Date of Award

Spring 2024

Document Type

Thesis

Publisher

Santa Clara : Santa Clara University, 2024

Degree Name

Master of Science (MS)

Department

Mechanical Engineering

First Advisor

Christopher Kitts

Abstract

Agricultural robotics plays a critical role in addressing contemporary societal challenges, including food security, sustainable agricultural practices, and labor shortages. Effective navigation is essential for agricultural robots to traverse intricate environments and execute tasks with precision. This thesis introduces two innovative methods for in-row vineyard navigation: one leveraging branch features and the other harnessing trunk features of the vine. Through a comprehensive analysis of the two methods of in-row vineyard navigation, the study demonstrates the superior performance of the trunk detection method over the branch detection algorithm, notably surpassing current research standards in terms of crosstrack error performance. By emphasizing algorithmic innovation, this research contributes to advancing precision agriculture practices and addressing the evolving complexities of modern agriculture. The findings emphasize the importance of robust navigation systems in enhancing the sustainability and productivity of agricultural systems, thus highlighting the potential of agricultural robotics to drive positive societal change.

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