EP4081959A1 - Système d'aide à la décision agricole - Google Patents

Système d'aide à la décision agricole

Info

Publication number
EP4081959A1
EP4081959A1 EP20906140.7A EP20906140A EP4081959A1 EP 4081959 A1 EP4081959 A1 EP 4081959A1 EP 20906140 A EP20906140 A EP 20906140A EP 4081959 A1 EP4081959 A1 EP 4081959A1
Authority
EP
European Patent Office
Prior art keywords
agricultural
crop
data
analysis
development
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20906140.7A
Other languages
German (de)
English (en)
Other versions
EP4081959A4 (fr
Inventor
Sinan OZ
Yusuf KURUCU
Emre TUNALI
Osman BAYTAROGLU
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Agrovisio Oue
Original Assignee
Agrovisio Oue
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from TR2019/21818A external-priority patent/TR201921818A1/tr
Application filed by Agrovisio Oue filed Critical Agrovisio Oue
Publication of EP4081959A1 publication Critical patent/EP4081959A1/fr
Publication of EP4081959A4 publication Critical patent/EP4081959A4/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/194—Terrestrial scenes using hyperspectral data, i.e. more or other wavelengths than RGB
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/68—Food, e.g. fruit or vegetables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/10—Recognition assisted with metadata

Definitions

  • the invention relates to an agricultural decision support system that classifies agricultural products and monitors the phenological development of the products by analysing multispectral satellite images with the help of machine learning and remote sensing techniques.
  • Figure 1 shows the graph of the daily calorie consumption over the years. In order to adapt to this process, it becomes a necessity to produce effective and dynamic agricultural policies (support, incentives, etc.) and to make ground truth controls. For this, detailed, accurate and uninterrupted data production is required.
  • UHUZAM operating under ITU
  • UHUZAM is an application and research centre.
  • This centre within the scope of the Agricultural Monitoring and Information System (TARBiL) project, periodic monitoring of the cultivated areas with high spatial resolution satellite images during the phenological period, determination of product types and spatial distribution by object-based image classification, production of plant indexes for tracking product development, transferring these images, classification and plant index data produced and statistical result reports to the relevant units under the Ministry of Food, Agriculture and Livestock are executed..
  • the methods applied in the centre have not been converted into service, and the works carried out within the scope of the TARBIL project since 2008 have been brought to TRL 3 level, but beyond this level they have not been converted into ready-to-use models.
  • Precision agricultural practices are developed by analysing agricultural areas with images taken from satellite and air platforms within Tiibitak UZAY.
  • the products grown in the GAP region are analysed by using data from air platforms with multi band satellite images, including RASAT and Gokturk-2 images.
  • RASAT multi band satellite images
  • Gokturk-2 images As a result of the analyses, it is aimed to determine the crops sown in agricultural land, estimate the crop, the fertilizer and irrigation need of the soil, and the plant disease status.
  • product tracking is done with the NDVI technique and the reflection values are recorded through this technique.
  • the present invention relates to an agricultural decision support system that meets the above-mentioned requirements, eliminates all disadvantages, and brings some additional advantages.
  • the primary purpose of the invention is to contribute to the production of quality and efficient food by using the resources correctly and on time; to provide solutions that will secure economic and sustainable agricultural production.
  • the invention can be expressed as an agricultural decision support system that follows the phenological development of agricultural products by analysing multispectral satellite images with the help of machine learning and remote sensing techniques.
  • generalization of reflection models tracking of the reflection values of the plant using all wavelengths offered by satellites and product classification with spectral product signatures are the most basic steps.
  • using all bands (wavelengths) in satellite images will be a critical threshold for perceiving the soil in which the plant grows and thus determining the development level of the plant, as well as allowing a better understanding of the plant.
  • agricultural knowledge and machine learning areas of expertise are combined with an interdisciplinary study, so that large data analysis can be performed from field scale to national scale based on soil characteristics and real-time satellite images.
  • the obtained information can be segmented, and product identification can be made in parcel and sub-parcel detail, products can be separated according to their development level and efficiency, yield estimation can be made, and anomalies and damages in product development can be detected.
  • Analyses mentioned in the invention can be carried out using computer vision techniques with a resolution below the plot scale.
  • the accuracy of the cultivated area, yield estimation is increased.
  • the same technical ability also improves the performance in terms of the location of the damaged area estimated by the damage detection module and the clarity of the area.
  • the invention it is shown as the superiority of the accuracy and sensitivity of the plant indexes obtained based on the multispectral product signature over object- oriented indexes.
  • a multispectral product signature is created, and in all subsequent analyses, remote sensing techniques are enriched with machine learning techniques.
  • the invention is more precise and detailed than the systems in the prior art in terms of both product identification and analysis methods.
  • the feature calculation technique used in classification is a new method. This method examines the effects of reflections at different wavelengths that make up the satellite images in relation to each other throughout the phenological phase. In this way, the acquired features become more distinctive and generalizable than the indexes currently in use. In addition, the segmentation and normalization existing in our solution provides a spatial resolution that can be exceeded by parcel resolution.
  • the invention is based on the ideas of making decisions on current data using every new satellite image and automating agricultural processes. For this reason, the output formats are designed to be compatible with agricultural information systems in accordance with the general GIS formats and can be opened and examined in other commercial platforms (.shp, .dbf, .tif etc.). Since our solution does not require costly exploration methods such as infrastructure installation in production areas or using drones, it can be scaled quickly and used automatedly in large areas. In this way, it will be an infrastructure that can help decision makers to see the whole with the analysis of the big data produced.
  • the method used in the system subject to invention makes decisions based on up-to-date data by using each new satellite image and automates agricultural processes.
  • the stage of creating digital signatures of the product developments mentioned in the method includes creating a histogram from the pixel reflection values taken from each band of satellite images that coincide with the relevant parcels for the specified products, creating the probability density function (pdf) that gives the distribution probabilities of the pixel values for the relevant band (imaging wavelength) of the relevant satellite image of the product by normalizing the histogram with the total number of pixels, recording these probability density functions as temporal series by repeating in all bands (wavelengths) of each new satellite image throughout the phenological phase for different samples, creating multidimensional features on which plant growth is modelled, producing probability maps containing the expected reflection values of the specific product in a particular region throughout the year, as a result of this process repeated with each reference parcel obtained from the training data set, creating digital signatures of product developments by repeating this process for different products and different regions.
