Document Type

Thesis - Open Access

Award Date


Degree Name

Master of Science (MS)

Department / School

Mathematics and Statistics

First Advisor

Hossein Moradi Rekabdarkolaee


Precision agriculture, statistics


As the population of the Earth increases, there is a growing need for food to feed the inhabitants. Precision agriculture offers techniques and tools that can be used to help accommodate the growing population. One specific precision agriculture tool is remote sensing data, which can be used to image fields as an effort to better predict or understand the crops. In this thesis, deep neural networks are used to evaluate various spatial, spectral, and temporal resolutions of three different satellite images to determine which best predicts corn yield. The main metrics we used to evaluate the models were R-squared (R2), root mean squared error (RMSE), and mean absolute error (MAE). Regarding spectral resolutions, our results suggest that more granularity produces better models. For spatial resolutions, our results suggest less granularity performs better. Additionally, our results found that high frequency temporal resolution does not produce perform better than low frequency temporal resolution.

Library of Congress Subject Headings

Precision farming.
Crop yields -- Remote sensing.
Neural networks (Computer science)
Agricultural estimating and reporting.

Number of Pages



South Dakota State University



Rights Statement

In Copyright