Varroa destructor detection on honey bees using hyperspectral imagery

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151729" target="_blank" >RIV/00216305:26220/24:PU151729 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0168169924006100?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0168169924006100?pes=vor</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.compag.2024.109219" target="_blank" >10.1016/j.compag.2024.109219</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Varroa destructor detection on honey bees using hyperspectral imagery

Popis výsledku v původním jazyce

Hyperspectral (HS) imagery in agriculture is becoming increasingly common. These images have the advantage of higher spectral resolution. Advanced spectral processing techniques are required to unlock the information potential in these HS images. The present paper introduces a method rooted in multivariate statistics designed to detect parasitic Varroa destructor mites on the body of western honey bee Apis mellifera, enabling easier and continuous monitoring of the bee hives. The present paper is the first to utilize hyperspectral imagery for the task, previous studies existing only for multispectral imagery. The methodology explores unsupervised (K-means++) and recently developed supervised (Kernel Flows-Partial Least-Squares, KF-PLS) methods for parasitic identification. Additionally, in light of the emergence of custom-band multispectral cameras, the present research outlines a strategy for identifying the specific wavelengths necessary for effective bee-mite separation, suitable for implementation in a custom-band camera. Illustrated with a real-case dataset, our findings demonstrate that as few as four spectral bands are sufficient for accurate parasite identification.

Název v anglickém jazyce

Varroa destructor detection on honey bees using hyperspectral imagery

Popis výsledku anglicky

Hyperspectral (HS) imagery in agriculture is becoming increasingly common. These images have the advantage of higher spectral resolution. Advanced spectral processing techniques are required to unlock the information potential in these HS images. The present paper introduces a method rooted in multivariate statistics designed to detect parasitic Varroa destructor mites on the body of western honey bee Apis mellifera, enabling easier and continuous monitoring of the bee hives. The present paper is the first to utilize hyperspectral imagery for the task, previous studies existing only for multispectral imagery. The methodology explores unsupervised (K-means++) and recently developed supervised (Kernel Flows-Partial Least-Squares, KF-PLS) methods for parasitic identification. Additionally, in light of the emergence of custom-band multispectral cameras, the present research outlines a strategy for identifying the specific wavelengths necessary for effective bee-mite separation, suitable for implementation in a custom-band camera. Illustrated with a real-case dataset, our findings demonstrate that as few as four spectral bands are sufficient for accurate parasite identification.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

COMPUTERS AND ELECTRONICS IN AGRICULTURE

ISSN

0168-1699

e-ISSN

1872-7107

Svazek periodika

224

Číslo periodika v rámci svazku

září 2024

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

„“-„“

Kód UT WoS článku

001271974900001

EID výsledku v databázi Scopus

2-s2.0-85198275920