for the data exchange because data is vulnerable to some attacks by the attackers at each layer of IoT. The IoT has a layered structure where each layer provides a service. The security needs vary from layer to layer as each layer serves a different purpose. This paper aims to analyze the various security and privacy threats related to IoT. Some attacks have been discussed along with some existing and proposed counter measures. In developing countries, IoT technologies have brought increased efficiency and effectiveness to existing processes. For instance, farmers are using remote sensors to monitor moisture levels and soil conditions in the fields to avoid crop failure. Similar sensors are providing remote control of micro-irrigation pumps for example in India and water pumps in Rwanda, improving functionality and reducing repair intervals. And in Haiti, healthcare professionals are using “smart” thermometers to better track vaccine delivery and storage. Totally the paper presents an overview of the enablement of IoT in developing countries and discusses the visions, general applications, opportunities and challenges of IoT in these countries. It also touches on issues relating to security, privacy, trust and the accompanied complexity. The potential applications for IoT are countless and can optimize processes with a direct impact on society. Certain challenges have to be addressed to make IoT deployment successful. It will be shown that some of the challenges can be turned into opportunities and that IoT has good chances to succeed in developing countries. This report explores the current use and potential of Internet of Things (IoT) technologies in tackling global development challenges, highlighting a number of specific instances where IoT interventions are helping to solve some of the world’s most pressing issues it presents summary conclusions on what is required for the IoT to reach billions of people living in the developing world, and also to accelerate income growth and social development as a result.

Smart city is a city that increases the interactivity of its components and put emphasis on their functionality. Internet of Things technology (IoT) is an innovative solution in environmental protection. Usually, information on air quality is very scattered. This paper describes the test stages of pre-implementation works, focusing on the presentation of the technical design of the measurement nodes and the assumptions of the IT project. The goal of the project Intelligent Wireless Sensor Network Infrastructure (IIBSC) is, among others, to create a dense network of air quality measurement nodes at city, district or even street level. The concept is based on Internet of Things (IoT) technology using a matrix construction tool connected to multiple identical measurement nodes located in the test area. The project developed a hardware platform supporting sensors and resistant to external factors, and an ISIMPIO information platform based on edge processing technology for processing data from air quality sensors. Due to the use of Internet of Things (IoT) technology, an edge server using edge processing was designed. Edge server provides a complete ecosystem for building edge applications that are fully optimized for seamless field work. In addition, it allows the implementation of integrated Python software, the MQ Telemetry Transport support protocol (MQTT), time-series database, firmware update over a wireless network, and built-in security system. Measuring the concentration of particulate matter and other substances in the air will be useful for specialists assessing their dynamics. The technology and test installation selected corresponds to the leading solutions in this field in Europe and, in the future, should also be extended to less urbanised areas.

In this paper, we present the design and implementation of a smart irrigation system using Internet of Things (IoT) technology, which can be used for automating the irrigation process in agricultural fields. It is expected that this system would create a better opportunity for farmers to irrigate their fields efficiently, as well as eliminating the field's under‐watering, which could stress the plants. The developed system is organized into three parts: sensing side, cloud side, and user side. We used Microsoft Azure IoT Hub as an underlying infrastructure to coordinate the interaction between the three sides. The sensing side uses a Raspberry Pi 3 device, which is a low‐cost, credit‐card sized computer device that is used to monitor in near real‐time soil moisture, air temperature and relative humidity, and other weather parameters of the field of interest. Sensors readings are logged and transmitted to the cloud side. At the cloud side, the received sensing data is used by the irrigation scheduling model to determine when and for how long the water pump should be turned on based on a user‐predefined threshold. The user side is developed as an Android mobile app, which is used to control the operations of the water pump with voice recognition capabilities. Finally, this system was evaluated using various performance metrics, such as latency and scalability.

The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming explores how cutting-edge technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain are transforming farming for a sustainable future. Addressing challenges such as climate change, resource scarcity, and food supply chain inefficiencies, the book highlights how these technologies can improve decision-making, enhance crop yields, and increase transparency in agriculture. With a blend of theory and real-world applications, it covers everything from AI-driven pesticide prediction and disease identification to using Blockchain for efficient food supply chain management. This comprehensive guide is essential for researchers, professionals, and anyone interested in the intersection of technology and sustainable farming. Key Features: - Introduction to Digital Twin technology for sustainable farming - Practical applications of AI and IoT in agriculture - Blockchain&amp;amp;amp;#039;s role in food supply chain management - Frameworks for precision agriculture and access to government schemes - Insights on integrating AI, IoT, and Blockchain into solid waste management systems

The Internet of Things (IoT) technology will soon become an integral part of our daily lives to facilitate the control and monitoring of processes and objects and revolutionize the ways that human interact with the physical world. For all features of IoT to become fully functional in practice, there are several obstacles on the way to be surmounted and critical challenges to be addressed. These include, but are not limited to cybersecurity, data privacy, energy consumption and scalability. The Blockchain decentralized nature and its multi-faceted procedures offers a useful mechanism to tackle several of these IoT challenges. However, applying the Blockchain protocols to IoT without considering their tremendous computational loads, delays, and bandwidth overhead can let to a new set of problems. This review evaluates some of the main challenges we face in the integration of Blockchain and IoT technologies and provides insights and high level solutions that can potentially handle the shortcomings and constraints of both IoT and Blockchain technologies.

Nowadays, one can observe the arrival of several mechanical techniques in the agricultural sector. To make the technology accessible to everyone at an affordable cost and to diminish workload for farmers by making it easier to control and understandable, there should be the presence of new technology and it is IoT, which is elaborated as Internet of Things. Usually, Internet of Things is defined as the number of gadgets are connected through wireless networks such as Wi-Fi, SIM networks, etc. IoT technology in the agricultural sector might create wonders by reducing the burden for farmers in a great way. Using IoT (Internet of Things), modern wireless devices can be constructed at an affordable cost to farmers across the country. Therefore, farmers could gain high productivity as the tasks are getting easier as well as being completed within a short period. This paper centers around the job of the Internet of Things in agribusiness that prompts brilliant cultivating. The modern technology named IoT not only shows the impact in the agricultural field but also provides several functionalities in other fields. The point of this undertaking is to propose IOT based Smart and Automated Agricultural Management System that records the data of moisture, temperature, humidity, heat index, and water level. In Smart and Automated Agricultural Management System, soil moisture sensor, DHT11 sensor, water-level sensor, and Arduino technology are utilized to simplify tasks by controlling the parameters of crop moisture content, environmental temperatures at appropriate times through the mobile application called Blynk with secured working.