  • PDF probability density function
  • the product classification mentioned in the method is made by examining the effects of reflections of different wavelengths that form satellite images based on the matched features throughout the phenological phase in relation to each other.
  • the development monitoring mentioned in the method is made by using multispectral reflection values obtained from satellite images by using all bands of the satellite, converting the development values of the products into metrics, and evaluating whether the product follows the expected development process, and using supervised learning techniques.
  • the harvest estimation mentioned in the method is made by finding the damage presence on the planted areas on a parcel basis, determining the amount of damage on an area basis and to report the damage to the user by estimating in tonnage and using product development information and soil characteristics.
  • the invention makes decisions based on updated data using each new satellite image and automates agricultural processes.
  • the data mentioned in the method is stored for each agricultural parcel on a one-year or multi-year basis.
  • Another preferred embodiment of the invention is a desktop, mobile (Android/ iOS), or web application platform that allows the information platform mentioned in the method to provide inputs to the analysis modules in line with the user authorization and the opportunity to examine their outputs, accessing the outputs of all analysis modules, reporting and visual outputs to be given according to analysis types.
  • Spectral signature product catalogue which is one of the main outcomes of the invention and has not yet been produced in Turkey until today, however can be used in many areas of public and private organizations, is a strategic product for the needs.
  • each administrative unit is represented with a sub-administrative level unit (country with provinces, province with districts, district with villages, village with parcels) in the sample platform view.
  • Figure-8 demonstrates the village analysis result produced and visualized by combining sample parcel-based analyses (mosaic).
  • Figure-9 demonstrates the district analysis result produced and visualized by combining the sample parcel-based analysis (mosaic).
  • Figure- 10 demonstrates the presentation of the sample parcel-based analysis results as a series of images.
  • Figure- 11 demonstrates the sample parcel -based analysis and visual and textual information.
  • Figure- 12 demonstrates a sample analysis and classification result.
  • Figure- 13 demonstrates the sample reference parcels and management screen.
  • the data set is a factor that directly affects learning success. For this reason, there is a need for a high descriptive and large data set as possible. Selecting highly descriptive reference plots for products while creating this data set requires a significant amount of agricultural knowledge and experience. In order to meet this agricultural knowledge and experience, the data set has been shaped by field studies in line with the experiences in studies of our consultant professor and one of the inventors, who has been working on remote product detection for many years, Prof. Dr. Yusuf Kurucu, At the end of the study, a digital signature (reference catalogue) of the product developments at the level of sub-regions and phenological stage of selected field crops was created. Thus, in other words, a model of the usual development phase of the product is created by learning the expected multi-band reflection values during the phenological stages of the specified products.
  • a histogram is created from the pixel reflection values taken from each band of satellite images that coincide with the relevant parcels. This histogram is normalized with the total number of pixels and the probability density function (pdf) is created for the relevant band (imaging wavelength) of the relevant satellite image of the product. These probability density functions give the distribution probabilities of pixel values for the relevant band of that product. These probability density functions are repeated in all bands (wavelengths) of each new satellite image throughout the phenological phase for different samples and are recorded as temporal series. Thus, multidimensional features on which plant growth is modelled are formed.
  • Figure 5 shows a sample product development output. Analysis of Parcels Given as User Input:
  • Our solution which is the basis for this application, is an analysis and information platform. Users define the agricultural parcels they want to analyse and the analysis types they want to apply through this interface. Access to analysis results is also made through this interface. The data obtained from analysis modules are presented to the user in visual and textual reports through the same interface. The interface, which enables different types of analysis to be performed through different menus for different user groups, can also present the reports as visual and textual reports customized for the relevant user types.
  • the basis of our analysis is to examine the time-dependent changes of all bands of satellite images belonging to the analysed parcel and their correlations with each other.
  • This module has undertaken the task of including every new satellite image received in the analysis without delay, thus ensuring the automatic continuous tracking.
  • soil groups classified based on yield potential have been created by making similarity analysis in terms of basic soil properties taking into account the land slope characteristics, sea height, soil depth, salinity, alkalinity, drainage insufficiency, structure, soil lime content, characteristics, and by making query models in the Geographical Information System. These maps are used during yield and loss tonnage estimation.
  • this analysis module development deficiencies can be detected during the production process and reported to the relevant decision makers on a location basis.
  • the analysis outputs of this module which can be accessed by the notification platform, will be reported on the information platform in accordance with the relevant user profile.
  • Statement control analysis module The product in the output of the classification module in the relevant parcels is compared with the farmer's declaration obtained by various methods (farmer registration systems, third party database containing the product declaration for insurance/credit/premium, etc.). If the result of agreement with the declaration is negative, a warning is given to the operator via the information platform.
  • This platform which offers the user the opportunity to provide input to analysis modules in line with their authorizations and to examine their outputs, is designed to access the outputs of all analysis modules.
  • Visual outputs (figures and graphics) to be given according to analysis types with reporting can also be accessed via the user platform.
  • the information platform developed and prototyped as a desktop application, is also designed, and developed in accordance with mobile (Android / iOS) and web platforms.
  • the system is based on the ideas of making decisions based on up-to-date data and automating agricultural processes using each new satellite image. For this reason, the output formats are designed to be compatible with agricultural information systems in accordance with the common GIS formats and can be opened and examined in other commercial platforms (.shp, .dbf, .tiff etc.). Since our solution does not require costly exploration methods such as installing infrastructure in production areas or using drones, it is possible to work in large areas automatically by scaling quickly. In this way, an infrastructure that can provide big data and help decision makers to see the whole will be created.
  • the invention is a method that classifies agricultural products by analysing multispectral satellite images with the help of machine learning and remote sensing techniques in order to provide agricultural decision support to users systematically and follows the phenological development of the products and is characterised in that containing these operating steps:

