BR112021007682A8 - Detecção de doenças de planta por meio de aprendizado profundo em diversas etapas e diversas escalas - Google Patents
Detecção de doenças de planta por meio de aprendizado profundo em diversas etapas e diversas escalasInfo
- Publication number
- BR112021007682A8 BR112021007682A8 BR112021007682A BR112021007682A BR112021007682A8 BR 112021007682 A8 BR112021007682 A8 BR 112021007682A8 BR 112021007682 A BR112021007682 A BR 112021007682A BR 112021007682 A BR112021007682 A BR 112021007682A BR 112021007682 A8 BR112021007682 A8 BR 112021007682A8
- Authority
- BR
- Brazil
- Prior art keywords
- programmed
- plant diseases
- images
- deep learning
- class
- Prior art date
Links
Classifications
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01B—SOIL WORKING IN AGRICULTURE OR FORESTRY; PARTS, DETAILS, OR ACCESSORIES OF AGRICULTURAL MACHINES OR IMPLEMENTS, IN GENERAL
- A01B79/00—Methods for working soil
- A01B79/005—Precision agriculture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F18/2148—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24317—Piecewise classification, i.e. whereby each classification requires several discriminant rules
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- 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
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image
- G06T3/40—Scaling the whole image or part thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using 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
- G06V20/188—Vegetation
-
- 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
-
- 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
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G7/00—Botany in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Abstract
DETECÇÃO DE DOENÇAS DE PLANTA POR MEIO DE APRENDIZADO PROFUNDO EM DIVERSAS ETAPAS E DIVERSAS ESCALAS. Em algumas modalidades, o sistema é programado para construir a partir de diversos conjuntos de treinamento modelos digitais, cada um para identificar doenças de planta que apresentam sintomas de tamanhos similares. Cada modelo digital pode ser implementado com uma arquitetura de aprendizado profundo que classifica uma imagem em várias classes. Para cada conjunto de treinamentos, o sistema é assim programado para coletar imagens que mostram sintomas de uma ou mais doenças de planta de tamanhos similares. Essas imagens são então atribuídas a diversas classes. Para o primeiro dos conjuntos de treinamentos usados na construção do primeiro modelo digital, o sistema é programado para incluir também imagens que correspondem a uma condição saudável e imagens de sintomas que têm outros tamanhos. Essas imagens são então atribuídas a uma classe sem doença e uma classe genérica. Dada uma imagem nova proveniente de um dispositivo de usuário, o sistema é programado para primeiramente aplicar o primeiro modelo digital. Para as partes da imagem nova que são classificadas na classe genérica, o sistema é programado para aplicar outros modelos digitais. Por fim, o sistema é programado para transmitir dados de classificação para o dispositivo de usuário indicando como cada parte da imagem nova é classificada em uma classe que corresponde a uma ou nenhuma doença de planta.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201862750143P | 2018-10-24 | 2018-10-24 | |
US62/750,143 | 2018-10-24 | ||
PCT/US2019/057819 WO2020086818A2 (en) | 2018-10-24 | 2019-10-24 | Detection of plant diseases with multi-stage, multi-scale deep learning |
Publications (2)
Publication Number | Publication Date |
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BR112021007682A2 BR112021007682A2 (pt) | 2021-07-27 |
BR112021007682A8 true BR112021007682A8 (pt) | 2022-11-08 |
Family
ID=70327287
