Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Domingues, T., Brandão, T., Ribeiro, R. & Ferreira, J. (2022). Insect detection in sticky trap images of tomato crops using machine learning. Agriculture. 12 (11)
Exportar Referência (IEEE) 
T. Domingues et al.,  "Insect detection in sticky trap images of tomato crops using machine learning", in Agriculture, vol. 12, no. 11, 2022
Exportar BibTeX 
@article{domingues2022_1714079989974,
	author = "Domingues, T. and Brandão, T. and Ribeiro, R. and Ferreira, J.",
	title = "Insect detection in sticky trap images of tomato crops using machine learning",
	journal = "Agriculture",
	year = "2022",
	volume = "12",
	number = "11",
	doi = "10.3390/agriculture12111967",
	url = "https://www.mdpi.com/journal/agriculture"
}
Exportar RIS 
TY  - JOUR
TI  - Insect detection in sticky trap images of tomato crops using machine learning
T2  - Agriculture
VL  - 12
IS  - 11
AU  - Domingues, T.
AU  - Brandão, T.
AU  - Ribeiro, R.
AU  - Ferreira, J.
PY  - 2022
SN  - 2077-0472
DO  - 10.3390/agriculture12111967
UR  - https://www.mdpi.com/journal/agriculture
AB  - As climate change, biodiversity loss, and biological invaders are all on the rise, the significance of conservation and pest management initiatives cannot be stressed. Insect traps are frequently
used in projects to discover and monitor insect populations, assign management and conservation
strategies, and assess the effectiveness of treatment. This paper assesses the application of YOLOv5
for detecting insects in yellow sticky traps using images collected from insect traps in Portuguese
tomato plantations, acquired under open field conditions. Furthermore, a sliding window approach
was used to minimize insect detection duplicates in a non-complex way. This article also contributes
to event forecasting in agriculture fields, such as diseases and pests outbreak, by obtaining insect related metrics that can be further analyzed and combined with other data extracted from the crop fields, contributing to smart farming and precision agriculture. The proposed method achieved good results when compared to related works, reaching 94.4% for mAP_0.5, with a precision and recall of 88% and 91%, respectively, using YOLOv5x.

ER  -