Título:	A lightweight and enhanced model for detecting the Neotropical brown stink bug, Euschistus heros (Hemiptera: Pentatomidae) based on YOLOv8 for soybean fields
Autor(es):	Lima, Bruno Pinheiro de Melo Borges, Lurdineide de Araújo Barbosa Hirose, Edson Borges, Díbio Leandro
ORCID:	https://orcid.org/0009-0000-8713-798X https://orcid.org/0000-0002-9301-0851 https://orcid.org/0000-0002-4868-0629
Afiliação do autor:	University of Brasilia, Department of Mechanical Engineering EMBRAPA Cerrados EMBRAPA Soybean University of Brasilia, Department of Mechanical Engineering University of Brasilia, Department of Computer Science
Assunto:	Aprendizado profundo Soja - plantio Análise de imagens Percevejo (Inseto)
Data de publicação:	27-fev-2024
Editora:	Elsevier
Referência:	LIMA, Bruno Pinheiro de Melo; BORGES, Lurdineide de Araújo Barbosa; HIROSE, Edson; BORGES, Díbio Leandro. A lightweight and enhanced model for detecting the Neotropical brown stink bug, Euschistus heros (Hemiptera: Pentatomidae) based on YOLOv8 for soybean fields. Ecological Informatics, [S.l.], v. 80, e102543, 2024. DOI: https://doi.org/10.1016/j.ecoinf.2024.102543. Disponível em: https://www.sciencedirect.com/science/article/pii/S1574954124000852?via%3Dihub. Acesso em: 12 fev. 2026.
Abstract:	Insect pest detection and monitoring are vital in an agricultural crop to help prevent losses and be more precise and sustainable regarding the consequent actions to be taken. Deep learning (DL) approaches have attracted attention, showing triumphant performance in many image-based applications. In the adult stage, this research considers detecting a vital insect pest in soybean crops, the Neotropical brown stink bug (Euschistus heros), from field images acquired by drones and cellphones. We develop and test an improved YOLO-model convolutional neural network (CNN) with fewer parameters than other state-of-the-art models and demonstrate its superior generalization and average precision on public image datasets and the new field data provided here. Considering the proposal's precision and time of response, the possibility of deploying this technology for automatic monitoring and pest management in the near future is promising. We provide open code and data for all the experiments performed.
Unidade Acadêmica:	Faculdade de Tecnologia (FT) Departamento de Engenharia Mecânica (FT ENM) Instituto de Ciências Exatas (IE) Departamento de Ciência da Computação (IE CIC)
Programa de pós-graduação:	Programa de Pós-Graduação em Sistemas Mecatrônicos Programa de Pós-Graduação em Informática
Licença:	This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/).
DOI:	https://doi.org/10.1016/j.ecoinf.2024.102543
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