[go: up one dir, main page]

Li et al., 2022 - Google Patents

Maize leaf disease identification based on WG-MARNet

Li et al., 2022

View HTML
Document ID
12265882486422219795
Author
Li Z
Zhou G
Hu Y
Chen A
Lu C
He M
Hu Y
Wang Y
Publication year
Publication venue
Plos one

External Links

Snippet

In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can solve the problems of …
Continue reading at journals.plos.org (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/007—Dynamic range modification
    • G06T5/008—Local, e.g. shadow enhancement
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Similar Documents

Publication Publication Date Title
Singh et al. Deep transfer modeling for classification of maize plant leaf disease
Li et al. Maize leaf disease identification based on WG-MARNet
Nawaz et al. A robust deep learning approach for tomato plant leaf disease localization and classification
Xiong et al. Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset
Pandi et al. Rice plant disease classification using dilated convolutional neural network with global average pooling
Yu et al. A recognition method of soybean leaf diseases based on an improved deep learning model
Zhu et al. Grape leaf black rot detection based on super-resolution image enhancement and deep learning
Iftikhar et al. Plant disease management: a fine-tuned enhanced CNN approach with mobile app integration for early detection and classification
Kaur et al. Performance analysis of segmentation models to detect leaf diseases in tomato plant
Parez et al. Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection.
Suo et al. Casm-amfmnet: A network based on coordinate attention shuffle mechanism and asymmetric multi-scale fusion module for classification of grape leaf diseases
Abisha et al. Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network
Yang et al. Identification of citrus diseases based on AMSR and MF-RANet
Niu et al. Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision
C. R et al. Integration of dilated convolution with residual dense block network and multi‐level feature detection network for cassava plant leaf disease identification
Shenbagam et al. Corn leaf disease detection; a survey
Agarwal et al. Plant leaf disease classification using deep learning: A survey
Sreedevi et al. Development of weighted ensemble transfer learning for tomato leaf disease classification solving low resolution problems
Yu et al. Rapid and non-destructive classification of rice seeds with different flavors: An approach based on HPFasterNet
Ke et al. Intelligent vineyard blade density measurement method incorporating a lightweight vision transformer
Qin et al. Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring
Wang et al. TomaFDNet: A multiscale focused diffusion-based model for tomato disease detection
Bi et al. Apple leaf disease severity grading based on deep learning and the DRL-Watershed algorithm
Subbarayudu et al. An automated hybrid deep learning framework for paddy leaf disease identification and classification
Kaur et al. Leaf disease detection and classification: A comprehensive survey