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Multimedia (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Animal Husbandry (AREA)
  • Primary Health Care (AREA)
  • Mining & Mineral Resources (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Agronomy & Crop Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Game Theory and Decision Science (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un système d'aide à la décision agricole, qui classe des produits agricoles et surveille le développement phénologique des produits par analyse d'images satellites multispectrales à l'aide de techniques d'apprentissage automatique et de télédétection.
EP20906140.7A 2019-12-26 2020-12-02 Système d'aide à la décision agricole Pending EP4081959A4 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
TR2019/21818A TR201921818A1 (tr) 2018-12-27 2019-12-26 Tarimsal karar destek si̇stemi̇
PCT/TR2020/051210 WO2021133310A1 (fr) 2019-12-26 2020-12-02 Système d'aide à la décision agricole

Publications (2)

Publication Number Publication Date
EP4081959A1 true EP4081959A1 (fr) 2022-11-02
EP4081959A4 EP4081959A4 (fr) 2023-12-06

Family

ID=76574660

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20906140.7A Pending EP4081959A4 (fr) 2019-12-26 2020-12-02 Système d'aide à la décision agricole

Country Status (2)

Country Link
EP (1) EP4081959A4 (fr)
WO (1) WO2021133310A1 (fr)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11710186B2 (en) * 2020-04-24 2023-07-25 Allstate Insurance Company Determining geocoded region based rating systems for decisioning outputs
CN113723801A (zh) * 2021-08-27 2021-11-30 广州市城市规划勘测设计研究院 一种村庄类型划分方法、装置、设备及存储介质
CN113763396B (zh) * 2021-09-02 2023-07-25 中国农业科学院农业信息研究所 一种基于深度学习的遥感图像地块提取方法及系统
CN115984028B (zh) * 2023-03-21 2023-06-23 山东科翔智能科技有限公司 基于ai技术的智慧农业生产数据决策管理系统
CN117575164A (zh) * 2023-12-05 2024-02-20 北京金林伟业科技有限公司 一种林业有害生物信息化综合管理平台

