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

Ludovico Chieffallo, Michele Torresani, Piero Zannini, Jan Peter Reinier de Vries, Marharyta Blaha, Alessio Monacchia, David Kleijn, Duccio Rocchini,
Machine learning for biodiversity: UAV-based flower detection as an indirect proxy for bee abundance,
Ecological Informatics,
Volume 91,
2025,
103346,
ISSN 1574-9541,
https://doi.org/10.1016/j.ecoinf.2025.103346.
(https://www.sciencedirect.com/science/article/pii/S1574954125003553)
Abstract: 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.
Keywords: Machine learning; Applied ecology; Bees; Remote sensing; UAV