Wang et al., 2022 - Google Patents
MGA-YOLO: A lightweight one-stage network for apple leaf disease detectionWang et al., 2022
View HTML- Document ID
- 13205772518283563711
- Author
- Wang Y
- Wang Y
- Zhao J
- Publication year
- Publication venue
- Frontiers in Plant Science
External Links
Snippet
Apple leaf diseases seriously damage the yield and quality of apples. Current apple leaf disease diagnosis methods primarily rely on human visual inspection, which often results in low efficiency and insufficient accuracy. Many computer vision algorithms have been …
- 201000010099 disease 0 title abstract description 77
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
- G06K9/36—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
- G06K9/6267—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
-
- 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
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
-
- 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
-
- 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
-
- 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
- G06Q10/00—Administration; Management
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- 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 |
---|---|---|
Wang et al. | MGA-YOLO: A lightweight one-stage network for apple leaf disease detection | |
Ahmad et al. | Deep learning based detector YOLOv5 for identifying insect pests | |
Naranjo-Torres et al. | A review of convolutional neural network applied to fruit image processing | |
Lei et al. | Underwater target detection algorithm based on improved YOLOv5 | |
Mukhiddinov et al. | Improved classification approach for fruits and vegetables freshness based on deep learning | |
Ge et al. | Tracking and counting of tomato at different growth period using an improving YOLO-deepsort network for inspection robot | |
Nawaz et al. | AI-based object detection latest trends in remote sensing, multimedia and agriculture applications | |
Luo et al. | YOLOD: A target detection method for UAV aerial imagery | |
Zhang et al. | AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning | |
Zhang et al. | Lightweight fruit-detection algorithm for edge computing applications | |
Zhang et al. | Deep learning based automatic grape downy mildew detection | |
Wang et al. | Study on pear flowers detection performance of YOLO-PEFL model trained with synthetic target images | |
Sun et al. | YOLO-P: An efficient method for pear fast detection in complex orchard picking environment | |
Zhang et al. | A marine organism detection framework based on the joint optimization of image enhancement and object detection | |
Liu et al. | A domestic trash detection model based on improved YOLOX | |
Wang et al. | Segment-tube: Spatio-temporal action localization in untrimmed videos with per-frame segmentation | |
Xu et al. | ALAD-YOLO: an lightweight and accurate detector for apple leaf diseases | |
Zhao et al. | Precision detection of crop diseases based on improved YOLOv5 model | |
Zhang et al. | Ts-yolo: an all-day and lightweight tea canopy shoots detection model | |
Yang et al. | Real-time classification of invasive plant seeds based on improved YOLOv5 with attention mechanism | |
Hu et al. | Fruit detection and counting in apple orchards based on improved Yolov7 and multi-Object tracking methods | |
Wang et al. | Fine-grained grape leaf diseases recognition method based on improved lightweight attention network | |
Lv et al. | YOLOV5-CBAM-C3TR: an optimized model based on transformer module and attention mechanism for apple leaf disease detection | |
Xu et al. | A neural network structure with attention mechanism and additional feature fusion layer for tomato flowering phase detection in pollination robots | |
Chen et al. | TeaViTNet: Tea Disease and Pest Detection Model Based on Fused Multiscale Attention |