Document Type

Dissertation - Open Access

Award Date

2020

Degree Name

Doctor of Philosophy (PhD)

Department / School

Electrical Engineering and Computer Science

First Advisor

Qiquan Qiao

Keywords

Internet of Things, Machine Learning, Precision Agriculture, Sensor-Node, Super-Node

Abstract

Recent studies assumed that the world population would reach 10.3 billion by 2070. This will require more land for housing; simultaneously resulting in a loss of land for agricultural purposes. However, the new generations also need food, and the lack of new agrarian land is a critical reason that leads researchers and producers to improve daily agriculture practices by using precision agriculture concepts and technologies to increase yield and crop quality. This work represents the design, development, and testing of a customizable and cost-effective Weather-Soil Sensor Station (W-SSS) for use in Precision Agriculture based on high accuracy sensors, wireless communication, cloud data storage, and computation technology. Also, it illustrates empirical models developed based on advanced Machine Learning (ML) algorithms to predict LWD on the canopy of the soybean crop in the eastern region of South Dakota. The sensor data was evaluated using ML-based models including Gradient Boosting Tree and Random Forest to forecast LWD with an accuracy greater than 95%. The information obtained from the W-SSS demonstrates the unique variations in weather and soil conditions which, combined with ML analysis, will enable farmers to enhance their decision-making strategies.

Library of Congress Subject Headings

Precision farming.
Wireless sensor networks.
Internet of things.
Machine learning.
Leaves -- Moisture -- Measurement.
Soybean -- South Dakota.

Format

application/pdf

Number of Pages

169

Publisher

South Dakota State University

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Rights Statement

In Copyright