Thesis Abstract

Precision agriculture has emerged as a promising approach to optimize agricultural practices by utilizing advanced technologies and data-driven decision-making. This study aims to develop smart farming technologies that can enhance precision agriculture and improve overall farm productivity. The research will focus on integrating various technologies, such as Internet of Things (IoT), remote sensing, and data analytics, to enable real-time monitoring and management of agricultural processes. The study will involve the design and implementation of sensor networks for collecting data on soil moisture, temperature, nutrient levels, and crop health. These data will be analyzed using machine learning algorithms to generate actionable insights for precise irrigation, fertilization, and pest management. Additionally, the research will explore the use of unmanned aerial vehicles (UAVs) and satellite imagery for high-resolution mapping and monitoring of crop growth and field conditions. The outcomes of this study will contribute to the development of smart farming systems that can optimize resource utilization, reduce environmental impact, and increase crop yields. By implementing precision agriculture techniques, farmers can make informed decisions based on real-time data, leading to more efficient and sustainable agricultural practices. Ultimately, the integration of smart farming technologies will pave the way for a more productive and resilient agricultural sector in the face of evolving challenges such as climate change and population growth.

Thesis Overview