Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs

Andrea Balcacer; Brendan Hannon; Yulia Kumar; Kuan Huang; Joseph Sarnoski; Shuting Liu; J. Jenny Li; Patricia Morreale

doi:10.1109/URTC60662.2023.10534955

Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs

Andrea Balcacer
, Brendan Hannon
, Yulia Kumar
, Kuan Huang
, Joseph Sarnoski
, Shuting Liu
, J. Jenny Li
, Patricia Morreale

Kean University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The emergence of harmful algal blooms (HABs) along the U.S. East Coast has necessitated the development of an innovative drone-based detection system, a response to the growing challenges posed by climate change and pollution. This multifaceted approach involves the collection of high-resolution images from drones and smartphone cameras, the detection and classification of algae using Transformer Neural Networks and Large Language Models (LLMs), and the application of a data segmentation approach for efficient data analysis. The system enhances traditional monitoring techniques by integrating unique custom data gathered from New Jersey's water bodies with public data collected by the state's Environmental Protection Department. It offers a more precise and efficient method for HAB detection.

Original language	English
Title of host publication	IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350308600
DOIs	https://doi.org/10.1109/URTC60662.2023.10534955
State	Published - 2023
Event	2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Hybrid, Cambridge, United States Duration: 6 Oct 2023 → 8 Oct 2023

Publication series

Name	IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings

Conference

Conference	2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023
Country/Territory	United States
City	Hybrid, Cambridge
Period	6/10/23 → 8/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

HABs classification
HABs detection
LLMs
data segmentation
drones
marine ecosystems
transformers

Access to Document

10.1109/URTC60662.2023.10534955

Cite this

Balcacer, A., Hannon, B., Kumar, Y., Huang, K., Sarnoski, J., Liu, S., Li, J. J., & Morreale, P. (2023). Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs. In IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings (IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/URTC60662.2023.10534955

Balcacer, Andrea ; Hannon, Brendan ; Kumar, Yulia et al. / Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs. IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings).

@inproceedings{21889687f7794b19a81cc4aa6f61d85c,

title = "Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs",

abstract = "The emergence of harmful algal blooms (HABs) along the U.S. East Coast has necessitated the development of an innovative drone-based detection system, a response to the growing challenges posed by climate change and pollution. This multifaceted approach involves the collection of high-resolution images from drones and smartphone cameras, the detection and classification of algae using Transformer Neural Networks and Large Language Models (LLMs), and the application of a data segmentation approach for efficient data analysis. The system enhances traditional monitoring techniques by integrating unique custom data gathered from New Jersey's water bodies with public data collected by the state's Environmental Protection Department. It offers a more precise and efficient method for HAB detection.",

keywords = "HABs classification, HABs detection, LLMs, data segmentation, drones, marine ecosystems, transformers",

author = "Andrea Balcacer and Brendan Hannon and Yulia Kumar and Kuan Huang and Joseph Sarnoski and Shuting Liu and Li, \{J. Jenny\} and Patricia Morreale",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023 ; Conference date: 06-10-2023 Through 08-10-2023",

year = "2023",

doi = "10.1109/URTC60662.2023.10534955",

language = "English",

series = "IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings",

}

Balcacer, A, Hannon, B, Kumar, Y , Huang, K , Sarnoski, J , Liu, S , Li, JJ & Morreale, P 2023, Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs. in IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings. IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023, Hybrid, Cambridge, United States, 6/10/23. https://doi.org/10.1109/URTC60662.2023.10534955

Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs. / Balcacer, Andrea; Hannon, Brendan; Kumar, Yulia et al.
IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs

AU - Balcacer, Andrea

AU - Hannon, Brendan

AU - Kumar, Yulia

AU - Huang, Kuan

AU - Sarnoski, Joseph

AU - Liu, Shuting

AU - Li, J. Jenny

AU - Morreale, Patricia

PY - 2023

Y1 - 2023

N2 - The emergence of harmful algal blooms (HABs) along the U.S. East Coast has necessitated the development of an innovative drone-based detection system, a response to the growing challenges posed by climate change and pollution. This multifaceted approach involves the collection of high-resolution images from drones and smartphone cameras, the detection and classification of algae using Transformer Neural Networks and Large Language Models (LLMs), and the application of a data segmentation approach for efficient data analysis. The system enhances traditional monitoring techniques by integrating unique custom data gathered from New Jersey's water bodies with public data collected by the state's Environmental Protection Department. It offers a more precise and efficient method for HAB detection.

AB - The emergence of harmful algal blooms (HABs) along the U.S. East Coast has necessitated the development of an innovative drone-based detection system, a response to the growing challenges posed by climate change and pollution. This multifaceted approach involves the collection of high-resolution images from drones and smartphone cameras, the detection and classification of algae using Transformer Neural Networks and Large Language Models (LLMs), and the application of a data segmentation approach for efficient data analysis. The system enhances traditional monitoring techniques by integrating unique custom data gathered from New Jersey's water bodies with public data collected by the state's Environmental Protection Department. It offers a more precise and efficient method for HAB detection.

KW - HABs classification

KW - HABs detection

KW - LLMs

KW - data segmentation

KW - drones

KW - marine ecosystems

KW - transformers

UR - https://www.scopus.com/pages/publications/85195519030

U2 - 10.1109/URTC60662.2023.10534955

DO - 10.1109/URTC60662.2023.10534955

M3 - Conference contribution

AN - SCOPUS:85195519030

T3 - IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings

BT - IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

Y2 - 6 October 2023 through 8 October 2023

ER -

Balcacer A, Hannon B, Kumar Y , Huang K , Sarnoski J , Liu S et al. Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs. In IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (IEEE MIT Undergraduate Research Technology Conference, URTC 2023 - Proceedings). doi: 10.1109/URTC60662.2023.10534955

Mechanics of a Drone-based System for Algal Bloom Detection Utilizing Deep Learning and LLMs

Abstract

Publication series

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UN SDGs

Keywords

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