Revolutionizing Agriculture With Cloud-Connected Irrigation Technology

The article analyzes the current stage of educational studies of the cloud-based learning systems. The relationship between the notions of the cloud-based learning system and the cloud-based learning environment are investigated. It was found that the researchers paid most attention to the design of a cloud-based learning environment. However, in the process of a cloud-based environment design, the researchers consider a cloud-based system as a component within the cloud-based learning environment of as a stage in the process of design. It is shown that in the research literature there is no single interpretation of the concept of a cloud-based system for educational purposes. Still the number of basic approaches to the interpretation of the concept under investigation are revealed. The first approach is based on the understanding of the system, as a set of cloud services or cloud-based technologies. The second approach is to consider a separate cloud service as a cloud-based learning system. In this case, the cloud service tools should include such components that cover the content, the tools, the forms and the methods of learning. The structure of the cloud-based learning system within the interpretation of the latest works of Ukrainian researchers is considered.

Smart irrigation systems in agriculture: An overview

The implementation of cloud-based accounting information systems has significantly transformed modern auditing practices. This study examines the influence of cloud-based system implementation, user understanding level, and data quality on the efficiency of the audit process. This study uses a qualitative approach with case studies on organizations that have implemented cloud-based systems. Data was collected through in-depth interviews, direct observations, and documentation of the audit process. The results show that the implementation of cloud-based systems improves audit efficiency by reducing manual tasks and enabling better data integration. User understanding and high data quality are important factors in ensuring reliable audit results. In addition, organizational readiness and integration with advanced technologies such as big data analytics further strengthen the benefits of cloud-based systems in auditing.

The practical application of human nose for fragrance recognition is severely limited by the fact that our sense of smell is subjective and gets tired easily. Consequently, there is a significant need for an instrument that can be a substitution of the human sense of smell. Development of an electronic nose devices is an active area of research starting from pioneering research of Dodd and Persuad in the mid-1980s. Such systems are used in growing number of applications. They comprise an array of several electrochemical gas sensors with partial specificity and a pattern recognition algorithms. Since most of the existing types of sensors are characterized by low selectivity, they can all be used in the construction of electronic noses. Most electronic nose systems are based only on one type of gas sensors. However, different types of sensors have different advantages and disadvantages. In this article, an electronic nose system for the purpose of incorporating different types of sensors has been proposed developed. Such approach allows to benefit from advantages of different type of sensors, and thus improve the operation of the entire system.

Fiber optic data transmission has caused revolutionary developments in the current information society. It was also an eye opener for the Oil & Gas industry when fiber optic-based Distributed Temperature Sensing was introduced in the nineties. Temperature profiles over the entire length of the wellbore could suddenly be monitored in real-time to deliver data that is used for troubleshooting and production optimization. Ongoing research in fiber optic sensing technology for applications with extreme operational conditions (such as aerospace or nuclear fusion) will further boost the development of fiber optic multi-parameter sensing systems. Innovations like these will particularly benefit the Oil & Gas industry in situations where conventional electronic sensors are not technically feasible or cost efficient. In multi-parameter sensing systems of the future, different types of fiber optic sensors (e.g. flow, pressure, acoustic, chemical, vibration, gravity) can be manufactured and read out via one single optical fiber. Data from the different types of sensors will be merged and processed via special data-mining algorithms to generate unambiguous information for faster and better decision making. Judging on the speed of developments witnessed today, we believe there will be a second fiber optic sensing revolution in the Oil & Gas industry in the near future. In this paper, we will give some in-house examples of fiber optic innovations from both in and outside the Oil & Gas industry to demonstrate the versatility and potential of fiber optic technology. Introduction Increasing energy demand combined with scarcity means that oil must be produced from new and often more complex reservoirs. These reservoirs may consist of many sections separated by geological faults or the oil may be very viscous, making production more difficult or economically impossible with current technology. This implies that well and production strategies are becoming increasingly complex and require accurate and reliable input for decision making. Maturing assets develop complex ‘time critical’ process dynamics such as production decline, liquid loading and salt precipitation creating the need for real-time monitoring and control. This measurement and control technology must give a more accurate understanding of the key processes involved. It should also improve asset diagnostics to lead to improved production control and recovery strategies without compromising reliability or safety. Furthermore the industry has to deal with increasingly complex environmental and safety regulations and an increasing shortage of expert personnel. We believe that innovations in fiber optic sensing technology have the potential to help better understand the key processes behind decision-making in the oil and gas industry. This insight is based on many projects both inside and outside the oil and gas industry related to monitoring, process modeling and data processing. These new insights are essential to control and optimize the ‘new’ Oil & Gas industry when one considers the challenges that lie ahead. Before we focus on these upcoming fiber optic technologies for the oil and gas industry, we would like to illustrate the versatility and potential of fiber optic technology based on applications from other industries and their applicability to the oil and gas industry. Fiber optic technology is a proven technology but needs to be adapted and modified to the restricting and demanding applications of the oil and gas industry. These applications also give rise to innovations as will be discussed later.

