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.