@proceedings {257, title = {Multi species weed detection with Retinanet one-step network in a maize field}, journal = {Precision agriculture{\textquoteright}21}, year = {2021}, month = {06/2021}, pages = {2223{\textendash}2228}, publisher = {Wageningen Academic Publishers}, address = {Budapest, Hungary}, 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.

}, keywords = {deep learning, object detection networks, RetinaNet, site-specific weed management}, isbn = {978-90-8686-916-9}, issn = {978-90-8686-363-1}, doi = {https://doi.org/10.3920/978-90-8686-916-9}, url = {https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-916-9}, author = {Correa, JM L{\'o}pez and Todeschini, M and P{\'e}rez, DS and Karouta, J and Bromberg, F and Ribeiro, A and And{\'u}jar, D} }