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Dissertation
EFFORTS TOWARDS EFFECTIVE ROBOTIC STRAWBERRY HARVESTING
Washington State University
Doctor of Philosophy (PhD), Washington State University
05/2024
DOI:
https://doi.org/10.7273/000006469
Abstract
Strawberry is one of the most important agricultural products in the United States and around the world. Traditionally the harvesting of these delicate fruits has been heavily reliant on a seasonal workforce, which is currently impacted by uncertainties in labor availability and increasing labor costs. The investigation of robotic strawberry harvesting has emerged as a promising alternative to address these labor-related challenges. This dissertation research focused on advancing the robotic strawberry harvesting technology by exploring the challenges posed by occlusion of ripe strawberries by other canopy parts such as leaves, vine, and immature fruit. The study introduced innovative solutions in three different areas. Firstly, the study advanced the machine vision system by leveraging the YOLO (You Only Look Once) family of deep learning models. Specifically, YOLOv5 and YOLO v8 models were modified (e.g., YOLOv5s-Straw(+C2f+SPPFCSP) and YOLOv8s(+C3x+head+αIoU)) to improve their performance in strawberry detection, which achieved the highest mean average precision (mAP) of 80.3% and the highest peak mAP of 83.2%, while maintaining real-time inference speeds on a laptop computer with GPU (RTX3070) and CPU (11800H). Secondly, this research addressed the critical task of determining the pickability of partially occluded strawberries using the YOLOv5s-cls model, which achieved an accuracy of 95.0% with a very low inference time of 2.8 ms. These machine vision models were then integrated with a robotic system designed for open-field strawberry harvesting. The robot included a commercial 6 DOF (Degree of Freedom) manipulator and a novel gripper and fan system, which were installed on a mobile platform. In the challenging open-field environments, this integrated harvester, particularly with the fan system, achieved a significantly higher picking success rate of 73.9% compared to the same achieved without the fan system (58.1%). This research findings demonstrated the potential of these technological innovations in addressing occlusion-related complexities, thereby enhancing efficiency and precision in robotic strawberry harvesting in open-field environments.
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Details
- Title
- EFFORTS TOWARDS EFFECTIVE ROBOTIC STRAWBERRY HARVESTING
- Creators
- Zixuan He
- Contributors
- Manoj Karkee (Chair)Qin Zhang (Committee Member)Sindhuja Sankaran (Committee Member)Ming Luo (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Department of Biological Systems Engineering
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Publisher
- Washington State University
- Number of pages
- 179
- Identifiers
- 99901121440301842
- Language
- English
- Resource Type
- Dissertation