Vegetation Health Prediction Using RGB Images From Uav With Deep Learning

Author

Masud Rana, Prairie View A&M University

Date of Award

12-2025

Document Type

Thesis

Degree Name

Master of Science

Degree Discipline

Electrical Engineering

Abstract

Vegetation health prediction is one of the most important research questions in precision agriculture. The Normalized Difference Vegetation Index (NDVI) is the most widely adopted indicator of vegetation health, such as crop vigor and stress. Traditional NDVI estimation relies on multispectral or hyperspectral imaging, which is expensive and inaccessible for small-scale farming. Recent advances in deep learning enable the prediction of NDVI from low-cost RGB imagery; however, the limited availability of paired RGB-NDVI datasets and domain discrepancies between existing sources hinder robust generalization. In this thesis, we constructed UAV-based RGB-NDVI paired datasets and investigated multiple deep learning architectures, including a Fully Connected Neural Network (FCNN), an RGB-Split Convolutional Neural Network (RSCNN), and an Autoencoder with Dual Loss (ADL). Our experimental results showed that the RSCNN consistently delivered the strongest regression performance across datasets, achieving R2 values of 82.43% and 89.53%, and the lowest MAE of 1.87% and 1.57% on Dataset 1 and Dataset 2, respectively. Furthermore, we studied transfer learning and proposed a self-learning approach that relied on pseudo-labeled target samples. The results demonstrated improved performance that varied with the chosen confidence threshold and the transfer direction.

Index Terms: ADL, deep learning, FCNN, hyperspectral imagery, multispectral imagery, NDVI, precision agriculture, RSCNN, self-learning, transfer learning.

Committee Chair/Advisor

Lijun Qian

Committee Member

Xishuang Dong

Committee Member

Xiangfang Li

Committee Member

Ram Ray

Publisher

Prairie View A&M University

Rights

Date of Digitization

12/02/2025

Contributing Institution

J. B . Coleman Library

City of Publication

Prairie View

MIME Type

Application/PDF

Recommended Citation

Rana, M. (2025). Vegetation Health Prediction Using RGB Images From Uav With Deep Learning. Retrieved from https://digitalcommons.pvamu.edu/pvamu-theses/1656