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

LOPEZ CORREA, JUAN MANUEL; TODESCHINI, M.; PEREZ, DIEGO SEBASTIÁN; KAROUTA, J.; BROMBERG, FACUNDO; RIBEIRO; ANDUJAR

Budapest

Conferencia; European Conference on Precision Agriculture; 2021

Hungarian Society of Precision Agriculture

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.