Call for Chapters: Computer Vision Techniques for Agricultural Advancements

Editors

Dibya Bora, The Assam Kaziranga University, India
Rubul Bania, Birangana Sati Sadhani Rajyik Vishwavidyalaya (BSSRV), India

Call for Chapters

Proposals Submission Deadline: July 31, 2024
Full Chapters Due: October 2, 2024
Submission Date: October 2, 2024

Introduction

In recent years, the convergence of cutting-edge technologies with traditional agricultural practices has paved the way for transformative changes in the way we cultivate, monitor, and manage crops. Among these technologies, computer vision stands out as a powerful tool, offering unprecedented insights and capabilities to revolutionize various facets of agriculture. Our book, "Computer Vision Techniques for Agricultural Advancements," finds its purpose within this dynamic landscape. As global population growth and environmental challenges place increasing pressure on food production systems, the need for innovative solutions in agriculture has never been more urgent. Computer vision, with its ability to analyze visual data and extract actionable insights, holds immense potential to address these challenges. From crop monitoring and disease detection to precision agriculture and environmental sustainability, computer vision is reshaping agricultural practices and paving the way for a more resilient and sustainable future. "Computer Vision Techniques for Agricultural Advancements" aims to explore the dynamic intersection of computer vision, artificial intelligence (AI) and agriculture, offering a comprehensive overview of the latest trends, challenges, and opportunities in this rapidly evolving field. Through a series of in-depth chapters, we examine into various applications of computer vision across different stages of agricultural production and management, showcasing its impact on productivity, efficiency, and sustainability. The book is structured to provide both theoretical foundations and practical insights, catering to a diverse audience of researchers, practitioners, and policymakers. Each chapter will offer a synthesis of recent advances in computer vision techniques, accompanied by real-world case studies and examples, to illustrate their effectiveness in addressing the pressing challenges facing the agriculture sector. Furthermore, this book aims to stimulate further research, collaboration, and innovation in the field of agricultural computer vision. By identifying key challenges, emerging trends, and future directions, we hope to inspire readers to explore new possibilities and leverage cutting-edge technologies to drive transformative changes in agriculture. We invite readers to embark on this journey with us as we explore the potential of technology to shape the future of farming and ensure food security, environmental stewardship, and rural livelihoods on a global scale.

Objective

The primary objective of this book is to serve as a comprehensive guide for researchers, practitioners, and policymakers interested in leveraging computer vision for agricultural advancements. By presenting a synthesis of recent advances in computer vision techniques, case studies, and real-world applications, this book aims to: ● Provide a thorough understanding of the foundational principles and methodologies of computer vision in agricultural contexts. ● Explore the latest trends and innovations in computer vision algorithms, including deep learning architectures, convolutional neural networks, and multi-modal data fusion techniques. ● Showcase the diverse applications of computer vision and AI across different stages of agricultural production and management, highlighting their impact on productivity, efficiency, and sustainability. ● Identify key challenges and opportunities in the integration of computer vision with other emerging technologies, such as IoT, drones, and satellite imaging for some applications in the field of agriculture. ● Stimulate further research, collaboration, and innovation in the field of agricultural computer vision, with a focus on addressing societal, environmental, and economic needs.

Target Audience

The target audience for the book "Computer Vision Techniques for Agricultural Advancements" likely includes: 1. Researchers and Academics: Those who are involved in the study and development of computer vision technologies, especially as they apply to agricultural sciences and practices. 2. Agricultural Engineers and Technologists: Professionals working on the implementation and innovation of technology in the agricultural sector, looking for the latest trends and techniques in computer vision. 3. Agribusiness Professionals: Individuals in the agricultural industry seeking to understand how advanced technologies can improve productivity, efficiency, and crop management. 4. Graduate and Postgraduate Students: Students in fields such as agricultural science and engineering, computer science, and related disciplines who are focusing on the application of computer vision in agriculture for their studies and research projects. 5. Technology Developers and Startups: Entrepreneurs and developers who are building new tools and applications for the agricultural sector and need insights into the latest computer vision techniques and trends. 6. Policy Makers and Agricultural Consultants: Those involved in decision-making and advisory roles within the agricultural sector, aiming to stay informed about technological advancements that can shape future policies and recommendations. 7. Educators: Teachers and professors who are looking to incorporate the latest developments in computer vision and agricultural technology into their curricula. 8. Investors and Venture Capitalists: Investors interested in the agricultural technology sector who want to understand emerging trends and opportunities in computer vision applications.

