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Multi species weed detection with Retinanet one-step network in a maize field

TítuloMulti species weed detection with Retinanet one-step network in a maize field
Publication TypeConference Proceedings
Year of Conference2021
AuthorsCorrea JMLópez, Todeschini M, Pérez DS, Karouta J, Bromberg F, Ribeiro A, Andújar D
Conference NamePrecision agriculture’21
Pagination2223–2228
Date Published06/2021
PublisherWageningen Academic Publishers
Conference LocationBudapest, Hungary
ISBN Number978-90-8686-916-9
ISBN978-90-8686-363-1
Palabras clavedeep learning, object detection networks, RetinaNet, site-specific weed management
Abstract

Weed density and composition are not uniform throughout the field, nevertheless, the conventional approach is to carry out a uniform application. Object Detection Networks have already arrived in agricultural applications that can be used for weed management. The current study developed a detection and classification of weeds system in a one-step procedure using RetinaNet Object Detection Network. The procedure was based on identifying Solanum nigrum L., Cyperus rotundus L. and Echinochloa crus-galli L. and two growth stages both for a broadleaf species (S. nigrum) as well as narrow-leaved species (C. rotundus) in a maize field. The predictions were evaluated by mAP metric. The result obtained was 0.88 with values between 0.98 and 0.75 depending on the class.

URLhttps://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-916-9
DOI10.3920/978-90-8686-916-9
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