Application of Machine Learning in Earth Sciences

[Emperor2011]

Application of Machine Learning in Earth Sciences | 42.5 MB

Title: Application of Machine Learning in Earth Sciences
Author: Swapnil Vyas · Shridhar D. Jawak · Pramit Kumar Deb Burman · Hemlata Patel · Avinash Kandekar · Suraj Sawanta
Category: Nonfiction, Science & Nature, Technology, Agriculture & Animal Husbandry, Business & Finance, Industries & Professions, Industries
Language: English | 660 Pages | ISBN: 3031198476


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

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