For the last decade, Harbor Branch Oceanographic Institute at Florida Atlantic University (HBOI) has been developing Integrated Multi-Trophic Aquaculture (IMTA), where multiple species are farmed together. Compared with the traditional Recirculating Aquaculture Systems (RAS), the IMTA system can improve efficiency, reduce waste, and provide ecosystem services. For the IMTA system to be successful at a commercial farm scale, HBOI is developing an AI-centric Internet of Things framework to support the operations of the IMTA system. The Pseudorandom Encoded Light for Evaluating Biomass (PEEB) sensor is an endeavor in this effort to realize automated monitoring of the growth of the Sea Lettuce (Ulva lactuca), an important organism in the HBOI IMTA system. PEEB utilizes the measurements from a sequence of encoded light flashes to quantify the seaweed biomass. Such a configuration ensures the sensor can operate under different ambient light conditions and biomass densities. An improved PEEB sensor based on a unified electronic sensor design that is more robust against ambient conditions and capable of long-range data transmission is discussed. This electronic design will be the backbone to support future sensors for the IMTA system. Multiple PEEB sensors have been deployed at the HBOI IMTA system. The cloud-based storage and analysis of the sensor data are discussed.
Aerial drones have great potential to monitor large areas quickly and efficiently. Aquaculture is an industry that requires continuous water quality data to successfully grow and harvest fish. The Hybrid Aerial Underwater Robotic System (HAUCS) is designed to collect water quality data of aquaculture ponds to reduce labor costs for farmers. The routing of drones to cover each fish pond on an aquaculture farm can be reduced to the Vehicle Routing Problem. A dataset is created to simulate the distribution of ponds on a farm and is used to assess the HAUCS Path Planning Algorithm (HPP). Its performance is compared with the Google Linear Optimization Package (GLOP) and a Graph Attention Model (GAM) for routing problems. GLOP is the most efficient solver for 50 to 200 ponds at the expense of long run times, while HPP outperforms the other methods in solution quality and run time for instances larger than 200 ponds.
This article presents the experimental study of a biomass sensor to monitor the growth of macroalgae (seaweeds) in the Integrated Multi-Trophic Aquaculture (IMTA) at Harbor Branch Oceanographic Institute at Florida Atlantic University. Pseudorandom Encoded-light for Evaluating Biomass (PEEB) utilizes the measurements from a sequence of encoded light flashes to quantify the seaweed biomass. Such configuration ensures the sensor provides robust automated data acquisition under different ambient conditions and biomass densities. This data will be used to support a machine learning-based prediction biomass model, critical in any commercial-scale IMTA farm. A PEEB sensor based on an improved design has been developed based on an earlier feasibility study. The design of such a system and the initial tests at the macroalgal seaweed cultivation raceway in the HBOI IMTA system are discussed.
This paper investigates Multi-Agent Systems (MAS) related data analytics. MAS is used for modeling complex, decentralized, and real-world tasks such as package delivery by Unmanned Aircraft Systems (UAS), environmental monitoring, precision agriculture, security, disaster management, UAS Traffic Management (UTM) among others. Fish farming is one such area, where the deployment of UAS platforms could drastically improve the current labor-intensive and resource-constraint operations. This research addresses the design of mission control and path planning strategies for UASs deployed on the fish farm. The proposed strategy enables periodic monitoring of mission-critical parameters. A control strategy is designed to address the tracking control of the UAS under wind conditions.
This article presents the initial development of a biomass sensor to monitor the growth of macroalgae (seaweeds) in the Integrated Multi-Trophic Aquaculture (IMTA) at Harbor Branch Oceanographic Institute at Florida Atlantic University. The sensor utilizes a combined optical/acoustic means to quantify the seaweed biomass. Such configuration ensures the sensor providing robust coverage under different ambient conditions and biomass densities. After the biomass sensor’s performance has been validated in the lab environment, we deployed the sensor at the macroalgal seaweed cultivation raceway to quantify the seaweed density. The data processing procedures are documented, and the field test results are presented. Finally, the advantages and disadvantages of this approach and future application of the sensor to drive a machine learning-based prediction biomass model are discussed.
This paper aims to develop a robust dissolved oxygen (DO) prediction model of water quality to support the Hybrid Aerial Underwater Robotics System (HAUCS) project. Many challenges arise in developing such a model using the fish farm data collected, such as a small dataset containing missing data and noisy measurements taken in an irregular interval. An attempt to deal with these issues to obtain a robust prediction is discussed. Machine learning techniques, such as Long Short-Term Memory (LSTM) and Phased LSTM (PLSTM), are presented and motivated for dealing with the problem. The performances of LSTM and PLSTM against a larger and less problematic water quality dataset are first investigated. The attempts to transfer the knowledge of the models trained on this large dataset for fish farm DO data prediction through Transfer Learning are then reported. To mitigate the noisy measurement data, a loss function which can better deal with Gaussian noise: the correntropy loss is adopted. The long-range prediction experimental results using this Transfer Learning technique and the correntropy loss function are presented.
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