Data-Driven Farming:Haessing the Power of AI and Machine Leaing in Agriculture

Data-Driven Farming:Haessing the Power of AI and Machine Leaing in Agriculture

by: Syed Nisar Hussain Bukhari (Editor)

Publisher: Auerbach Publications
Edition:1st

Publication Date: 13 Jun. 2024

Language: English

Print Length: 282 pages

ISBN-10: 1032778725

ISBN-13: 9781032778723

Book Description

In the dynamic realm of agriculture, artificial intelligence (AI) and machine leaing (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Inteet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather pattes, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies.Data-Driven Farming:Haessing the Power of AI and Machine Leaing in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.

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