Robotics Application in Precision Spraying

This thesis presents an investigation on innovative approaches to agricultural management, addressing challenges in both viticulture and turfgrass management. The first topic of this thesis introduces the Adaptive Crop Load Estimation (ACLE) method, a deep learning-based grape counting approach designed to alleviate the need for extensive annotated datasets. By training the model on a limited set of images, this method demonstrates promising results in accurately estimating grape cluster counts across different zones in the vineyards, with an average Mean Absolute Error (MAE)/Root Mean Square Error (RMSE) of 0.86/0.66. The ACLE method aims to reduce the cost of deploying automated grape counting systems by minimizing manual image annotation efforts and enabling model reusability across different vineyards. The second topic of this thesis delves into the realm of Turfgrass management, recognizing its pivotal roles in environmental health and aesthetics. Focusing on the challenges posed by spot- based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This framework employs Unmanned Ground Vehicles (UGV) for targeted spot spraying, optimizing robot stops and trajectories based on varying scenarios such as different spot sizes and robot capabilities. The trajectory planner developed within STPAS utilizes GPS coordinates and the radius of affected areas to determine efficient stops and paths for autonomous vehicles. Comparative analysis on the developed simulators reveals that STPAS reduces the distance traveled and time taken for spot spraying by over 50% compared to conventional boom-based sprayers, thereby enhancing both economic and environmental sustainability in Turfgrass management practices.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Crop and Soil Environmental Sciences

Department dc:contributor.department

Crop and Soil Environmental Sciences

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Poudel, Puspa Kamal

Chair dc:contributor.committeechair

• Seyyedhasani, Hasan

Committee members dc:contributor.committeemember

• Sherif, Sherif Mohamed
• Nita, Mizuho

• robotics
• grape cluster mapping
• precision spraying
• planning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:39514

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/118280