[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Sunitha et al., 2022 - Google Patents

Modeling convolutional neural network for detection of plant leaf spot diseases

Sunitha et al., 2022

Document ID
1044902053478928392
Author
Sunitha G
Madhavi K
Avanija J
Reddy S
Vittal R
Publication year
Publication venue
2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC)

External Links

Snippet

Farming has come a long way further than directly a system to provide food to an ever- increasing population. The seventieth population of Asian countries depends on agriculture. The identification of plant leaf diseases is typically carried out with the inspection by masters …
Continue reading at ieeexplore.ieee.org (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
    • G06K9/4671—Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
    • 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
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • 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
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
    • 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
    • G06T2207/30004—Biomedical image processing
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • 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
    • 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
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • G06F17/30247—Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • 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

Similar Documents

Publication Publication Date Title
Paymode et al. Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG
Vishnoi et al. Plant disease detection using computational intelligence and image processing
Haridasan et al. Deep learning system for paddy plant disease detection and classification
Zhao et al. An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild
Joseph et al. Intelligent plant disease diagnosis using convolutional neural network: a review
Mishra et al. Weed density estimation in soya bean crop using deep convolutional neural networks in smart agriculture
Rathore et al. Automatic rice plant disease recognition and identification using convolutional neural network
Kumar K et al. Detection of rice plant disease using AdaBoostSVM classifier
Archana et al. A novel method to improve computational and classification performance of rice plant disease identification
Sunitha et al. Modeling convolutional neural network for detection of plant leaf spot diseases
Kumar et al. An identification of crop disease using image segmentation
Abisha et al. Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network
Nirmal et al. Farmer Friendly Smart App for Pomegranate Disease Identification
Paçal et al. Data-efficient vision transformer models for robust classification of sugarcane
Mishra et al. A robust pest identification system using morphological analysis in neural networks
Nagila et al. The effectiveness of machine learning and image processing in detecting plant leaf disease
Ahmed et al. An automated system for early identification of diseases in plant through machine learning
Pawar et al. Farmer Buddy-Plant Leaf Disease Detection on Android Phone
Saxena et al. Disease detection in plant leaves using deep learning models: AlexNet and GoogLeNet
Kaur et al. A novel framework for semi-automated system for grape leaf disease detection
Patel et al. A review on rice plant phenotyping traits estimation for disease and growth management using modern ML techniques
Baljon A Framework for Agriculture Plant Disease Prediction using Deep Learning Classifier
Biradar et al. Segmentation and detection of crop pests using novel U‐Net with hybrid deep learning mechanism
Kathan et al. Precision Agriculture-Classification Weed From Paddy Using Improved CNN
Kumar et al. Image Based Plant Disease Classification Using Deep Learning Technique