Flex sensors are frequently used as wearable tools for unobtrusively tracking human joint movements. Among all the flex sensor types, the resistive ones are the mostly adopted thanks to their electrical and mechanical properties capable to furnish electrical resistance values related to the amount of mechanical flexion. In particular, resistive flex sensors have been finding many applications when embedded into gloves, in order to evaluate fine flexion/extension movements of the finger joints. Within this frame, here we investigate the possible utilization of a different type of flex sensors embedded into gloves, i.e., the stretch ones, since the stretch sensors change in resistance proportionally to their stretch that can be just obtained when laid on-top of a finger joint. In such a view, here we compare the characteristics of commercial flex and stretch sensors obtained by means of an ad-hoc setup and protocol. Results demonstrate the different peculiarities of the two different types of sensors, so to determine when it is convenient to adopt one type instead of the other.

Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model involves different IoT based sensors for soil moisture, humidity, temperature sensor, and light. Besides, the sensed data are transmitted to the cloud server for processing and decision making. Moreover, artificial algae algorithm (AAA) with least squares-support vector machine (LS-SVM) model is employed for the classification process to determine the need for irrigation. Furthermore, the AAA is applied to optimally tune the parameters involved in the LS-SVM model, and thereby the classification efficiency is significantly increased. The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

Urban areas face significant challenges, including a lack of green spaces, scarce water resources, environmental pollution, and elevated heat emissions, particularly in developing countries experiencing rapid population growth. Therefore, the study aims to advance sustainable urban agriculture by designing and evaluating a solar-powered smart rooftop irrigation system for peppermint cultivation. The system incorporates two drip irrigation setups—conventional and smart irrigation—powered by photovoltaic (PV) panels. The smart system integrates real-time monitoring of critical variables, including (1) soil moisture, (2) relative humidity, (3) PV panel temperature, and (4) PV panel current and voltage. Key performance metrics such as water and energy consumption, water use efficiency, energy productivity, and carbon dioxide emissions were evaluated for both systems. In addition, the economic analysis of the smart system was determined. Results revealed that the smart system reduced water and energy consumption by 28.1% compared to conventional irrigation. Additionally, the smart system achieved a notable reduction in carbon footprint, with CO2 emissions of 0.181 kg CO₂/m2/year compared to 0.252 kg CO₂/m2/year for the conventional system. The system’s economic analysis demonstrated a payback period of 5.6 years, highlighting its financial viability. This study underscores the transformative potential of solar-powered smart irrigation systems in enhancing food security, conserving water, reducing energy consumption, and mitigating carbon emissions in urban agriculture.

The reliability and availability of a cloud-based system play an important role in evaluating its system performance. Due to the promised high reliability of physical facilities provided for cloud services, software faults have become a major factor for failures of cloud-based systems. In this paper, we focus on the software aging phenomenon where system performance may be progressively degraded due to exhaustion of system resources, fragmentation and accumulation of errors. We present a proactive technique, called software rejuvenation, to counteract the software aging problem. The dynamic fault tree (DFT) formalism is adopted to model the system reliability before and during a software rejuvenation process in an aging cloud-based system. Then it is converted into Markov Chains to derive the system reliability function. We use a case study of a cloud-based system to illustrate the validity of our approach. Based on the reliability analysis results, we show how to estimate software rejuvenation schedules that can keep the system reliability above a predefined critical level for required system availability.

Water management in agriculture has been transformed by smart irrigation systems that use the Internet of Things (IoT) to maximize resource use and boost crop yields. IoT-based irrigation, which reduces waste and improves sustainability in the face of growing water scarcity, combines sensors, actuators and real-time data analytics to deliver accurate water flow. The use of IoT in agriculture is reviewed in this paper, which also covers AI-driven decision-making algorithms and communication technologies like LoRa, Zigbee and Wi-Fi. In agriculture, IoT has been widely employed in agriculture to enable better farming and redefine conventional practices. Some of the necessary applications include monitoring crops, monitoring livestock, automation of the supply of water through real-time data, promotion of optimal water consumption and wastage minimization. Furthermore, increased agricultural yields, cost savings and water conservation are just a few benefits of using IoT in irrigation systems. Research also claimed that when compared to traditional methods, IoT-based irrigation systems can save up to 30% on water usage. IoT-based irrigation has drawbacks despite its benefits, including costly upfront costs, complicated technology and cyber security threats. The use of self-powered sensors, AI-powered predictive irrigation and blockchain-enhanced data security are some of the upcoming trends. Smart irrigation systems have the ability to transform precision agriculture and guarantee both environmental preservation and food security by removing implementation obstacles. Future research should focus on enhancing interoperability among IoT devices, improving decision-making algorithms and developing cost-effective solutions for small-scale farmers. With continuous innovation and widespread adoption, IoT-driven smart irrigation has the potential to address global food security concerns while preserving water resources, creating a more sustainable and technologically advanced agriculture industry.