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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BR112021007682A BR112021007682A8 (pt) | 2018-10-24 | 2019-10-24 | Detecção de doenças de planta por meio de aprendizado profundo em diversas etapas e diversas escalas |
Country Status (9)
Country | Link |
---|---|
US (4) | US10713542B2 (pt) |
EP (1) | EP3871149A4 (pt) |
JP (1) | JP7357675B2 (pt) |
CN (1) | CN113228047A (pt) |
AR (1) | AR116848A1 (pt) |
AU (1) | AU2019364434A1 (pt) |
BR (1) | BR112021007682A8 (pt) |
CA (1) | CA3117337A1 (pt) |
WO (1) | WO2020086818A2 (pt) |
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EP3820268A4 (en) | 2018-07-11 | 2022-04-27 | Raven Industries, INC. | DETECTION OF A ROW ASSOCIATED WITH A CROP FROM AN IMAGE |
US11197417B2 (en) * | 2018-09-18 | 2021-12-14 | Deere & Company | Grain quality control system and method |
US11818982B2 (en) | 2018-09-18 | 2023-11-21 | Deere & Company | Grain quality control system and method |
US10713542B2 (en) * | 2018-10-24 | 2020-07-14 | The Climate Corporation | Detection of plant diseases with multi-stage, multi-scale deep learning |
US11120552B2 (en) * | 2019-02-27 | 2021-09-14 | International Business Machines Corporation | Crop grading via deep learning |
WO2021007554A1 (en) | 2019-07-11 | 2021-01-14 | Sneyders Yuri | Determining image feature height disparity |
CN111767849A (zh) * | 2020-06-29 | 2020-10-13 | 京东数字科技控股有限公司 | 农作物病虫害识别方法、设备及存储介质 |
CN112001370A (zh) * | 2020-09-29 | 2020-11-27 | 中国农业科学院农业信息研究所 | 一种农作物病虫害识别方法及系统 |
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US11783576B2 (en) * | 2020-10-29 | 2023-10-10 | Deere & Company | Method and system for optical yield measurement of a standing crop in a field |
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CN113989509B (zh) * | 2021-12-27 | 2022-03-04 | 衡水学院 | 基于图像识别的农作物虫害检测方法、检测系统及设备 |
CN114898327B (zh) * | 2022-03-15 | 2024-04-26 | 武汉理工大学 | 一种基于轻量化深度学习网络的车辆检测方法 |
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2019
- 2019-10-23 US US16/662,017 patent/US10713542B2/en active Active
- 2019-10-24 BR BR112021007682A patent/BR112021007682A8/pt unknown
- 2019-10-24 CN CN201980085791.3A patent/CN113228047A/zh active Pending
- 2019-10-24 AU AU2019364434A patent/AU2019364434A1/en active Pending
- 2019-10-24 AR ARP190103077A patent/AR116848A1/es active IP Right Grant
- 2019-10-24 WO PCT/US2019/057819 patent/WO2020086818A2/en unknown
- 2019-10-24 JP JP2021522397A patent/JP7357675B2/ja active Active
- 2019-10-24 CA CA3117337A patent/CA3117337A1/en active Pending
- 2019-10-24 EP EP19875386.5A patent/EP3871149A4/en active Pending
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2020
- 2020-07-14 US US16/928,857 patent/US11216702B2/en active Active
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2022
- 2022-01-03 US US17/567,635 patent/US11615276B2/en active Active
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2023
- 2023-03-27 US US18/190,358 patent/US11856881B2/en active Active
Also Published As
Publication number | Publication date |
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US20200134392A1 (en) | 2020-04-30 |
CN113228047A (zh) | 2021-08-06 |
US11856881B2 (en) | 2024-01-02 |
EP3871149A4 (en) | 2022-07-06 |
US11615276B2 (en) | 2023-03-28 |
BR112021007682A2 (pt) | 2021-07-27 |
US20220121887A1 (en) | 2022-04-21 |
AU2019364434A1 (en) | 2021-05-20 |
AR116848A1 (es) | 2021-06-16 |
WO2020086818A2 (en) | 2020-04-30 |
US20200342273A1 (en) | 2020-10-29 |
JP2022505742A (ja) | 2022-01-14 |
US11216702B2 (en) | 2022-01-04 |
US10713542B2 (en) | 2020-07-14 |
EP3871149A2 (en) | 2021-09-01 |
WO2020086818A3 (en) | 2020-07-30 |
JP7357675B2 (ja) | 2023-10-06 |
US20230225239A1 (en) | 2023-07-20 |
CA3117337A1 (en) | 2020-04-30 |
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