Deep Learning for Sustainable Agriculture

Deep Learning for Sustainable Agriculture

1st Edition - January 9, 2022

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  • Editors: Ramesh Poonia, Vijander Singh, Soumya Nayak
  • eBook ISBN: 9780323903622
  • Paperback ISBN: 9780323852142

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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.

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

Readership

Academics and Senior Graduates; university and faculty teachers, instructors and senior students in Computer Science working in the area of deep learning and agriculture. Environmentalists working in the field of agriculture science

Table of Contents

  • Cover image
  • Title page
  • Table of Contents
  • Copyright
  • Contributors
  • Chapter 1: Smart agriculture: Technological advancements on agriculture—A systematical review
  • Abstract
  • 1: Introduction
  • 2: Methodology
  • 3: Role of image processing in agriculture
  • 4: Role of Machine Learning in Agriculture
  • 5: Role of deep learning in agriculture
  • 6: Role of IoT in agriculture
  • 7: Role of wireless sensor networks in agriculture
  • 8: Role of data mining in agriculture
  • 9: Conclusion
  • References
  • Chapter 2: A systematic review of artificial intelligence in agriculture
  • Abstract
  • 1: Precision farming
  • 2: Plant disease detection
  • 3: Soil health monitoring using AI
  • 4: Scope and challenges of AI in agriculture
  • 5: Conclusions
  • References
  • Chapter 3: Introduction to deep learning in precision agriculture: Farm image feature detection using unmanned aerial vehicles through classification and optimization process of machine learning with convolution neural network
  • Abstract
  • 1: Introduction
  • 2: Deep learning overview
  • 3: CNN training
  • 4: Methodology
  • 5: Experiment and results
  • 6: Discussion
  • 7: Conclusion
  • References
  • Chapter 4: Design and implementation of a crop recommendation system using nature-inspired intelligence for Rajasthan, India
  • Abstract
  • 1: Introduction
  • 2: Literature survey
  • 3: Proposed methodology
  • 4: Results
  • 5: Conclusion and future work
  • References
  • Further reading
  • Chapter 5: Artificial intelligent-based water and soil management
  • Abstract
  • 1: Introduction
  • 2: Applications of artificial intelligence in water management
  • 3: Applications of artificial intelligence in soil management
  • 4: Conclusion and recommendations for water-soil management
  • References
  • Chapter 6: Machine learning for soil moisture assessment
  • Abstract
  • 1: Introduction
  • 2: Overview of machine learning
  • 3: Machine learning algorithms applied in soil moisture research
  • 4: Applications of machine learning for soil moisture assessment
  • 5: Conclusions
  • References
  • Chapter 7: Automated real-time forecasting of agriculture using chlorophyll content and its impact on climate change
  • Abstract
  • 1: Introduction
  • 2: Current status
  • 3: Problem statement
  • 4: Objective of the proposed work
  • 5: Research highlights
  • 6: Scientific significance of the proposed work
  • 7: Materials and methods
  • 8: Detailed work plan to achieve the objectives
  • 9: Results and discussion
  • 10: Conclusion
  • References
  • Chapter 8: Transformations of urban agroecology landscape in territory transition
  • Abstract
  • 1: Introduction
  • 2: Agroecological landscapes
  • 3: Agroecological practices
  • 4: Agroecological territorial transformation and transition
  • 5: Conclusion
  • References
  • Chapter 9: WeedNet: A deep neural net for weed identification
  • Abstract
  • 1: Introduction
  • 2: Related work
  • 3: WeedNet
  • 4: Evaluation strategy
  • 5: Experimental setup
  • 6: Experimental evaluation
  • 7: Conclusion
  • References
  • Chapter 10: Sensors make sense: Functional genomics, deep learning, and agriculture
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Section I. Functional genomics
  • 3: Section II. DAS networks
  • 4: Section III. GRANITE and the agent-based GRANITE Network Discovery Tool
  • 5: Conclusions
  • References
  • Chapter 11: Crop management: Wheat yield prediction and disease detection using an intelligent predictive algorithms and metrological parameters
  • Abstract
  • 1: Introduction
  • 2: Literature review
  • 3: Discussion
  • 4: Conclusion and future scope
  • References
  • Chapter 12: Sugarcane leaf disease detection through deep learning
  • Abstract
  • 1: Introduction
  • 2: Methodology
  • 3: Experimentation
  • 4: Results and discussion
  • 5: Conclusion
  • References
  • Chapter 13: Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture
  • Abstract
  • 1: Introduction
  • 2: Applications of geospatial analytics for agriculture
  • 3: Material analysis
  • 4: System model design and implementation
  • 5: System evaluation
  • 6: Discussion
  • 7: Conclusions
  • References
  • Chapter 14: Artificial intelligence-based detection and counting of olive fruit flies: A comprehensive survey
  • Abstract
  • Acknowledgments
  • 1: Introduction
  • 2: Literature survey of recognition systems
  • 3: Evaluation and discussions
  • 4: Conclusions
  • References
  • Index

