Role of Data Visualization and Big Data Analytics in Smart Agriculture

Taking a byte out of big data

A survey on smart farming data, applications and techniques

Deep-learning-based visual data analytics for smart construction management

The relentless growth of the global population, compounded by the adversities posed by climate change, has triggered an unprecedented demand for sustenance. This burgeoning need places an imperative on the agricultural sector to adopt practices that are both sustainable and efficient. Traditional farming methods, marked by inefficiencies and environmental ramifications, prove inadequate in meeting this escalating demand. Inresponse, precision farming emerges as a transformative solution, centering on data-driven decision-making and the optimization of resources. This groundbreaking whitepaper meticulously navigates the intersection of precision farming and augmented reality (AR) technology, showcasing a paradigm shift in agriculturalpractices. AR, a revolutionary tool overlaying digital insights onto the physical world,empowers farmers with real-time information on soil conditions, crop health, and moisture Levels. Harnessing AR's capabilities, farmers gain unprecedented control over irrigation and fertilizer applications, minimizing waste while maximizing yields. The paper dives deep into the modern challenges of agriculture, articulates the substantial benefits of precision farming, and underscores AR's transformative potential in surmounting these challenges. Precision farming, elucidated as the fusion of technology to gather and analyze data on parameters like soil moisture and weather conditions, is detailed with utmost clarity. In contrast to the antiquated one-size-fits-all approach of traditional farming, precision farming, facilitated by real-time data and analytics, enables bespoke crop management. This optimization minimizes resource usage, curtails waste, and augments productivity to Unparalleled levels. The whitepaper seamlessly dissects precision farming into two indispensable components:data capture and data visualization. The Internet of Things (IoT) emerges as the linchpin for data capture, employing sensors that collect real-time data transmitted to a centralized platform for astute analysis. Concurrently, AR, the second component, emerges as the pivotal interface, empowering farmers to seamlessly visualize and interpret data for judicious decision-making. In the current corporate panorama, stalwarts like Augmenta and Grow Glide, coupled with potential contributors like Rams Creative Technologies Pvt. Ltd. and emerging entities likeInfosys Limited, stand as vanguards propelling technological advancements to bolster precision farming. However, the potential for widespread AR adoption in precision farming faces impediments. Concerns regarding the safety and health risks associated with AR hardware, coupled with formidable development costs, and a prevailing lack of awareness pose substantial challenges.

Risk monitoring model of intelligent agriculture Internet of Things based on big data

Because of continuous innovation and the great importance placed on automation, digital information has become an increasingly valuable driver transitioning all domains. The fundamental focus of this chapter stems from the dynamic interaction between information progression and trending technologies, as well as the growth of real-world concerns, which has been speeding the progress of the digital economy. Those who can acquire, keep, transform, and use data are in control. As a result, intelligence gained through data analysis and learning improves our understanding via computational intelligence, big data analytics and Internet of things. This chapter provides a systematic literature review on computational intelligence, big data analytics, and the Internet of things, focusing on the problems and opportunities presented by domains such as healthcare, smart farming, industry 4.0/5.0, smart cities.

In this chapter, a discussion is presented about what Big Data and Internet of Things (IoT) really is and what intricacies are used while building big data and internet of things. Further Big Data and Internet of Things have been used for building an application used for Smart City & Agriculture. A smart city is an urban development vision to integrate multiple information and communication technology (ICT) solutions. Smart city's goal is to improve quality of life with technology to improve the efficiency of services and meet residents' needs. Smart agriculture approach is to develop, transform and reorient agricultural development under new realities of climate change. It increases productivity enhances resilience (adaptation), reduces mitigation with achievement of national food security and development goals. This chapter includes detailed discussion on Smart City and Smart Agriculture along with planning, designing as well as various approaches used to build and implement them effectively.

