Iot Machine Learning Applications In Telecom Energy And Agriculture
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IoT Machine Learning Applications in Telecom, Energy, and Agriculture
Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will Learn Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch Who This Book Is For Raspberry Pi and Arduino enthusiasts and data science and machine learning professionals.
Digital Agriculture
Author: Muhammad Azhar Iqbal
language: en
Publisher: Springer Nature
Release Date: 2024-09-06
The success of modern-day agriculture lies in the digitalization of agricultural systems. The primary aim of this book is to provide a starting point to understand the fundamentals and design of digital agriculture systems with reference to the enabling technologies that deal with the production, improvement, and protection of crops/plants and livestock. The other associated objectives of this book include the explanation of the design and deployment of IoT-based digital agriculture systems in such a simple way that agriculture students understand straightforwardly. Therefore, this book is an effort to partially fill the gap associated with the understanding of the development and deployment of digital agriculture systems (including both precision and smart farming). We believe that with the provided details of enabling technologies and their usage in digital agriculture systems, agriculture students will find it easier to comprehend the designing of small-/large-scale IoT-based digital agriculture systems. This book provides insight into different technologies, architectures, and case studies that will ultimately help students to understand the concept of Digital Agriculture and its related applications. It enables students to realize the importance of open issues and future challenges of digital agriculture systems.
Industrial Internet of Things (IIoT)
INDUSTRIAL INTERNET OF THINGS (IIOT) This book discusses how the industrial internet will be augmented through increased network agility, integrated artificial intelligence (AI) and the capacity to deploy, automate, orchestrate, and secure diverse user cases at hyperscale. Since the internet of things (IoT) dominates all sectors of technology, from home to industry, automation through IoT devices is changing the processes of our daily lives. For example, more and more businesses are adopting and accepting industrial automation on a large scale, with the market for industrial robots expected to reach $73.5 billion in 2023. The primary reason for adopting IoT industrial automation in businesses is the benefits it provides, including enhanced efficiency, high accuracy, cost-effectiveness, quick process completion, low power consumption, fewer errors, and ease of control. The 15 chapters in the book showcase industrial automation through the IoT by including case studies in the areas of the IIoT, robotic and intelligent systems, and web-based applications which will be of interest to working professionals and those in education and research involved in a broad cross-section of technical disciplines. The volume will help industry leaders by Advancing hands-on experience working with industrial architecture Demonstrating the potential of cloud-based Industrial IoT platforms, analytics, and protocols Putting forward business models revitalizing the workforce with Industry 4.0. Audience Researchers and scholars in industrial engineering and manufacturing, artificial intelligence, cyber-physical systems, robotics, safety engineering, safety-critical systems, and application domain communities such as aerospace, agriculture, automotive, critical infrastructures, healthcare, manufacturing, retail, smart transports, smart cities, and smart healthcare.