Wang et al., 2022 - Google Patents
TIA-YOLOv5: An improved YOLOv5 network for real-time detection of crop and weed in the fieldWang et al., 2022
View HTML- Document ID
- 8849990741625315400
- Author
- Wang A
- Peng T
- Cao H
- Xu Y
- Wei X
- Cui B
- Publication year
- Publication venue
- Frontiers in Plant Science
External Links
Snippet
Introduction Development of weed and crop detection algorithms provides theoretical support for weed control and becomes an effective tool for the site-specific weed management. For weed and crop object detection tasks in the field, there is often a large …
- 241000196324 Embryophyta 0 title abstract description 139
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
-
- 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
- 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
- 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
- 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
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- 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
- 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
- G06Q10/00—Administration; Management
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | TIA-YOLOv5: An improved YOLOv5 network for real-time detection of crop and weed in the field | |
Ahmad et al. | Deep learning based detector YOLOv5 for identifying insect pests | |
Zhang et al. | Applications of deep learning for dense scenes analysis in agriculture: A review | |
Albahar | A survey on deep learning and its impact on agriculture: Challenges and opportunities | |
Zhou et al. | Learning region-based attention network for traffic sign recognition | |
Zhang et al. | AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning | |
Zhou et al. | A wheat spike detection method based on Transformer | |
Yan et al. | Identification and picking point positioning of tender tea shoots based on MR3P-TS model | |
Teng et al. | MSR-RCNN: a multi-class crop pest detection network based on a multi-scale super-resolution feature enhancement module | |
Nong et al. | Semi-supervised learning for weed and crop segmentation using UAV imagery | |
Li et al. | Comparison of deep learning methods for detecting and counting sorghum heads in UAV imagery | |
Khaki et al. | High-throughput image-based plant stand count estimation using convolutional neural networks | |
Dong et al. | CRA-Net: A channel recalibration feature pyramid network for detecting small pests | |
Lu et al. | Citrus green fruit detection via improved feature network extraction | |
Lu et al. | Counting dense leaves under natural environments via an improved deep-learning-based object detection algorithm | |
Yu et al. | Wheat lodging extraction using Improved_Unet network | |
Xu et al. | ALAD-YOLO: an lightweight and accurate detector for apple leaf diseases | |
Su et al. | Technical solution discussion for key challenges of operational convolutional neural network-based building-damage assessment from satellite imagery: Perspective from benchmark xBD dataset | |
Lin et al. | Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning | |
Popescu et al. | New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review | |
Jiao et al. | Global context-aware-based deformable residual network module for precise pest recognition and detection | |
Wang et al. | Apple detection and instance segmentation in natural environments using an improved Mask Scoring R-CNN Model | |
Hu et al. | Crop node detection and internode length estimation using an improved YOLOv5 model | |
Jia et al. | An accurate green fruits detection method based on optimized YOLOX-m | |
Liu et al. | Tomato disease object detection method combining prior knowledge attention mechanism and multiscale features |