WO2024084296 - SYSTEMS AND METHODS FOR NON-CONTACT SOIL ORGANIC MATTER SENSING
National phase entry:
Publication Number
WO/2024/084296
Publication Date
25.04.2024
International Application No.
PCT/IB2023/056493
International Filing Date
23.06.2023
Title **
[English]
SYSTEMS AND METHODS FOR NON-CONTACT SOIL ORGANIC MATTER SENSING
[French]
SYSTÈMES ET PROCÉDÉS DE DÉTECTION DE MATIÈRE ORGANIQUE DE SOL SANS CONTACT
Applicants **
AGCO CORPORATION
Inventors
ZIMMERMAN, Jeffrey Michael
SUSKO, Alexander Quentin
GOEBEL, Darren
GODBOLE, Ravindra
Priority Data
63/380,480
21.10.2022
US
Application details
| Total Number of Claims/PCT | * |
| Number of Independent Claims | * |
| Number of Priorities | * |
| Number of Multi-Dependent Claims | * |
| Number of Drawings | * |
| Pages for Publication | * |
| Number of Pages with Drawings | * |
| Pages of Specification | * |
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| Number of Office Actions | * |
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International Searching Authority |
EPO
* |
| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
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| Type of Assignment |
The Standard Agent's Assignment
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| Applicant's Legal Status |
Legal Entity
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| * | |
| * | |
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| * | |
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| Entry into National Phase under |
Chapter I
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| Patent Delivery |
Send the Letters Patent by Courier
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| Translation |
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* The data is based on automatic recognition. Please verify and amend if necessary.
** IP-Coster compiles data from publicly available sources. If this data includes your personal information, you can contact us to request its removal.
Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 2214 | |
| EPO | Filing, Examination, Granting | 10851 | |
| Japan | Filing, Examination, Granting | 2325 | |
| South Korea | Filing, Examination, Granting | 2371 | |
| USA | Filing, Examination, Granting | 4740 |
Total:
22,501
The term for entry into the National Phase has expired. This quotation is for informational purposes only
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Abstract[English]
An image of soil is received from a camera and is associated with location data from a GPS sensor. The image is divided into sub-images, which are processed using a first ML model. The first ML model is trained to classify images into either soil or residue. The sub-images classified as soil are processed using a second ML model, which is trained to classify images into either high organic matter or low organic matter. The second ML model is trained on image data where a mean and standard deviation of RGB data of the image data provides an indication of an amount of organic matter in the imaged soil. An organic matter map is then generated.[French]
Une image du sol est reçue en provenance d'une caméra et est associée à des données de localisation provenant d'un capteur GPS. L'image est divisée en sous-images, qui sont traitées à l'aide d'un premier modèle ML. Le premier modèle ML est entraîné pour classifier des images comme étant un sol ou un résidu. Les sous-images classées en tant que sol sont traitées à l'aide d'un second modèle ML, qui est entraîné pour classifier des images en une matière organique élevée ou une matière organique faible. Le second modèle ML est entraîné sur des données d'image où un écart moyen et standard de données RVB des données d'image fournit une indication d'une quantité de matière organique dans le sol imagé. Une carte de matières organiques est ensuite générée.