Razavi et al., 2024 - Google Patents
ResNet deep models and transfer learning technique for classification and quality detection of rice cultivarsRazavi et al., 2024
- Document ID
- 1713571391499644977
- Author
- Razavi M
- Mavaddati S
- Koohi H
- Publication year
- Publication venue
- Expert Systems with Applications
External Links
Snippet
Rice classification and quality detection are therefore crucial for ensuring the safety and quality of rice for human consumption and reducing the financial losses associated with rice spoilage. Accurate and efficient rice classification and quality detection techniques can help …
- 235000007164 Oryza sativa 0 title abstract description 268
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
- G06K9/6267—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
- G06K9/62—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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6228—Selecting the most significant subset of features
-
- 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
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- 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
- 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
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Chen et al. | Using deep transfer learning for image-based plant disease identification | |
Koklu et al. | Multiclass classification of dry beans using computer vision and machine learning techniques | |
Barbedo | Plant disease identification from individual lesions and spots using deep learning | |
Li et al. | Apple quality identification and classification by image processing based on convolutional neural networks | |
Bhargava et al. | Automatic detection and grading of multiple fruits by machine learning | |
Yu et al. | Deep-learning-based regression model and hyperspectral imaging for rapid detection of nitrogen concentration in oilseed rape (Brassica napus L.) leaf | |
Zhang et al. | Deep indicator for fine-grained classification of banana’s ripening stages | |
Talukder et al. | Nutrients deficiency diagnosis of rice crop by weighted average ensemble learning | |
Razavi et al. | ResNet deep models and transfer learning technique for classification and quality detection of rice cultivars | |
Kumari et al. | Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer | |
CN103278464A (en) | Method and device for fish flesh detection | |
Nadimi et al. | Automated detection of mechanical damage in flaxseeds using radiographic imaging and machine learning | |
Yao et al. | L2MXception: an improved Xception network for classification of peach diseases | |
CN108197636A (en) | A kind of paddy detection and sorting technique based on depth multiple views feature | |
Sharma et al. | Image processing techniques to estimate weight and morphological parameters for selected wheat refractions | |
Erbaş et al. | Classification of hazelnuts according to their quality using deep learning algorithms. | |
Lanjewar et al. | Modified transfer learning frameworks to identify potato leaf diseases | |
Du et al. | A method for detecting the quality of cotton seeds based on an improved ResNet50 model | |
Hidayat et al. | Determining the Rice Seeds Quality Using Convolutional Neural Network | |
Balasubramaniyan et al. | Color contour texture based peanut classification using deep spread spectral features classification model for assortment identification | |
Yang et al. | Detection of starch in minced chicken meat based on hyperspectral imaging technique and transfer learning | |
Li et al. | DBANet: Dual-branch Attention Network for hyperspectral remote sensing image classification | |
Ali et al. | An efficient quality inspection of food products using neural network classification | |
Guo et al. | A multivariate algorithm for identifying contaminated peanut using visible and near-infrared hyperspectral imaging | |
McDonald et al. | Images, features, or feature distributions? A comparison of inputs for training convolutional neural networks to classify lentil and field pea milling fractions |