Date of Award
9-11-2024
Document Type
Thesis
Degree Name
Computer Science, MS
First Advisor
Jason Causey
Second Advisor
Emily Bellis
Committee Members
Ahmed Hashem, John Nowlin, Steven Green
Call Number
ISBN 9798384077183
Abstract
Multispectral imagery collected through unmanned aerial vehicles (UAVs) holds promise for optimizing crop production to meet the rising market demands. This study aimed to enhance the main two facets behind the UAV data pipeline: data collection and data analysis. Chapter II addresses data collection challenges by presenting a software tool designed to assist UAV operators in validating and identifying potential errors in the collected images. Through beta testing, the software effectively reduced operational costs and laid a solid foundation for subsequent data analysis. Chapter III delves into the data analysis facet to develop advanced modeling approaches for accurate and robust rice yield prediction through machine and deep learning techniques. The proposed models showed significant improvements compared to traditional approaches and hold promise in providing actionable insights for informed nitrogen applications. Overall, this study contributes to improving the usability of UAV data in precision agriculture, facilitating efficient crop production to sustainably meet growing population needs.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Farag, Fared, "Developing Decision Support Tools to Optimize Crop Production Using UAV-Based Multispectral Imagery" (2024). Student Theses and Dissertations. 36.
https://arch.astate.edu/all-etd/36