Product details

  • No. of pages: 406
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: January 9, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780323903622
  • Paperback ISBN: 9780323852142

About the Editors

Ramesh Poonia

Dr. Ramesh Chandra Poonia is an Associate Professor at the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India. Recently completed his Postdoctoral Fellowship from CPS Lab, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, Ålesund, Norway. He has received his Ph.D. degree in Computer Science from Banasthali University, Banasthali, India in July 2013. His research interests are Cyber-Physical Systems, Network Protocol Evaluation and Artificial Intelligence. He is Chief Editor of TARU Journal of Sustainable Technologies and Computing (TJSTC) and Associate Editor of the Journal of Sustainable Computing: Informatics and Systems, Elsevier. He also serves in the editorial boards of a few international journals. He is main author and co-author of 06 books and an editor of more than 25 special issue of journals and books including Springer, CRC Press – Taylor and Francis, IGI Global and Elsevier, edited books and Springer conference proceedings and has authored/co-authored over 65 research publications in peer-reviewed reputed journals, book chapters and conference proceedings.

Affiliations and Expertise

Associate Professor Department of Computer Science,CHRIST (Deemed to be University), Bangalore, Karnataka, India

Vijander Singh

Dr. Vijander Singh is working as Assistant Professor, Department of Computer Science and Engineering, Manipal University Jaipur, India. He received Ph.D. degree from Banasthali University, Banasthali, India, in April 2017. He has published 25 research papers in indexed journals and several book chapters for international publishers. He authored two books and handled/handling journals of international repute such as Taylor & Francis, Taru Publication, IGI Global, Inderscienc, etc. as guest editor. He is an associate editor of TARU Journal of Sustainable Technologies and Computing (TJSTC). He has organized several International Conferences, FDPs, and Workshops as a core team member of the organizing committee. His research area includes Machine Learning, Deep Learning, Precision Agriculture, and Networking.

Affiliations and Expertise

Associate Professor, Department of Computer Science and Engineering, Manipal University Jaipur, India

Soumya Nayak

Soumya Ranjan Nayak is an assistant professor at the Amity School of Engineering and Technology, Amity University, Noida, India. He received his PhD degree in computer science and engineering under the MHRD Govt. of India. He has published over 80 articles in peer-reviewed journals and conferences of international repute like Elsevier, Springer, World Scientific, IOS Press, Taylor & Francis, Inderscience, and IGI Global. His current research interests include medical image analysis and classification, machine learning, deep learning, pattern recognition, fractal graphics, and computer vision. He serves as a reviewer for many peer-reviewed journals such as IEEE Journal of Biomedical and Health Informatics, IEEE Access, Applied Mathematics and Computation, Journal of Applied Remote Sensing, Mathematical Problems in Engineering, International Journal of Light and Electron optics, Journal of Intelligent and Fuzzy Systems, Future Generation Computer Systems, and Pattern Recognition Letters.

Affiliations and Expertise

Assistant Professor, Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India

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