The Agricultural industry is always at risk - crops are affected by weather, diseases and pests. When a global pandemic hits suddenly, it becomes very difficult to manage various processes because most are not digital. In parallel, the rapid increase in the population and urbanization, demands more food production on less land. Farming under the demand pressure by increasing input consumption, leads to increases costs and negative impacts to the environment such as decreasing soil fertility continuously over time. There is therefore the need to move beyond traditional farming. To produce more yield in the agricultural field, there is the need to dig deep into the technological field and apply sensors, Internet of Things (IoT), big data analytics, cloud computing and machine learning (ML) and deep learning (DL) techniques. Better decision making, prediction and reliability depend upon high level of knowledge base and perceptions.

Moo’s Law is the latest title from successful investor Jim Mellon, to help readers understand the investment landscape in cultivated and plant-based proteins and materials. Jim has a vision that within the next couple of decades world agriculture will be radically transformed by the advent of cultivated meat technology. This book grounds the reader in why such an advancement is absolutely necessary and informs them of the investments they could make to become part of the New Agricultural Revolution themselves. The harrowing effects on our environment, animal cruelty in food and fashion, and the struggling ability to feed the world's ever-growing population gives us no choice but to grow meat in labs or derive our proteins from plant-based sources. Not only this, he outlines what he sees as the major hurdles to the industry's success in terms of scalability of production and the smart designing of regulatory frameworks to stimulate innovation in this sector. The future of food is being developed in labs across the world - it will be cleaner, safer, more ethical and, importantly soon, cheaper too! Once price parity with conventional meats is reached, there will be no turning back -- this is Moo's Law™.

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. - Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials - Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets - Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems - Utilizes case studies to illustrate how machine learning methods can be employed in practice

Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today Key FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataProcess IoT data and predict outcomes in real time to build smart IoT modelsCover practical case studies on industrial IoT, smart cities, and home automationBook Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learnApply different AI techniques including machine learning and deep learning using TensorFlow and KerasAccess and process data from various distributed sourcesPerform supervised and unsupervised machine learning for IoT dataImplement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platformsForecast time-series data using deep learning methodsImplementing AI from case studies in Personal IoT, Industrial IoT, and Smart CitiesGain unique insights from data obtained from wearable devices and smart devicesWho this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.

This title documents the burgeoning eco art movement from A to Z, presenting a panorama of artistic responses to environmental concerns, from Ant Farms anti-consumer antics in the 1970s to Marina Zurkows 2007 animation that anticipates the havoc wreaked upon the planet by global warming.

Food systems around the world face a triple challenge: providing food security and nutrition for a growing global population; supporting livelihoods for those working along the food supply chain; and contributing to environmental sustainability. Better policies hold tremendous promise for making progress in these domains.

After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?

The report highlights the crucial role of engineering in achieving each of the 17 SDGs. It shows how equal opportunities for all is key to ensuring an inclusive and gender balanced profession that can better respond to the shortage of engineers for implementing the SDGs. It provides a snapshot of the engineering innovations that are shaping our world, especially emerging technologies such as big data and AI, which are crucial for addressing the pressing challenges facing humankind and the planet. It analyses the transformation of engineering education and capacity-building at the dawn of the Fourth Industrial Revolution that will enable engineers to tackle the challenges ahead. It highlights the global effort needed to address the specific regional disparities, while summarizing the trends of engineering across the different regions of the world.