公开(公告)号：US20230071265A1

公开(公告)日：2023-03-09

申请号：US17799829

申请日：2021-02-19

Applicant: BASF SE

Inventor： Aranzazu BERECIARTUA-PEREZ ,  Artzai PICON RUIZ ,  Aitor ALVAREZ GILA ,  Jone ECHAZARRA HUGUET ,  Till EGGERS ,  Christian KLUKAS ,  Ramon NAVARRA-MESTRE ,  Laura GOMEZ ZAMANILLO

IPC: G06T7/00 ,  G06V10/44 ,  G06V10/56 ,  G06V10/82 ,  G06V20/10 ,  G06V10/762 ,  G06T7/11

Abstract: A computer generates a training set with annotated images (473) to train a convolutional neural network (CNN). The computer receives leaf-images showing leaves and biological objects such as insects, in a first color-coding (413-A), changes the color-coding of the pixels to a second color-coding and thereby enhances the contrast (413-C), assigns pixels in the second color-coding to binary values (413-D), differentiates areas with contiguous pixels in the first binary value into non-insect areas and insect areas by an area size criterion (413-E), identifies pixel-coordinates of the insect areas with rectangular tile-areas (413-F), and annotates the leaf-images in the first color-coding by assigning the pixel-coordinates to corresponding tile-areas. The annotated image is then used to train the CNN for quantifying plant infestation by estimating the number of biological object such as insects on the leaves of plants.

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.