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AR117511A1 - Pronóstico del nivel de rendimiento de campos en estación - Google Patents

Pronóstico del nivel de rendimiento de campos en estación

Info

Publication number
AR117511A1
AR117511A1 ARP190103860A ARP190103860A AR117511A1 AR 117511 A1 AR117511 A1 AR 117511A1 AR P190103860 A ARP190103860 A AR P190103860A AR P190103860 A ARP190103860 A AR P190103860A AR 117511 A1 AR117511 A1 AR 117511A1
Authority
AR
Argentina
Prior art keywords
agricultural
field
spectral band
intensity values
processed
Prior art date
Application number
ARP190103860A
Other languages
English (en)
Inventor
Wei Guan
Gardar Johannesson
Yaqi Chen
Original Assignee
Climate Corp
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
Application filed by Climate Corp filed Critical Climate Corp
Publication of AR117511A1 publication Critical patent/AR117511A1/es

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/143—Sensing or illuminating at different wavelengths
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Software Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Development Economics (AREA)
  • Animal Husbandry (AREA)
  • Quality & Reliability (AREA)
  • Mining & Mineral Resources (AREA)
  • Operations Research (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Agronomy & Crop Science (AREA)
  • Primary Health Care (AREA)
  • Computational Linguistics (AREA)
  • Remote Sensing (AREA)
  • Astronomy & Astrophysics (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)

Abstract

En una realización, se reciben imágenes digitales de campos agrícolas en un sistema de procesamiento agrícola inteligente. Cada campo agrícola está representado por una o más imágenes digitales obtenidas durante la estación o en diferentes estaciones de plantación. Cada imagen digital de un campo agrícola incluye un grupo de píxeles que tienen valores de píxel, cada valor de píxel de un píxel incluye una pluralidad de valores de intensidad de banda espectral y cada valor de intensidad de banda espectral describe una intensidad de banda espectral de una banda de entre varias bandas de radiación electromagnética. Para cada uno de los campos agrícolas, se preprocesan los valores de intensidad de banda espectral de cada banda al nivel del campo, utilizando las imágenes digitales para ese campo agrícola, de lo que derivan resultados valores de intensidad preprocesados. Los valores de intensidad preprocesados se almacenan y los valores de intensidad de banda espectral preprocesados y almacenados para cada campo agrícola se proporcionan como datos de entrada para un modelo de aprendizaje automático capacitado. El modelo genera un valor de rendimiento previsto para cada campo. El valor de rendimiento previsto se usa para actualizar los mapas de rendimiento de los campos agrícolas para hacer pronósticos y puede mostrarse mediante una interfaz gráfica del usuario (IGU) de un dispositivo electrónico del cliente.
ARP190103860A 2018-12-21 2019-12-23 Pronóstico del nivel de rendimiento de campos en estación AR117511A1 (es)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US201862784252P 2018-12-21 2018-12-21

Publications (1)

Publication Number Publication Date
AR117511A1 true AR117511A1 (es) 2021-08-11

Family

ID=71098490

Family Applications (1)

Application Number Title Priority Date Filing Date
ARP190103860A AR117511A1 (es) 2018-12-21 2019-12-23 Pronóstico del nivel de rendimiento de campos en estación

Country Status (8)

Country Link
US (1) US11574465B2 (es)
EP (1) EP3899785A4 (es)
CN (1) CN113196287B (es)
AR (1) AR117511A1 (es)
AU (1) AU2019401506B2 (es)
BR (1) BR112021010122A2 (es)
WO (1) WO2020132674A1 (es)
ZA (1) ZA202105120B (es)

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Also Published As

Publication number Publication date
CN113196287A (zh) 2021-07-30
WO2020132674A1 (en) 2020-06-25
CA3120299A1 (en) 2020-06-25
BR112021010122A2 (pt) 2021-08-24
ZA202105120B (en) 2025-04-30
US20200202127A1 (en) 2020-06-25
US11574465B2 (en) 2023-02-07
AU2019401506A1 (en) 2021-07-15
EP3899785A1 (en) 2021-10-27
CN113196287B (zh) 2024-12-20
AU2019401506B2 (en) 2024-10-03
EP3899785A4 (en) 2022-08-10

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