Thesis Abstract

Abstract
The agricultural sector plays a crucial role in ensuring food security and sustaining economies worldwide. However, crop diseases pose a significant threat to agricultural productivity and food security. Traditional methods of disease detection and management are often time-consuming, labor-intensive, and may lack accuracy. In recent years, artificial intelligence (AI) technologies have shown promise in revolutionizing various industries, including agriculture, by providing efficient and accurate solutions to complex problems. This thesis explores the application of AI in crop disease detection and management to enhance agricultural practices and mitigate the impact of crop diseases. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions. The chapter sets the foundation for the study by outlining the importance of leveraging AI technologies in agriculture to address crop diseases effectively. Chapter 2 presents a comprehensive literature review on AI applications in agriculture, specifically focusing on crop disease detection and management. The review covers key concepts, methodologies, and technologies used in previous studies to detect and manage crop diseases using AI. By analyzing existing literature, this chapter aims to identify gaps in current research and provide a solid theoretical framework for the study. Chapter 3 details the research methodology employed in this study, including data collection methods, AI algorithms utilized, experimental design, and evaluation metrics. The chapter outlines the steps taken to develop an AI-based system for crop disease detection and management, emphasizing the importance of data quality, model training, and validation processes. Chapter 4 presents the findings of the research, highlighting the performance and effectiveness of the AI system in detecting and managing crop diseases. The chapter discusses the accuracy, efficiency, and scalability of the system, showcasing its potential to revolutionize agricultural practices and improve crop yield. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies. The chapter underscores the significance of utilizing AI for crop disease detection and management in agriculture, emphasizing the potential impact on food security, sustainability, and economic development. In conclusion, this thesis demonstrates the potential of AI technologies to transform agricultural practices by enhancing crop disease detection and management. By leveraging AI algorithms, agricultural stakeholders can make informed decisions, implement timely interventions, and ultimately improve crop yield and food security. This research contributes to the growing body of knowledge on AI applications in agriculture and underscores the importance of embracing technological advancements to address complex challenges in the agricultural sector.

Thesis Overview