Genemo, 2023 - Google Patents
Quantum Convolutional Neural Network for Agricultural Mechanization and Plant Disease DetectionGenemo, 2023
- Document ID
- 12870557795082259749
- Author
- Genemo M
- Publication year
- Publication venue
- International Conference on Image Processing and Capsule Networks
External Links
Snippet
Agricultural research is essential in addressing the challenges of food production and meeting the needs of a growing population. Severe crop disorders can lead to food insecurity, potentially causing disastrous effects on agricultural production, such as yield …
- 208000037265 diseases, disorders, signs and symptoms 0 title abstract description 89
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
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/6228—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
- G06K9/36—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
- G06K9/68—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
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- 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
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Kamath et al. | Classification of paddy crop and weeds using semantic segmentation | |
Fenu et al. | Using multioutput learning to diagnose plant disease and stress severity | |
Bhagat et al. | Bell pepper leaf disease classification with LBP and VGG-16 based fused features and RF classifier | |
Suma et al. | CNN based leaf disease identification and remedy recommendation system | |
Alguliyev et al. | Plant disease detection based on a deep model | |
Kunduracioglu et al. | Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases | |
Lamba et al. | Optimized classification model for plant diseases using generative adversarial networks | |
Rai et al. | Classification of diseased cotton leaves and plants using improved deep convolutional neural network | |
Ngugi et al. | Revolutionizing crop disease detection with computational deep learning: a comprehensive review | |
Nirmal et al. | Farmer Friendly Smart App for Pomegranate Disease Identification | |
Abisha et al. | Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network | |
Chikkamath et al. | Benchmarking of novel convolutional neural network models for automatic butterfly identification | |
Kumar et al. | A novel CNN gap layer for growth prediction of palm tree plantlings | |
Selvakumar et al. | Automated mango leaf infection classification using weighted and deep features with optimized recurrent neural network concept | |
Hessane et al. | Deep-PDSC: A Deep Learning-Based Model for a Stage-Wise Classification of Parlatoria Date Scale Disease | |
Prasad et al. | Leaf analysis based early plant disease detection using Internet of Things, Machine Learning and Deep Learning: A comprehensive review | |
Elakya et al. | A novel approach for early detection of disease and pest attack in food crop: A review | |
Nirmal et al. | Classification of pomegranate leaves diseases by image processing and machine learning techniques | |
Jadhav et al. | Comprehensive review on machine learning for plant disease identification and classification with image processing | |
Genemo | Quantum Convolutional Neural Network for Agricultural Mechanization and Plant Disease Detection | |
Eladl et al. | A proposed plant classification framework for smart agricultural applications using UAV images and artificial intelligence techniques | |
Kumar Apat et al. | IoT-assisted crop monitoring using machine learning algorithms for smart farming | |
Gulzar et al. | Classification and Analysis of Chilli Plant Disease Detection Using Convolution Neural Networks | |
Saini | Recent advancement of weed detection in crops using artificial intelligence and deep learning: A review | |
Vakula Rani et al. | Early Identification of Crop Disease Using Deep Convolution Neural Networks |