Deep Learning for Sustainable Agriculture

Deep Learning for Sustainable Agriculture book cover

Deep Learning for Sustainable Agriculture

Author(s): Ramesh Chandra Poonia PhD (Editor), Vijander Singh (Editor), Soumya Ranjan Nayak (Editor)

  • Publisher: Academic Press
  • Publication Date: 28 Jan. 2022
  • Edition: 1st
  • Language: English
  • Print length: 406 pages
  • ISBN-10: 0323852149
  • ISBN-13: 9780323852142

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

Editorial Reviews

Review

Reviews the fundamental concepts of gathering, processing, and analyzing different deep learning models

From the Back Cover

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.

KEY FEATURES

• 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

View on Amazon

电子书代发PDF格式价格30我要求助
未经允许不得转载:Wow! eBook » Deep Learning for Sustainable Agriculture