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：US20230351743A1

公开(公告)日：2023-11-02

申请号：US17761849

申请日：2020-09-29

Applicant: BASF SE

Inventor： Aitor ALVAREZ GILA ,  Amaia Maria Ortiz Barredo ,  David Roldan Lopez ,  Javier Romero Rodriguez ,  Corinna Maria Spangler ,  Christian Klukas ,  Till Eggers ,  Jone Echazarra Huguet ,  Ramon Navarra Mestre ,  Artzai Picon Ruiz ,  Aranzazu Bereciartua Perez

IPC: G06V20/10 ,  G06V10/44 ,  G06V10/764 ,  G06T7/00 ,  G06T7/11

CPC classification number: G06V20/188 ,  G06V10/454 ,  G06V10/764 ,  G06T7/0012 ,  G06T7/11 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Quantifying plant infestation is performed by estimating the number of biological objects (132) on parts (122) of a plant (112). A computer (202) receives a plant-image (412) taken from a particular plant (112). The computer (202) uses a first convolutional neural network (262/272) to derive a part-image (422) that shows a part of the plant. The computer (202) splits the part-image into tiles and uses a second network to process the tiles to density maps. The computer (202) combines the density maps to a combined density map in the dimension of the part-image and integrates the pixel values to an estimate number of objects for the part. Object classes (132(1), 132(2)) can be differentiated to fine-tune the quantification to identify class-specific countermeasures.