Robotic Pruning for Indoor Indeterminate Plants

This thesis presents an innovative agricultural automation technique which focuses on addressing the significant perception challenges posed by occlusion within environments such as farms and greenhouses. Automated systems tasked with duties like pruning face considerable difficulties due to occlusion, complicating the accurate identification of plant features. To tackle these challenges, this work introduces a novel approach utilizing a LiDAR camera mounted on a robot arm, enhancing the system's ability to scan plants and dynamically adjust the arm's trajectory based on machine learning-derived segmentation. This adjustment significantly increases the detection area of plant features, improving identification accuracy and efficiency. Building on foreground isolation and instance segmentation, the thesis then presents an automated method for identifying optimal pruning points using best pose view images of indeterminate tomato plants. By integrating advanced image processing techniques, the proposed method ensures the pruning process by targeting branches with the highest leaf load. Experimental validation of the proposed method was conducted in a simulated environment, where it demonstrated substantially enhanced performance. In terms of pruning point identification, the method achieved impressive results with 94% precision, 90% recall, and 92% F1 score for foreground isolation. Furthermore, the segmentation of isolated images significantly outperformed non-isolated ones, with improvements exceeding 30%, 27%, and 30% in precision, recall, and F1 metrics, respectively. This validation also confirmed the method's effectiveness in accurately identifying pruning points, achieving a 67% accuracy rate when compared against manually identified pruning points. These results underscore the robustness and reliability of the approach in automating pruning processes in agricultural settings.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Srivastava, Chhayank

Chair dc:contributor.committeechair

• Seyyedhasani, Hasan

Committee members dc:contributor.committeemember

• Li, Song
• Akbari Hamed, Kaveh
• Mueller, Rolf

• Robotic imaging
• robotic pruning
• controlled environment agriculture
• foreground isolation
• automated segmentation

Statement dc:rights

• Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-sa/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41149

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/120574