Thesis Abstract

Abstract
Modern agriculture is continuously evolving with technological advancements to meet the growing demand for food production while ensuring sustainable practices. In the forestry sector, precision agriculture has emerged as a promising approach to optimize resource management and enhance productivity. This thesis explores the application of Artificial Intelligence (AI) techniques in precision agriculture for forestry management. The primary objective is to develop AI-based solutions that can analyze forestry data, provide real-time insights, and support decision-making processes to improve overall forest health and productivity. The research begins with a comprehensive review of existing literature on AI applications in agriculture and forestry, highlighting the benefits and challenges associated with these technologies. Through a detailed analysis of ten key research studies, this chapter establishes a foundation for the subsequent research methodology. The methodology section outlines the research design, data collection methods, AI algorithms utilized, and evaluation criteria employed in this study. By incorporating various AI techniques such as machine learning, computer vision, and data analytics, the research aims to develop predictive models and decision support systems tailored to forestry management. The findings chapter presents the results of the AI models developed and their performance in analyzing forestry data. Through case studies and simulations, the effectiveness of AI in optimizing planting strategies, monitoring forest health, and predicting timber yields is demonstrated. The discussion delves into the implications of these findings for forestry practitioners, emphasizing the potential of AI to revolutionize traditional forestry practices and promote sustainable resource management. In conclusion, this thesis underscores the significance of AI in enhancing precision agriculture practices in forestry management. By leveraging AI technologies, forestry stakeholders can harness the power of data-driven insights to make informed decisions, mitigate risks, and improve overall productivity. The study contributes to the growing body of research on AI applications in agriculture and forestry, offering practical recommendations for integrating AI solutions into forestry operations. Ultimately, this research sets the stage for a more sustainable and efficient approach to managing forests through the innovative use of Artificial Intelligence.

Thesis Overview