Machine learning for biodiversity: UAV-based flower detection as an indirect proxy for bee abundance

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F25%3A103018" target="_blank" >RIV/60460709:41330/25:103018 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1574954125003553" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1574954125003553</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecoinf.2025.103346" target="_blank" >10.1016/j.ecoinf.2025.103346</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning for biodiversity: UAV-based flower detection as an indirect proxy for bee abundance

Popis výsledku v původním jazyce

Pollination plays a crucial role in supporting agriculture and ecosystem functioning, making it an essential ecosystem service provided by bees and other insects. However, bee populations are increasingly threatened by habitat fragmentation, intensive agriculture, and climate change, among other threats. Improved monitoring of critical habitat factors, such as flower cover, is crucial to restore pollinator populations. Traditional approaches, such as field analyses, are often time-consuming and expensive, prompting the adoption of alternative methods to achieve greater efficiency and cost-effectiveness. Here, we introduce a novel approach that incorporates machine learning algorithms and optical images obtained from an unoccupied aerial vehicle (UAV). Using machine learning methods on RGB UAV imagery enabled us to estimate flower cover in UAV-monitored areas and make numerical inferences of wild bee pollinator abundance from those estimates. Unlike our previous study, which relied on separate machine learning models for each study area, our new method develops a single model that can automatically and efficiently recognize flower cover in various grassland ecosystems to successively estimate bee abundance and diversity. In addiction to this main objective, we also sought to determine which machine learning model would perform this important task best. The machine learning models used, particularly the Gradient Boost Machine (GBM), highlighted the capability of UAV RGB images combined with artificial intelligence to predict flower cover over time, which was highly correlated with bee abundance and diversity. This development represents an additional starting point for the use of machine learning and deep learning techniques in biodiversity studies within AES systems.

Název v anglickém jazyce

Machine learning for biodiversity: UAV-based flower detection as an indirect proxy for bee abundance

Popis výsledku anglicky

Pollination plays a crucial role in supporting agriculture and ecosystem functioning, making it an essential ecosystem service provided by bees and other insects. However, bee populations are increasingly threatened by habitat fragmentation, intensive agriculture, and climate change, among other threats. Improved monitoring of critical habitat factors, such as flower cover, is crucial to restore pollinator populations. Traditional approaches, such as field analyses, are often time-consuming and expensive, prompting the adoption of alternative methods to achieve greater efficiency and cost-effectiveness. Here, we introduce a novel approach that incorporates machine learning algorithms and optical images obtained from an unoccupied aerial vehicle (UAV). Using machine learning methods on RGB UAV imagery enabled us to estimate flower cover in UAV-monitored areas and make numerical inferences of wild bee pollinator abundance from those estimates. Unlike our previous study, which relied on separate machine learning models for each study area, our new method develops a single model that can automatically and efficiently recognize flower cover in various grassland ecosystems to successively estimate bee abundance and diversity. In addiction to this main objective, we also sought to determine which machine learning model would perform this important task best. The machine learning models used, particularly the Gradient Boost Machine (GBM), highlighted the capability of UAV RGB images combined with artificial intelligence to predict flower cover over time, which was highly correlated with bee abundance and diversity. This development represents an additional starting point for the use of machine learning and deep learning techniques in biodiversity studies within AES systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Ecological Informatics

ISSN

1574-9541

e-ISSN

1574-9541

Svazek periodika

91

Číslo periodika v rámci svazku

103346

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

001658211800001

EID výsledku v databázi Scopus

2-s2.0-105012874984