| Title: |
Bee together: Joining bee audio datasets for hive extrapolation in AI-based monitoring |
| Authors: |
Bricout, Augustin; Leleux, Philippe; Acco, Pascal; Escriba, Christophe; Fourniols, Jean-Yves; Soto-Romero, Georges; Floquet, Rémi |
| Contributors: |
Équipe Instrumentation embarquée et systèmes de surveillance intelligents (LAAS-S4M); Laboratoire d'analyse et d'architecture des systèmes (LAAS); Université Toulouse Capitole (UT Capitole); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse); Institut National des Sciences Appliquées (INSA)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Institut National des Sciences Appliquées (INSA)-Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse - Jean Jaurès (UT2J); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse III - Paul Sabatier (UT3); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP); Communauté d'universités et établissements de Toulouse (Comue de Toulouse)-Université Toulouse Capitole (UT Capitole); Communauté d'universités et établissements de Toulouse (Comue de Toulouse); Trustworthy systems: foundations and practices (LAAS-TRUST); RF Innovation, Toulouse, France; This research was funded by RF Innovation (Toulouse, France), which manages the CIFRE 2019/1498 Ph.D. grant |
| Source: |
ISSN: 1424-8220 ; Sensors ; https://hal.science/hal-04800976 ; Sensors, 2024, 24 (18), pp.6067. ⟨10.3390/s24186067⟩. |
| Publisher Information: |
CCSD; MDPI |
| Publication Year: |
2024 |
| Collection: |
Université Toulouse III - Paul Sabatier: HAL-UPS |
| Subject Terms: |
bee acoustics; machine learning; classification; generalization; contrastive learning; queen presence detection; beehive monitoring; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Description: |
International audience ; Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the "BeeTogether" dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives' extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony's belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.3390/s24186067 |
| Availability: |
https://hal.science/hal-04800976; https://hal.science/hal-04800976v1/document; https://hal.science/hal-04800976v1/file/sensors-24-06067.pdf; https://doi.org/10.3390/s24186067 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.E84226D6 |
| Database: |
BASE |