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey article, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization articles included in our survey based on different taxonomies used in visualization and visual analytics research. We review these articles in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

Smart agriculture has emerged as a transformative approach to addressing the twin challenges of food security and environmental sustainability in the face of a growing global population. In the midst of a growing global population and in the face of twin challenges of food security and environmental sustainability, smart agriculture has come to represent a novel approach to tackling these problems. Some of these advanced technologies being utilised in smart agriculture include: Internet of Things (IoT), Artificial Intelligence (AI), blockchain, and Big Data. These technologies permit greater precision in resource management, data driven decision making and the introduction of environmentally friendly practices. This research examines how demographic factors like age, education, location and farm size influence a farmer’s adoption of smart agricultural technology. Using a structured survey with a total of 150 participants, the study investigates the attitudes of farmers about five different aspects: Then there is technology efficiency, technology cost effectiveness, technology convenience use, environmental technology advantage, and adoption potential. These results pinpoint education as a huge influence, as farmers with postsecondary education were much more likely to adopt. There is also a very marked difference in the propensity of younger farmers to accept advances when compared with their older colleagues. The biggest environmental ones with highest marks shows a sincere accord with ecological benefits of smart technologies. On the other hand, high initial expense continues to be a great barrier, implying the need for financial incentives and subsidies. The goal of this research is to provide policymakers and industry stakeholders with ideas that may be implemented. In these recommendations, there are investments in education, digital infrastructure and targeted outreach initiatives to bridge gaps between demographic and geographical differences.

This paper discusses a crucial component of contemporary agriculture: using Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) to monitor and reduce carbon emissions from agriculture. We concentrate on how these cutting-edge technologies augment Climate-Smart Agriculture (CSA) and advance sustainable farming methods. A thorough analysis of the ways in which IoT, Big Data, and AI can be integrated to track carbon footprints and promote more general sustainability goals in agriculture is given in this research. As a significant contribution, we provide a practical, all-encompassing system architecture that integrates real-time data analytics, predictive modeling, and IoT-enabled sensors created especially for carbon footprint assessment. This paper assesses how well these technologies work to enhance emission monitoring, operational effectiveness, and environmental compliance while highlighting their observable advantages through real-world case studies. The rigorous analysis of issues including data interoperability, device energy efficiency, and implementation costs also sheds light on current research gaps. The report also outlines potential future paths, such as blockchain-based carbon credit trading platforms, scalable IoT-based carbon markets, and machine learning (ML) algorithms for precision farming. The goal is to offer helpful guidance on how to apply cutting-edge technology in the agricultural sector to achieve carbon neutrality and environmental sustainability.

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

Internet of Things (IoT) is increasingly deployed in different domains and environments including smart homes, smart cities, healthcare, industry 4.0, and smart agriculture, by connecting a large number of physical objects to deliver a new class of applications. The rising number of these connected objects and their heterogeneity have raised new research directions and challenges regarding their communications, scalability and the large amount of data that generate. As a result, new communication technologies have been proposed to be applied in these environments and applications, mainly to consider their main inherent properties which are the information that generate and handle, and the content that disseminate. Among the adopted techniques and recently integrated into the IoT is the ICN (Information Centric Networking) paradigm. The choice of integration of ICN in the context of IoT is mainly motivated by all the advantages it represents, in particular content caching and decoupling senders and receivers. In this paper, we provide a detailed analysis of the motivations behind using ICN in IoT environments and a survey of existing research work that have already applied ICN as a communication support for IoT applications.

Machine learning for Big Data analytics in plants.

In the rapidly evolving landscape of data mining, the effective extraction of valuable insights from large datasets is paramount. This survey paper investigates the pivotal roles of data visualization and analytics in the intricate process of data mining abstraction. We delve into the symbiotic relationship between these two components, examining how they synergistically contribute to the extraction, representation, and interpretation of meaningful patterns and trends within complex datasets. The survey begins by elucidating the fundamental concepts of data mining abstraction and the significance of distilling actionable knowledge from raw data. It subsequently explores the multifaceted benefits of data visualization, elucidating its role in pattern identification, insight generation, and seamless communication of findings to diverse stakeholders.In parallel, the paper navigates through the landscape of data analytics, unraveling its diverse methods such as descriptive analytics, predictive analytics, and prescriptive analytics. Emphasis is placed on how these analytical techniques enhance the abstraction process, providing statistical rigor and predictive power to unveil hidden insights.The integration of data visualization and analytics is a focal point, showcasing their collective impact on various stages of data mining. From exploratory data analysis (EDA) for initial dataset understanding to the evaluation of mining models, the survey illuminates the collaborative nature of these components. Interactive dashboards emerge as a powerful tool, allowing users to dynamically explore datasets, visualize trends, and perform real-time analytics. Key Words:Data visualization , Data analytics ,Data mining , Tools and Software , Metrics and Key Performance Indicators (KPIs).