Kalim et al., 2022 - Google Patents
Citrus leaf disease detection using hybrid cnn-rf modelKalim et al., 2022
- Document ID
- 17477422747518597161
- Author
- Kalim H
- Chug A
- Singh A
- Publication year
- Publication venue
- 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST)
External Links
Snippet
One of the nutrient-dense foods with anti-oxidant and anti-mutagenic are citrus fruits. The protection of citrus fruit and leaves against infectious diseases is very essential. Diseases of citrus fruits are the primary factor contributing to the drastic reduction in citrus fruit yields …
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
- G06K9/6217—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
- G06K9/36—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
- G06K9/6256—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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
- G06K9/6284—Single class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- 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
- 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
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- 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
- G06N3/08—Learning methods
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kukreja et al. | A Deep Neural Network based disease detection scheme for Citrus fruits | |
Sambasivam et al. | A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks | |
Manavalan | Automatic identification of diseases in grains crops through computational approaches: A review | |
Raikar et al. | Classification and Grading of Okra-ladies finger using Deep Learning | |
Kalim et al. | Citrus leaf disease detection using hybrid cnn-rf model | |
Hao et al. | Growing period classification of Gynura bicolor DC using GL-CNN | |
Kunduracioglu et al. | Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases | |
Bhatti et al. | Deep Learning-Based Trees Disease Recognition and Classification Using Hyperspectral Data. | |
Aherwadi et al. | Fruit quality identification using image processing, machine learning, and deep learning: A review | |
Aldhyani et al. | Leaf pathology detection in potato and pepper bell plant using convolutional neural networks | |
Asriny et al. | Transfer learning VGG16 for classification orange fruit images | |
Gopi et al. | Transfer learning for rice leaf disease detection | |
Feng et al. | MSDD-YOLOX: An enhanced YOLOX for real-time surface defect detection of oranges by type | |
Kulkarni et al. | Fruit freshness detection using CNN | |
Nirmal et al. | Farmer Friendly Smart App for Pomegranate Disease Identification | |
Islam et al. | Nitrogen fertilizer recommendation for paddies through automating the leaf color chart (LCC) | |
Lwin et al. | Image Classification for Rice Leaf Disease Using AlexNet Model | |
Salem et al. | Impact of transfer learning compared to convolutional neural networks on fruit detection | |
Nourish et al. | A Study of Deep Learning based Techniques for the Detection of Maize Leaf Disease: A Short Review | |
Das et al. | An Automated Tomato Maturity Grading System Using Transfer Learning Based AlexNet. | |
Kunduracıoğlu et al. | Deep Learning-Based Disease Detection in Sugarcane Leaves: Evaluating EfficientNet Models | |
Malar et al. | Deep learning based disease detection in tomatoes | |
Shakil et al. | Addressing agricultural challenges: An identification of best feature selection technique for dragon fruit disease recognition | |
Mir et al. | Enhanced Multiclassification of Avocado Leaf Diseases: CNN and Random Forest Integration | |
Ghazalli et al. | Short Review on Palm Oil Fresh Fruit Bunches Ripeness and Classification Technique |