Bravo-Reyna et al., 2020 - Google Patents
Recognition of the damage caused by the cogollero worm to the corn plant, Using artificial visionBravo-Reyna et al., 2020
View PDF- Document ID
- 9177325191244177395
- Author
- Bravo-Reyna J
- Montero-Valverde J
- Martínez-Arroyo M
- Hernández-Hernández J
- Publication year
- Publication venue
- International Conference on Technologies and Innovation
External Links
Snippet
The vision by computer has become a very important tool and powerful in the area of agriculture and agronomy for monitoring and automatic handling of the different agricultural processes. Digital processing of images is used to segment and classify leaves in the corn …
- 241000602080 Dracaena fragrans 0 title description 8
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
- G06K9/4652—Extraction of features or characteristics of the image related to colour
-
- 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
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- 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
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
- G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
-
- 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
- G06T2207/20112—Image segmentation details
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
-
- 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
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
- G06F17/30247—Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Haridasan et al. | Deep learning system for paddy plant disease detection and classification | |
Neupane et al. | Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV) | |
Dhingra et al. | Study of digital image processing techniques for leaf disease detection and classification | |
Sadeghi-Tehran et al. | Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping | |
Francis et al. | Identification of leaf diseases in pepper plants using soft computing techniques | |
Li et al. | Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images | |
Mundada et al. | Detection and classification of pests in greenhouse using image processing | |
Kumar K et al. | Detection of rice plant disease using AdaBoostSVM classifier | |
Kumar et al. | Delineation of field boundary from multispectral satellite images through U-Net segmentation and template matching | |
Pereira et al. | Pixel-based leaf segmentation from natural vineyard images using color model and threshold techniques | |
Ahlswede et al. | Hedgerow object detection in very high-resolution satellite images using convolutional neural networks | |
Kumar et al. | An identification of crop disease using image segmentation | |
Safari et al. | A review on automated detection and assessment of fruit damage using machine learning | |
Li et al. | Detection of small-sized insects in sticky trapping images using spectral residual model and machine learning | |
Owen et al. | Measuring soil coverage using image feature descriptors and the decision tree learning algorithm | |
Bravo-Reyna et al. | Recognition of the damage caused by the cogollero worm to the corn plant, Using artificial vision | |
Saha et al. | Classification of starfruit maturity using smartphone-image and multivariate analysis | |
Gupta et al. | Drought stress detection technique for wheat crop using machine learning | |
Macías-Macías et al. | Mask R-CNN for quality control of table olives | |
Geetha et al. | An innovative method for detection of insect based on mask-r-cnn approach | |
Bini et al. | Intelligent agrobots for crop yield estimation using computer vision | |
Mahenthiran et al. | Smart pest management: an augmented reality-based approach for an organic cultivation | |
Hernández-Hernández et al. | Search for optimum color space for the recognition of oranges in agricultural fields | |
Afonso et al. | Automatic trait estimation in floriculture using computer vision and deep learning | |
Pant et al. | Detection of the affected area and classification of pests using convolutional neural networks from the leaf images |