公开(公告)号：US20220327815A1

公开(公告)日：2022-10-13

申请号：US17640742

申请日：2020-09-03

Applicant: BASF SE

Inventor： Artzai PICON RUIZ ,  Miguel LINARES DE LA PUERTA ,  Christian KLUKAS ,  Till EGGERS ,  Rainer OBERST ,  Juan Manuel CONTRERAS GALLARDO ,  Javier ROMERO RODRIGUEZ ,  Hikal Khairy Shohdy GAD ,  Gerd KRAEMER ,  Jone ECHAZARRA HUGUET ,  Ramon NAVARRA-MESTRE ,  Miguel GONZALEZ SAN EMETERIO

IPC: G06V10/82 ,  G06V20/10 ,  G06T7/00

Abstract: A computer-implemented method, computer program product and computer system (100) for identifying weeds in a crop field using a dual task convolutional neural network (120) having a topology with an intermediate module (121) to execute a classification task being associated with a first loss function (LF1), and with a semantic segmentation module (122) to execute a segmentation task with a second different loss function (LF2). The intermediate module and the segmentation module are being trained together, taking into account the first and second loss functions (LF1, LF2). The system executes a method including receiving a test input (91) comprising an image showing crop plants of a crop species in an agricultural field and showing weed plants of one or more weed species among said crop plants; predicting the presence of one or more weed species (11, 12, 13) which are present in the respective tile; outputting a corresponding intermediate feature map to the segmentation module as output of the classification task; generating a mask for each weed species class as segmentation output of the second task by extracting multiscale features and context information from the intermediate feature map and concatenating the extracted information to perform semantic segmentation; and generating a final image (92) indicating for each pixel if it belongs to a particular weed species, and if so, to which weed species it belongs.