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7184892B1 (en) * 2003-01-31 2007-02-27 Deere & Company Method and system of evaluating performance of a crop
US7702597B2 (en) 2004-04-20 2010-04-20 George Mason Intellectual Properties, Inc. Crop yield prediction using piecewise linear regression with a break point and weather and agricultural parameters
US20160224703A1 (en) * 2015-01-30 2016-08-04 AgriSight, Inc. Growth stage determination system and method
US10664702B2 (en) 2016-12-30 2020-05-26 International Business Machines Corporation Method and system for crop recognition and boundary delineation
US10586105B2 (en) * 2016-12-30 2020-03-10 International Business Machines Corporation Method and system for crop type identification using satellite observation and weather data
US10748081B2 (en) * 2017-05-12 2020-08-18 Harris Lee Cohen Computer-implemented methods, computer readable medium and systems for a precision agriculture platform that identifies generic anomalies in crops

Also Published As

Publication number Publication date
EP4081959A4 (fr) 2023-12-06
WO2021133310A1 (fr) 2021-07-01

Similar Documents

Publication Publication Date Title
US11657597B2 (en) Cloud detection on remote sensing imagery
US10872417B1 (en) Automatic delineation agricultural field management zones using remote sensing and field data
EP4081959A1 (fr) Système d'aide à la décision agricole
Meneguzzo et al. Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel-and object-based classification approaches
BR112019009999A2 (pt) identificação de zonas de gerenciamento em campos agrícolas e geração de planos de plantio para as zonas
Peters et al. Synergy of very high resolution optical and radar data for object-based olive grove mapping
AU2022410481A1 (en) Methods and systems for classifying and benchmarking irrigation performance
Korhonen et al. Detection of the need for seedling stand tending using high-resolution remote sensing data
Toosi et al. Citrus orchard mapping in Juybar, Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries
Faqe Ibrahim et al. Improving crop classification accuracy with integrated Sentinel-1 and Sentinel-2 data: a case study of barley and wheat
Meedeniya et al. Prediction of paddy cultivation using deep learning on land cover variation for sustainable agriculture
Tan et al. Mapping 30-m cotton areas based on an automatic sample selection and machine learning method using Landsat and MODIS images
Yuan et al. Rapidly count crop seedling emergence based on waveform Method (WM) using drone imagery at the early stage
Azzari et al. Understanding the requirements for surveys to support satellite-based crop type mapping
Boitt Hyperspectral remote sensing for cropland assessment and modeling for agro-ecological zones: A case study of Taita Hills, Kenya
Khemiri et al. Multi-temporal and multi-sensor approach for land use mapping: application to irrigated crops in the lower Mejerda Valley (Northeast Tunisia)
Dorbu Data-Driven Approach for Processing Remote Sensing Time-Series Imagery for Precision Agriculture
Liang et al. Object-Oriented Multi-Scale Segmentation and Multi-Feature Fusion-Based Method for Identifying Typical Fruit Trees in Arid Regions Using Sentinel-1/2 Satellite Images
Bakker Every Field in Minnesota: Building a Geographically Scalable Satellite Imagery Analytics System for Mapping Crop Fields
Raza et al. Improved method for cropland extraction of seasonal crops from multi-sensor satellite data
Sołtysiuk Vegetation classification of alien invasive species in rural and urban areas with the usage of remote sensing. Might change
Arnó Satorra et al. How to get and what to do with coloured maps
Yu et al. Crop Pattern and Status Monitoring
Li et al. Capitol Reef Ecological Conservation: Mapping Vegetation Functional Groups to Inform Invasive Vegetation Management, Ecological Conservation and Restoration in Capitol Reef National Park
Ngadi Remotely sensed detection and characterization of large-scale land acquisitions in Senegal

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20211230

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
A4 Supplementary search report drawn up and despatched

Effective date: 20231107

RIC1 Information provided on ipc code assigned before grant

Ipc: G06V 20/10 20220101ALI20231031BHEP

Ipc: G06V 10/26 20220101ALI20231031BHEP

Ipc: G06V 10/50 20220101ALI20231031BHEP

Ipc: G06N 3/02 20060101ALI20231031BHEP

Ipc: G06T 7/00 20170101ALI20231031BHEP

Ipc: G06N 20/00 20190101ALI20231031BHEP

Ipc: G06Q 50/02 20120101ALI20231031BHEP

Ipc: G06Q 10/06 20120101ALI20231031BHEP

Ipc: G06Q 10/00 20120101AFI20231031BHEP