公开(公告)号：US12073327B2

公开(公告)日：2024-08-27

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: G06T7/00 ,  G06N3/082 ,  G06T7/11 ,  G06V10/44 ,  G06V10/56 ,  G06V10/762 ,  G06V10/764 ,  G06V10/82 ,  G06V20/10

CPC classification number: G06N3/082 ,  G06T7/0012 ,  G06T7/11 ,  G06V10/454 ,  G06V10/56 ,  G06V10/762 ,  G06V10/764 ,  G06V10/82 ,  G06V20/10 ,  G06V20/188 ,  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.

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.