Advancing Water Hyacinth Recognition: Integration of Deep Learning and Multispectral Imaging for Precise Identification DOI Creative Commons
Diego Alberto Herrera Ollachica, Bismark Kweku Asiedu Asante,

Hiroki Imamura

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(4), С. 689 - 689

Опубликована: Фев. 18, 2025

The aquatic plant species Eichhornia crassipes, commonly known as water hyacinth, is indigenous to South America and considered an invasive species. hyacinth has caused significant economic ecological damage by preventing sunlight from penetrating the surface of water, resulting in loss life. To quantify invasiveness address issue accurately identifying species, hyacinths have prompted numerous researchers propose approaches detect regions occupied hyacinths. One such solution involves utilization multispectral imaging which obtain detailed information about based on reflectance index. This achieved analyzing intensity light spectra at different wavelengths emitted each plant. However, use imagery presents a potential challenge since there are various spectral indices that can be used capture information. Despite high accuracy these images, remains possibility plants similar may misclassified if right index not chosen. Considering this challenge, objective research develop low-cost camera capable capturing images. will equipped with two infrared spectrum filters 720 850 nanometers, respectively, well red, blue, green filters. Additionally, implementation U-Net architecture proposed for semantic segmentation identify hyacinths, other classes lakes land. An rate 96% was obtained identification using data captured autonomous drone constructed laboratory flying altitude 10 m. We also analyzed contribution layers camera’s setup.

Язык: Английский

Advancing Water Hyacinth Recognition: Integration of Deep Learning and Multispectral Imaging for Precise Identification DOI Creative Commons
Diego Alberto Herrera Ollachica, Bismark Kweku Asiedu Asante,

Hiroki Imamura

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(4), С. 689 - 689

Опубликована: Фев. 18, 2025

The aquatic plant species Eichhornia crassipes, commonly known as water hyacinth, is indigenous to South America and considered an invasive species. hyacinth has caused significant economic ecological damage by preventing sunlight from penetrating the surface of water, resulting in loss life. To quantify invasiveness address issue accurately identifying species, hyacinths have prompted numerous researchers propose approaches detect regions occupied hyacinths. One such solution involves utilization multispectral imaging which obtain detailed information about based on reflectance index. This achieved analyzing intensity light spectra at different wavelengths emitted each plant. However, use imagery presents a potential challenge since there are various spectral indices that can be used capture information. Despite high accuracy these images, remains possibility plants similar may misclassified if right index not chosen. Considering this challenge, objective research develop low-cost camera capable capturing images. will equipped with two infrared spectrum filters 720 850 nanometers, respectively, well red, blue, green filters. Additionally, implementation U-Net architecture proposed for semantic segmentation identify hyacinths, other classes lakes land. An rate 96% was obtained identification using data captured autonomous drone constructed laboratory flying altitude 10 m. We also analyzed contribution layers camera’s setup.

Язык: Английский

Процитировано

0