Invention Grant
- Patent Title: Quantifying objects on plants by estimating the number of objects on plant parts such as leaves, by convolutional neural networks that provide density maps
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Application No.: US17761849Application Date: 2020-09-29
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Publication No.: US12073327B2Publication Date: 2024-08-27
- 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
- Applicant: BASF SE
- Applicant Address: DE Ludwigshafen am Rhein
- Assignee: BASF SE
- Current Assignee: BASF SE
- Current Assignee Address: DE Ludwigshafen am Rhein
- Agency: Lowenstein Sandler LLP
- Priority: EP 200657 2019.09.30
- International Application: PCT/EP2020/077197 2020.09.29
- International Announcement: WO2021/063929A 2021.04.08
- Date entered country: 2022-03-18
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06N3/082 ; G06T7/11 ; G06V10/44 ; G06V10/56 ; G06V10/762 ; G06V10/764 ; G06V10/82 ; G06V20/10

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.
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