| Full text | |
| Author(s): |
Bernardes, Rodrigo Cupertino
[1]
;
Lima, Maria Augusta Pereira
[2]
;
Guedes, Raul Narciso Carvalho
[1]
;
da Silva, Clissia Barboza
[3]
;
Martins, Gustavo Ferreira
[4]
Total Authors: 5
|
| Affiliation: | [1] Univ Fed Vicosa, Dept Entomol, BR-36570900 Vicosa, MG - Brazil
[2] Univ Fed Vicosa, Dept Anim Biol, BR-36570900 Vicosa, MG - Brazil
[3] Univ Sao Paulo, Lab Radiobiol & Environm, Ctr Nucl Energy Agr, 303 Centenario Ave, BR-13416000 Piracicaba, SP - Brazil
[4] Univ Fed Vicosa, Dept Gen Biol, BR-36570900 Vicosa, MG - Brazil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | SENSORS; v. 21, n. 9 MAY 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses' possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields. (AU) | |
| FAPESP's process: | 17/15220-7 - Non-destructive image analysis methods for seed quality evaluation |
| Grantee: | Clíssia Barboza Mastrangelo |
| Support Opportunities: | Research Grants - Young Investigators Grants |