The Wave Crest Sensor Inter-comparison Study (WACSIS) employed many different types of sensors to measure ocean surface waves. These sensors were attached to a steel jacket platform located in 18 m deep water about 9 km from the Dutch coast. To investigate the suitability and consistency of different wave sensors, wave characteristics at the locations of some sensors were deterministically predicted based on three other wave measurements using a Directional Hybrid Wave Model (DHWM). The comparisons between the predictions and related measurements were applied to the examination of the consistency among different sensors. As an example, a mistake of the orientation of a current meter has been found through the comparisons. This study also demonstrates that consistency among different types of wave sensors is satisfactory and the DHWM is valuable to the analysis of field wave measurements.

Cloud-based data management system is emerging as a scalable, fault tolerant and efficient solution to large scale data management. More and more companies are moving their data management applications from expensive, high-end ser-vers to the cloud which is composed of cheaper, commodity machines. The implementations of existing cloud-based data management systems represent a wide range of approaches, including storage architectures, data models, tradeoffs in consistency and availability, etc. Several benchmarks have been proposed to evaluate the performance. However, there were no reported studies about these benchmark results which provide users with insights on the impacts of different implementation approaches on the performance. We conducted comprehensive experiments of several representative cloud-based data management systems to explore relative performance of different implementation approaches the results are valuable for further research and development of cloud-based data management systems.

Microbial composition and formation of biofilms in agricultural irrigation systems- a review

The main aim of this paper is to develop a ‘smart irrigation system’ equipped with sensors connected via IOT for continuous monitoring of soil moisture and supply water to agricultural lands as per requirements. Proper management of water resources for agricultural production is of great concern in cropping fields and automatic irrigation systems are designed to aid the farmer in needs and support during unfavourable conditions like under and over irrigation. Here, we proposed a system that contains sensors that collect direct soil moisture levels and provide an automatic irrigation facility. The renewable source of solar energy (use of solar panels) is the power source. Also, the image processing methods are done using Raspberry Pi and Open CV to detect plant diseases through capture of plant morphology. The aim is to demonstrate that the automatic irrigation can be used to reduce water utilization and real time monitoring systems of the agricultural growth and health systems.

The use of contemporary technologies in agriculture has transformed conventional farming practices, resulting in considerable increases in productivity, resource efficiency, and sustainability. This analysis looks at the uses of significant technology advancements such precision agriculture, smart irrigation systems, genetically modified organisms (GMOs), automation, robotics, and data analytics. These technologies have the potential to alter agriculture by increasing crop yields, optimizing resource utilization, and minimizing environmental impact. However, their broad use presents significant issues, notably for agricultural laborers who suffer job displacement and the need to up skill. While precision agriculture and automation can lessen the need for human labor, they require a workforce that is skilled at operating and maintaining sophisticated machinery and software. While GMOs increase yields and resilience, they also pose ethical and economic problems that may have an impact on labour relations. Furthermore, the growing reliance on data analytics and artificial intelligence (AI) in decision-making processes may result in a digital gap, marginalising individuals who lack access to or knowledge of these technologies. This paper gives a thorough examination of the advantages and disadvantages of modern agricultural technologies, with a special emphasis on the implications for workers. It emphasises the importance of policies that help displaced workers’ retraining and education, as well as the equitable sharing of technical breakthroughs to guarantee that all agricultural stakeholders benefit from these innovations. The findings underscore the significance of balancing technological advancement with the social and economic well-being of agricultural communities in order to ensure the sector’s long-term and inclusive growth. The study highlights key innovations, such as precision agriculture, automation, and biotechnology, that have revolutionized agricultural practices. While these technologies enhance productivity and sustainability, they also pose challenges, including the displacement of labor and the need for workforce reskilling. The discussion addresses the potential benefits, such as increased efficiency and reduced environmental impact, alongside the challenges of equitable access and socio-economic disparities. Practical solutions, including training programs and policies for inclusive technology adoption, are proposed to mitigate adverse effects and ensure a balanced transition for agricultural communities.