Recommended Topics

• Machine Learning Algorithms for Crop Disease Detection • Advanced Imaging Techniques for Soil Quality Analysis • Autonomous Drone Systems for Precision Agriculture • Deep Learning Models for Automated Harvesting • Multi-Spectral and Hyperspectral Imaging for Plant Health Monitoring • Real-Time Data Processing in Agricultural Robotics • Computer Vision-Based Weed Identification and Control • Livestock Behavior and Health Analysis Using Computer Vision • Integration of IoT and Computer Vision in Smart Farming • High-Resolution Satellite Imagery for Crop Yield Prediction • Object Detection and Classification in Agricultural Environments • Sensor Fusion Techniques for Enhanced Agricultural Monitoring • Remote Sensing Applications in Agricultural Management • Edge Computing for On-Farm Data Analytics • Ethical and Privacy Considerations in Agricultural Surveillance Systems • Image Segmentation Techniques for Crop and Weed Differentiation • 3D Imaging and Reconstruction for Plant Phenotyping • AI-Powered Soil Erosion and Moisture Detection • Development of Low-Cost Computer Vision Solutions for Smallholder Farmers • Machine Vision Systems for Post-Harvest Quality Assessment • UAVs (Unmanned Aerial Vehicles) in Precision Agriculture • Augmented Reality (AR) and Virtual Reality (VR) for Agricultural Training • Data Augmentation Techniques for Agricultural Image Datasets • Predictive Analytics for Pest and Disease Outbreaks Using Computer Vision • Automated Fruit Counting and Yield Estimation • Climate Adaptation Strategies Using Computer Vision in Agriculture • Integration of Blockchain with Computer Vision for Traceability in Agriculture • Cross-Disciplinary Approaches to Enhancing Computer Vision in Agriculture • Robotic Systems for Precision Livestock Farming • AI and Machine Learning for Sustainable Agriculture Practices • Monitoring and Mapping Crop Growth Stages Using Computer Vision • Thermal Imaging for Irrigation Management and Water Stress Detection • Evaluating Soil Fertility and Nutrient Deficiency with Computer Vision • Smart Greenhouses: Automated Control and Monitoring Systems • Harvesting Robots: Challenges and Innovations in Design and Implementation • Using Computer Vision for Real-Time Pest Surveillance and Control • Agricultural Supply Chain Optimization with Computer Vision Technologies • Advances in Field Phenotyping: Assessing Plant Traits with Computer Vision • AI-Driven Predictive Maintenance for Agricultural Equipment • Enhancing Food Safety and Quality Control with Computer Vision Systems

Submission Procedure

Researchers and practitioners are invited to submit on or before July 31, 2024, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by August 14, 2024 about the status of their proposals and sent chapter guidelines.Full chapters are expected to be submitted by October 2, 2024, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Computer Vision Techniques for Agricultural Advancements. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.



Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2025.



Important Dates

July 31, 2024: Proposal Submission Deadline
August 14, 2024: Notification of Acceptance
October 2, 2024: Full Chapter Submission
November 6, 2024: Review Results Returned
December 4, 2024: Final Acceptance Notification
December 11, 2024: Final Chapter Submission



Inquiries

Dibya Bora The Assam Kaziranga University research4dibya@gmail.com Rubul Bania Birangana Sati Sadhani Rajyik Vishwavidyalaya (BSSRV) rubul.bania@gmail.com

Classifications


Business and Management; Computer Science and Information Technology; Education; Life Sciences; Physical Sciences and Engineering
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