Multi species weed detection with Retinanet one-step network in a maize field
| Title | Multi species weed detection with Retinanet one-step network in a maize field |
| Publication Type | Conference Proceedings |
| Year of Conference | 2021 |
| Authors | Correa JMLópez, Todeschini M, Pérez DS, Karouta J, Bromberg F, Ribeiro A, Andújar D |
| Conference Name | Precision agriculture’21 |
| Pagination | 2223–2228 |
| Date Published | 06/2021 |
| Publisher | Wageningen Academic Publishers |
| Conference Location | Budapest, Hungary |
| ISBN Number | 978-90-8686-916-9 |
| ISBN | 978-90-8686-363-1 |
| Keywords | deep 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. |
| URL | https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-916-9 |
| DOI | 10.3920/978-90-8686-916-9 |
