Machine Learning and Artificial Intelligence for Agricultural Economics: Prognostic Data Analytics to Serve Small Scale Farmers Worldwide

This book discusses machine learning and artificial intelligence (AI) for agricultural economics, with a focus on bringing advanced analytics and prognostics capabilities to small-scale farmers worldwide. It provides data science and software engineering teams with the skills and tools to utilize economic models for creating lifesaving applications. Chapters detail agricultural economic and AI reference architectures, focusing on data integration, algorithm development, regression, prognostics model development, and mathematical optimization.

Format: Paperback / softback
Length: 599 pages
Publication date: 06 October 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the realm of machine learning and artificial intelligence (AI) in agricultural economics, with a primary objective of extending the benefits of advanced analytics and prognostics capabilities to small-scale farmers across the globe. Written with a focus on empowering data science and software engineering teams to harness the power of economic models for the development of life-saving applications, this volume offers valuable insights and practical tools for those involved in full-scale software development within the agricultural sector.

The book begins by introducing essential agricultural economic concepts from a software development perspective, emphasizing the creation of niche blue ocean products. It then proceeds to explore various agricultural economic and AI reference architectures, focusing on data integration, algorithm development, regression analysis, prognostics model development, and mathematical optimization. By upgrading traditional AI software development paradigms to adapt to dynamic agricultural and economic markets, this volume serves as a valuable resource for researchers and students in agricultural economics, data science, engineering, machine learning, as well as engineers and industry professionals in both the public and private sectors.

The book is organized into five chapters, each dedicated to a specific aspect of agricultural economic and AI development. Chapter 1 provides an overview of machine learning and AI in agriculture, highlighting their potential applications and challenges. Chapter 2 explores the role of data in agricultural economics, discussing data collection, storage, and processing techniques. Chapter 3 delves into the development of economic models for agricultural decision-making, covering topics such as linear programming, optimization, and simulation. Chapter 4 discusses the application of machine learning algorithms in agricultural economics, including supervised learning, unsupervised learning, and reinforcement learning. Chapter 5 presents case studies and examples of successful applications of machine learning and AI in agriculture, showcasing their potential to improve crop yields, reduce food waste, and enhance resource utilization.

In conclusion, this book serves as a valuable resource for anyone interested in leveraging machine learning and AI in agricultural economics. By providing a comprehensive exploration of essential concepts, reference architectures, and practical applications, it empowers data science and software engineering teams to develop innovative solutions that can have a significant impact on the lives of small-scale farmers worldwide. Whether you are a researcher, student, engineer, or industry professional, this book will provide you with the skills and knowledge necessary to advance the field of agricultural economics and contribute to a more sustainable and food-secure future.

Weight: 937g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030774875
Edition number: 1st ed. 2021