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Introduction

The use of drones in agriculture has gained significant attention in recent years due to their ability to capture high-resolution images of crops, monitor crop health, and assist in precision farming practices. Autonomous drones, equipped with artificial intelligence (AI) capabilities, have the potential to revolutionize the way farmers manage their crops by providing real-time data and insights. Edge AI, which involves processing data on the device itself rather than sending it to a centralized server, has emerged as a promising technology for enhancing the autonomy and efficiency of drones in agriculture.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of drones in agriculture
2.2 Applications of AI in agriculture
2.3 Edge computing in drones
2.4 Autonomous drones in agriculture
2.5 Challenges in implementing Edge AI for autonomous drones in agriculture
2.6 Previous studies on Edge AI for autonomous drones in agriculture
2.7 Current trends in the use of drones in agriculture
2.8 Integration of AI and drones in agriculture
2.9 Potential benefits of Edge AI for autonomous drones in agriculture
2.10 Future directions in the field of autonomous drones in agriculture

Chapter 3: System Design and Methodology
3.1 Selection of drone platform
3.2 Integration of AI algorithms for autonomous operation
3.3 Development of edge computing capabilities
3.4 Data collection and processing methods
3.5 Calibration and testing of the system
3.6 Evaluation metrics for performance assessment
3.7 Ethical considerations in the use of autonomous drones
3.8 Implementation of safety protocols for autonomous drones

Chapter 4: System Implementation
4.1 Hardware components used in the system
4.2 Software development for AI algorithms
4.3 Integration of sensors for data collection
4.4 Testing and validation of the system
4.5 Optimization techniques for efficient operation
4.6 Data visualization and analysis tools
4.7 User interface design for farmers
4.8 Deployment and maintenance of the system

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Discussion of results
5.3 Implications of the study for the field of agriculture
5.4 Recommendations for future research
5.5 Conclusion and final remarks

Thesis Overview:

The integration of Edge AI technology with autonomous drones has the potential to revolutionize the field of agriculture by providing farmers with real-time data and insights for better decision-making. This thesis focuses on the implementation of Edge AI for autonomous drones in agriculture, with a specific emphasis on the design, development, and testing of a system that can autonomously monitor and manage crops.

The literature review highlights the current state of research in the field of autonomous drones in agriculture, including the applications of AI, edge computing, and challenges in implementation. The system design and methodology chapter detail the selection of drone platforms, integration of AI algorithms, data collection methods, evaluation metrics, and ethical considerations. The system implementation chapter provides a comprehensive overview of the hardware and software components used in the system, testing procedures, optimization techniques, and deployment strategies.

Overall, this thesis aims to contribute to the growing body of knowledge on the use of Edge AI for autonomous drones in agriculture and provide insights into the potential benefits and challenges of implementing this technology in real-world farming practices.

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