Farjon et al., 2020 - Google Patents
Detection and counting of flowers on apple trees for better chemical thinning decisionsFarjon et al., 2020
View PDF- Document ID
- 12460513591154238387
- Author
- Farjon G
- Krikeb O
- Hillel A
- Alchanatis V
- Publication year
- Publication venue
- Precision Agriculture
External Links
Snippet
Accurate chemical thinning of apple trees requires estimation of their blooming intensity, and determination of the blooming peak date. Performing this task, as of today, requires human experts to be present in the orchards for the entire blossom period or extrapolate using a …
- 238000001514 detection method 0 title abstract description 65
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
- G06K9/00624—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
- G06K9/62—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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting 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
-
- 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
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
-
- 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
- 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
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Farjon et al. | Detection and counting of flowers on apple trees for better chemical thinning decisions | |
Tripathi et al. | A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: A survey | |
Kamilaris et al. | Deep learning in agriculture: A survey | |
Puttemans et al. | Automated visual fruit detection for harvest estimation and robotic harvesting | |
Jhuria et al. | Image processing for smart farming: Detection of disease and fruit grading | |
Chaivivatrakul et al. | Texture-based fruit detection | |
Zhang et al. | Computer vision‐based tree trunk and branch identification and shaking points detection in Dense‐Foliage canopy for automated harvesting of apples | |
Majeed et al. | Determining grapevine cordon shape for automated green shoot thinning using semantic segmentation-based deep learning networks | |
Sood et al. | Computer vision and machine learning based approaches for food security: A review | |
Häni et al. | Apple counting using convolutional neural networks | |
Farjon et al. | Deep-learning-based counting methods, datasets, and applications in agriculture: A review | |
Bhattarai et al. | A weakly-supervised approach for flower/fruit counting in apple orchards | |
Malik et al. | Detection and counting of on-tree citrus fruit for crop yield estimation | |
Majeed et al. | Estimating the trajectories of vine cordons in full foliage canopies for automated green shoot thinning in vineyards | |
Weis et al. | Detection and identification of weeds | |
Halstead et al. | Fruit quantity and quality estimation using a robotic vision system | |
Motie et al. | Identification of Sunn-pest affected (Eurygaster Integriceps put.) wheat plants and their distribution in wheat fields using aerial imaging | |
Mishra et al. | A robust pest identification system using morphological analysis in neural networks | |
Eladl et al. | A proposed plant classification framework for smart agricultural applications using UAV images and artificial intelligence techniques | |
Safari et al. | A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning | |
Alam et al. | Drone-Based Crop Product Quality Monitoring System: An Application of Smart Agriculture | |
Bulanon et al. | Machine vision system for orchard management | |
Balram et al. | Crop field monitoring and disease detection of plants in smart agriculture using internet of things | |
Afonso et al. | Deep learning based plant part detection in Greenhouse settings | |
Sharma et al. | Pest detection in plants using convolutional neural network |