Thesis Abstract

Abstract
This thesis explores the application of remote sensing technology in monitoring crop health and predicting yield in precision agriculture. The advancement of remote sensing technology has revolutionized the agricultural sector by providing valuable data for better decision-making processes. The primary objective of this research is to investigate the effectiveness of remote sensing techniques in monitoring crop health and predicting yield, with a focus on enhancing precision agriculture practices. The study begins with a comprehensive introduction to precision agriculture, highlighting the importance of utilizing advanced technologies to optimize agricultural practices. A detailed literature review is presented, discussing the various remote sensing technologies and methodologies used in monitoring crop health and predicting yield. The review also addresses the challenges and limitations associated with these technologies, providing a foundation for the research methodology. The research methodology section outlines the approach taken to collect and analyze data for the study. Various remote sensing techniques, such as satellite imagery, drones, and spectral analysis, are utilized to monitor crop health indicators and predict yield. The methodology also includes field experiments and data processing techniques to validate the accuracy of the remote sensing data. The findings of the study reveal the effectiveness of remote sensing technology in monitoring crop health indicators, such as vegetation indices, chlorophyll content, and water stress levels. The results also demonstrate the potential of remote sensing in predicting crop yield based on the collected data. The discussion section highlights the implications of the findings for precision agriculture practices and emphasizes the importance of integrating remote sensing technology into agricultural management strategies. In conclusion, this thesis provides valuable insights into the application of remote sensing technology for monitoring crop health and predicting yield in precision agriculture. The study underscores the significance of leveraging advanced technologies to enhance agricultural productivity and sustainability. Recommendations for future research include exploring the integration of artificial intelligence and machine learning algorithms to further improve the accuracy and efficiency of remote sensing techniques in agriculture. Keywords Remote Sensing, Precision Agriculture, Crop Health Monitoring, Yield Prediction, Agricultural Technology.

Thesis Overview