Theses and Dissertations

ORCID

https://orcid.org/0009-0004-0625-405X

Advisor

Ball, John

Committee Member

Gurbuz, Ali

Committee Member

Senyurek, Volkan

Committee Member

Diao, Junming

Date of Degree

5-15-2026

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Electrical & Computer Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

Accurate soil moisture (SM) measurement and crop yield estimation are critical components of modern precision agriculture (PA) management. Efficiently observing SM at high resolution on a sub-field scale can enhance irrigation planning and management, leading to improved yields and product quality while also conserving environmental resources. Yield predictions provide insights into expected production, facilitating optimized resource allocation, improved agricultural management strategies, and enhanced profitability. Unmanned Aircraft Systems (UAS) based multi-sensor receiver systems offer promising solutions in obtaining high-resolution SM measurements across large fields where satellite remote sensing falls short, as well as collecting necessary data over corn and cotton for timely yield estimation of these important crops. First, this study has developed a custom-made UAS-based passive GNSS-R (Global Navigation Satellite Systems Reflectometry) receiver system for soil moisture (SM) retrievals at the sub-field scale (higher spatial and temporal resolution) to identify relevant features and normalization techniques. From the three years of data collection over 2.31 hectares of corn and cotton fields, incorporating GNSS-R, multispectral imaging, LiDAR (Light Detection and Ranging), and in-situ SM measurements, the impact of receiver antenna characteristics, surface factors, and GNSS constellations on GNSS-R measurements are investigated. The results highlighted both the potential and the challenges of using a low-cost GNSS-R receiver system from a mid-size small UAS platform for accurate and reliable high-resolution SM measurement in PA. Next, a multi-year, multi-sensory dataset was compiled from UAS-mounted multispectral and LiDAR sensors, in-situ soil moisture probes, and weather station records, yielding over 30 weekly extracted features across five categories and 235 ground-truth yield observations from field plots over four growing seasons. This subsequent study outlines the methodology for feature selection and investigates the application of machine learning (ML) techniques, including Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), and Random Forest (RF) models, for predicting corn and cotton yields using spatiotemporal data. Using percentile root mean square error (RMSE) and mean absolute error (MAE) as performance metrics, the proposed ML-based approach, validated through year-based and field-wise cross-validation methods, demonstrates the effectiveness of using UAS-collected multi-sensor data for accurate crop yield estimation in PA.

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