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. / Master of Science / This thesis explores solutions for improving agricultural practices, specifically focusing on grapevine cultivation and turfgrass management. The first part introduces a novel method called Adaptive Crop Load Estimation (ACLE), which employs deep learning to accurately count grape clusters in vineyards. Unlike traditional methods requiring extensive annotated data, ACLE demonstrates significant results with minimal training images, aiming to reduce the cost of automated grape counting systems and enhance their adaptability across various vineyards.
In the second part, the thesis delves into development of planning algorithm for precision spot spraying. Addressing challenges posed by spot-based diseases, the study introduces the Spot Treatment Pathfinding and Scheduling (STPAS) method. This algorithm provides robot stops and optimizes routes based on different scenarios such as spot sizes and robot capabilities. Comparative analysis of the simulation results reveals that STPAS improves efficiency, reducing both the distance traveled and time taken for spot spraying compared to boom-based sprayers. This not only benefits economic considerations but also contributes to environmental sustainability in turfgrass management practices.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/118280 |
| Date | 05 March 2024 |
| Creators | Poudel, Puspa Kamal |
| Contributors | Crop and Soil Environmental Sciences, Seyyedhasani, Hasan, Sherif, Sherif Mohamed, Nita, Mizuho |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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