[go: up one dir, main page]

Khaire et al., 2023 - Google Patents

A Comprehensive Survey of Weed Detection and Classification Datasets for Precision Agriculture

Khaire et al., 2023

Document ID
6204347347182940488
Author
Khaire P
Attar V
Kalamkar S
Publication year
Publication venue
2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

External Links

Snippet

The plant that grows along with valuable agricultural goods is called a weed. This weed inhibits the crop's growth and diminishes farm productivity, so the weeds should be detected and removed. Weed detection and control play a vital role in agriculture, as weeds reduce …
Continue reading at ieeexplore.ieee.org (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
    • G06K9/00624Recognising 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
    • 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
    • G06K9/4671Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
    • 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
    • G06K9/62Methods 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
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/00664Recognising scenes such as could be captured by a camera operated by a pedestrian or robot, including objects at substantially different ranges from the camera
    • 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
    • G06K9/6201Matching; Proximity measures
    • G06K9/6202Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • 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
    • G06K9/20Image acquisition
    • G06K9/32Aligning or centering of the image pick-up or image-field
    • 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
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • 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
    • G06T2207/30Subject of image; Context of image processing
    • 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
    • G06F17/30244Information retrieval; Database structures therefor; File system structures therefor in image databases
    • G06F17/30247Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data

Similar Documents

Publication Publication Date Title
Cecotti et al. Grape detection with convolutional neural networks
Parvathi et al. Detection of maturity stages of coconuts in complex background using Faster R-CNN model
Jin et al. Weed identification using deep learning and image processing in vegetable plantation
Bah et al. Deep learning based classification system for identifying weeds using high-resolution UAV imagery
Milioto et al. Real-time blob-wise sugar beets vs weeds classification for monitoring fields using convolutional neural networks
Chen et al. Counting apples and oranges with deep learning: A data-driven approach
Qureshi et al. Machine vision for counting fruit on mango tree canopies
Kurtulmuş et al. Detecting corn tassels using computer vision and support vector machines
Farjon et al. Deep-learning-based counting methods, datasets, and applications in agriculture: A review
Blok et al. The effect of data augmentation and network simplification on the image‐based detection of broccoli heads with Mask R‐CNN
Pathak et al. A review of unmanned aerial vehicle-based methods for plant stand count evaluation in row crops
Hsieh et al. Fruit maturity and location identification of beef tomato using R-CNN and binocular imaging technology
Weyler et al. Phenobench: A large dataset and benchmarks for semantic image interpretation in the agricultural domain
Liang et al. Low-cost weed identification system using drones
Li et al. A novel approach for the 3D localization of branch picking points based on deep learning applied to longan harvesting UAVs
Weis et al. Detection and identification of weeds
Khaire et al. A Comprehensive Survey of Weed Detection and Classification Datasets for Precision Agriculture
Valente et al. Fast classification of large germinated fields via high-resolution UAV imagery
Liang et al. A rotated rice spike detection model and a crop yield estimation application based on UAV images
Kadethankar et al. Deep learning based detection of rhinoceros beetle infestation in coconut trees using drone imagery
CN115861686A (en) Litchi key growth period identification and detection method and system based on edge deep learning
Ashok Kumar et al. A review on crop and weed segmentation based on digital images
Dadashzadeh et al. A stereoscopic video computer vision system for weed discrimination in rice field under both natural and controlled light conditions by machine learning
Choudhury Segmentation techniques and challenges in plant phenotyping
Subramani et al. Citrus leaf disease detection using various deep learning architectures