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

Ninomiya, 2022 - Google Patents

High-throughput field crop phenotyping: current status and challenges

Ninomiya, 2022

View PDF
Document ID
16085310123333043435
Author
Ninomiya S
Publication year
Publication venue
Breeding Science

External Links

Snippet

In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The …
Continue reading at www.jstage.jst.go.jp (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6228Selecting the most significant subset of features
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor

Similar Documents

Publication Publication Date Title
Ninomiya High-throughput field crop phenotyping: current status and challenges
Yang et al. Applications of deep-learning approaches in horticultural research: a review
Zhou et al. A novel greenhouse-based system for the detection and plumpness assessment of strawberry using an improved deep learning technique
Font et al. Vineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at night
Zhang et al. Computer vision‐based tree trunk and branch identification and shaking points detection in Dense‐Foliage canopy for automated harvesting of apples
Joseph et al. Intelligent plant disease diagnosis using convolutional neural network: a review
Kumari et al. Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer
Sahu et al. A systematic literature review of machine learning techniques deployed in agriculture: A case study of banana crop
Mohimont et al. Computer vision and deep learning for precision viticulture
CN113657158B (en) Google EARTH ENGINE-based large-scale soybean planting area extraction algorithm
Paymode et al. Tomato leaf disease detection and classification using convolution neural network
Malathy et al. Disease detection in fruits using image processing
Pandey et al. Hyperspectral imaging combined with machine learning for the detection of fusiform rust disease incidence in loblolly pine seedlings
Kumar et al. Apple Sweetness Measurement and Fruit Disease Prediction Using Image Processing Techniques Based on Human‐Computer Interaction for Industry 4.0
Gupta et al. Fruit and vegetable disease detection and classification: Recent trends, challenges, and future opportunities
Brindha et al. Automatic detection of citrus fruit diseases using MIB classifier
Nirmal et al. Farmer Friendly Smart App for Pomegranate Disease Identification
Singhi et al. Integrated YOLOv4 deep learning pretrained model for accurate estimation of wheat rust disease severity
Selvanarayanan et al. Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques.
Mudgil et al. Identification of Tomato Plant Diseases Using CNN-A Comparative Review
Espinoza et al. Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions
Li et al. Review of deep learning-based methods for non-destructive evaluation of agricultural products
Safari et al. A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning
James et al. Citdet: A benchmark dataset for citrus fruit detection
Rocha IV et al. Philippine carabao mango pest identification using convolutional neural network