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ShellCollect: A Framework for Smart Precision Shellfish Harvesting Using Data Collection Path Planning
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AI
DOI：10.1109/ACCESS.2024.3505849.png)
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Abstract
En 中文
Shellfish aquaculture significantly contributes to the coastal economy of the United States. However, the current primary and most productive harvesting method for on-bottom shellfish farming-dredging with tow dredges -is highly inefficient, as farmers typically tow the dredges blindly and randomly in their fields. To enhance efficiency, we propose ShellCollect, a smart precision shellfish harvesting framework that generates harvesting paths based on underwater oyster distributions. We formulate this problem as data collection path planning and solve it by a Variable Neighborhood Search (VNS) based approach with a novel simplification scheme called merge-and-filtering. Additionally, we introduce ShellMapSim, a dedicated simulator for designing and evaluating harvesting path-planning methods. Through simulations in ShellMapSim, we demonstrate the effectiveness of our method across diverse oyster distributions. Moreover, we conducted field tests to validate the viability of our ShellCollect framework for real-world applications. To the best of our knowledge, this work is the first to address smart precision harvesting in shellfish.
Keywords:
Aquaculture
Data collection
Path planning
Boats
Turning
Sea measurements
Water quality
Temperature sensors
Temperature measurement
Sonar navigation
Shellfish aquaculture
smart precision harvesting
data collection path-planning
simulator