Machine Learning and Artificial Intelligence for Agricultural Economics PDF Download
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Author: Chandrasekar Vuppalapati
Publisher: Springer Nature
ISBN: 3030774856
Category : Business & Economics
Languages : en
Pages : 611
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Book Description
This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.
Author: Chandrasekar Vuppalapati
Publisher: Springer Nature
ISBN: 3030774856
Category : Business & Economics
Languages : en
Pages : 611
Get Book Here
Book Description
This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.
Author: Chandrasekar Vuppalapati
Publisher:
ISBN: 9783030774868
Category :
Languages : en
Pages : 0
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Book Description
This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications. The book introduces essential agricultural economic concepts from the perspective of full-scale software development with the emphasis on creating niche blue ocean products. Chapters detail several agricultural economic and AI reference architectures with a focus on data integration, algorithm development, regression, prognostics model development and mathematical optimization. Upgrading traditional AI software development paradigms to function in dynamic agricultural and economic markets, this volume will be of great use to researchers and students in agricultural economics, data science, engineering, and machine learning as well as engineers and industry professionals in the public and private sectors.
Author: Mohammad Ayoub Khan
Publisher: Academic Press
ISBN: 0323906680
Category : Business & Economics
Languages : en
Pages : 332
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Book Description
Application of Machine Learning in Smart Agriculture is the first book to present a multidisciplinary look at how technology can not only improve agricultural output, but the economic efficiency of that output as well. Through a global lens, the book approaches the subject from a technical perspective, providing important knowledge and insights for effective and efficient implementation and utilization of machine learning. As artificial intelligence techniques are being used to increase yield through optimal planting, fertilizing, irrigation, and harvesting, these are only part of the complex picture which must also take into account the economic investment and its optimized return. The performance of machine learning models improves over time as the various mathematical and statistical models are proven. Presented in three parts, Application of Machine Learning in Smart Agriculture looks at the fundamentals of smart agriculture; the economics of the technology in the agricultural marketplace; and a diverse representation of the tools and techniques currently available, and in development. This book is an important resource for advanced level students and professionals working with artificial intelligence, internet of things, technology and agricultural economics. - Addresses the technology of smart agriculture from a technical perspective - Reveals opportunities for technology to improve and enhance not only yield and quality, but the economic value of a food crop - Discusses physical instruments, simulations, sensors, and markets for machine learning in agriculture
Author: Suchismita Satapathy
Publisher: Springer Nature
ISBN: 3030888282
Category : Business & Economics
Languages : en
Pages : 129
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Book Description
This book examines different innovations in worldwide agricultural-systems including the applications of artificial intelligence (AI), internet of things (IoT) and features of machine learning (ML) for the benefits of the farm-community. Specifically, it examines the use of agricultural equipment and IoT to reduce physical stress; innovative equipment that measure and reduce mental work load; and innovative techniques to help with employee safety. Featuring case studies and future implications, this book is an excellent guide for academics and researchers in the agri-sector.
Author: OECD
Publisher: OECD Publishing
ISBN: 9264545190
Category :
Languages : en
Pages : 152
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Book Description
The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.
Author: Jyotir Moy Chatterjee
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110691272
Category : Computers
Languages : en
Pages : 426
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Book Description
Agriculture is one of the most fundamental human activities. As the farming capacity has expanded, the usage of resources such as land, fertilizer, and water has grown exponentially, and environmental pressures from modern farming techniques have stressed natural landscapes. Still, by some estimates, worldwide food production needs to increase to keep up with global food demand. Machine Learning and the Internet of Things can play a promising role in the Agricultural industry, and help to increase food production while respecting the environment. This book explains how these technologies can be applied, offering many case studies developed in the research world.
Author: Ajay Agrawal
Publisher: University of Chicago Press
ISBN: 0226833127
Category : Business & Economics
Languages : en
Pages : 172
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Book Description
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309473926
Category : Science
Languages : en
Pages : 243
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Book Description
For nearly a century, scientific advances have fueled progress in U.S. agriculture to enable American producers to deliver safe and abundant food domestically and provide a trade surplus in bulk and high-value agricultural commodities and foods. Today, the U.S. food and agricultural enterprise faces formidable challenges that will test its long-term sustainability, competitiveness, and resilience. On its current path, future productivity in the U.S. agricultural system is likely to come with trade-offs. The success of agriculture is tied to natural systems, and these systems are showing signs of stress, even more so with the change in climate. More than a third of the food produced is unconsumed, an unacceptable loss of food and nutrients at a time of heightened global food demand. Increased food animal production to meet greater demand will generate more greenhouse gas emissions and excess animal waste. The U.S. food supply is generally secure, but is not immune to the costly and deadly shocks of continuing outbreaks of food-borne illness or to the constant threat of pests and pathogens to crops, livestock, and poultry. U.S. farmers and producers are at the front lines and will need more tools to manage the pressures they face. Science Breakthroughs to Advance Food and Agricultural Research by 2030 identifies innovative, emerging scientific advances for making the U.S. food and agricultural system more efficient, resilient, and sustainable. This report explores the availability of relatively new scientific developments across all disciplines that could accelerate progress toward these goals. It identifies the most promising scientific breakthroughs that could have the greatest positive impact on food and agriculture, and that are possible to achieve in the next decade (by 2030).
Author: Xanthoula-Eirini Pantazi
Publisher: Academic Press
ISBN: 0128143924
Category : Business & Economics
Languages : en
Pages : 334
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Book Description
Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. - Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture - Addresses AI use in weed management, disease detection, yield prediction and crop production - Utilizes case studies to provide real-world insights and direction
Author: Ramesh Chandra Poonia
Publisher: Academic Press
ISBN: 0323903622
Category : Computers
Languages : en
Pages : 408
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Book Description
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. - Introduces new deep learning models developed to address sustainable solutions for issues related to agriculture - Provides reviews on the latest intelligent technologies and algorithms related to the state-of-the-art methodologies of monitoring and mitigation of sustainable agriculture - Illustrates through case studies how deep learning has been used to address a variety of agricultural diseases that are currently on the cutting edge - Delivers an accessible explanation of artificial intelligence algorithms, making it easier for the reader to implement or use them in their own agricultural domain
