Sabrol et al., 2016 - Google Patents
Intensity based feature extraction for tomato plant disease recognition by classification using decision treeSabrol et al., 2016
View PDF- Document ID
- 13240055142045704469
- Author
- Sabrol H
- Kumar S
- Publication year
- Publication venue
- International Journal of Computer Science and Information Security
External Links
Snippet
The research work in the area of automation of plant disease recognition using computer vision is widely growing extensively. The study proposed and conducted in the paper is based on the idea to automate the plant disease recognition in the mean of digital image …
- 201000010099 disease 0 title abstract description 54
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
- G06K9/36—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
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- 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
- 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
- G06T7/00—Image analysis
-
- 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
-
- 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 |
---|---|---|
Sabrol et al. | Intensity based feature extraction for tomato plant disease recognition by classification using decision tree | |
Mia et al. | Mango leaf disease recognition using neural network and support vector machine | |
Almadhor et al. | AI-driven framework for recognition of guava plant diseases through machine learning from DSLR camera sensor based high resolution imagery | |
Azim et al. | An effective feature extraction method for rice leaf disease classification | |
Mehta et al. | Apple leaf disease recognition: a robust federated learning CNN methodology | |
Haider et al. | A generic approach for wheat disease classification and verification using expert opinion for knowledge-based decisions | |
Arivazhagan et al. | Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features | |
Aruraj et al. | Detection and classification of diseases of banana plant using local binary pattern and support vector machine | |
Tripathi et al. | Recent machine learning based approaches for disease detection and classification of agricultural products | |
Pujari et al. | Classification of fungal disease symptoms affected on cereals using color texture features | |
Long et al. | Classification of wheat diseases using deep learning networks with field and glasshouse images | |
Liu et al. | Plant species classification based on hyperspectral imaging via a lightweight convolutional neural network model | |
Sabrol et al. | Fuzzy and neural network based tomato plant disease classification using natural outdoor images | |
Shafik et al. | Using a novel convolutional neural network for plant pests detection and disease classification | |
Shete et al. | TasselGAN: An application of the generative adversarial model for creating field-based maize tassel data | |
Kumar et al. | An identification of crop disease using image segmentation | |
Sharma et al. | Improving rice disease diagnosis using ensemble transfer learning techniques | |
Ashok et al. | Pest Detection and Identification by Applying Color Histogram and Contour Detection by SVM Model | |
Wang et al. | A transformer-based mask R-CNN for tomato detection and segmentation | |
Patel et al. | A survey on plant leaf disease detection | |
Shukla et al. | A New Approach for Leaf Disease Detection using Multilayered Convolutional Neural Network | |
Zhang et al. | Unsound wheat kernel recognition based on deep convolutional neural network transfer learning and feature fusion | |
Leiva et al. | ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits | |
Sahu et al. | CNN based disease detection in apple leaf via transfer learning | |
Sahasranamam et al. | AI and Neural Network-Based Approach for Paddy Disease Identification and Classification |