公开(公告)号：US20230230373A1

公开(公告)日：2023-07-20

申请号：US17925250

申请日：2021-05-07

Applicant: BASF SE

Inventor： Artzai PICON RUIZ ,  Miguel GONZALEZ SAN EMETERIO ,  Aranzazu BERECIARTUA-PEREZ ,  Laura GOMEZ ZAMANILLO ,  Carlos Javier JIMENEZ RUIZ ,  Javier ROMERO RODRIGUEZ ,  Christian KLUKAS ,  Till EGGERS ,  Jone ECHAZARRA HUGUET ,  Ramon NAVARRA-MESTRE

IPC: G06V20/10 ,  G06T7/12 ,  G06V10/74 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06V20/188 ,  G06T7/12 ,  G06V10/761 ,  G06V10/764 ,  G06V10/82 ,  G06T2207/10024

Abstract: Computer-implemented method and system (100) for estimating vegetation coverage in a real-world environment. The system receives an RGB image (91) of a real-world scenery (1) with one or more plant elements (10) of one or more plant species. At least one channel of the RGB image (91) is provided to a semantic regression neural network (120) which is trained to estimate at least a near-infrared channel (NIR) from the RGB image. The system obtains an estimate of the near-infrared channel (NIR) by applying the semantic regression neural network (120) to the at least one RGB channel (91). A multi-channel image (92) comprising at least one of the R-, G-, B-channels (R, G, B) of the RGB image and the estimated near-infrared channel (NIR), is provided as test input (TI1) to a semantic segmentation neural network (130) trained with multi-channel images to segment the test input (TI1) into pixels associated with plant elements and pixels not associated with plant elements. The system segments the test input (TI1) using the semantic segmentation neural network (130) resulting in a vegetation coverage map (93) indicating pixels of the test input associated with plant elements (10) and indicating pixels of the test input not associated with plant elements.

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.