Reim et al., 2022 - Google Patents
Development of a digital monitoring system for pear rust and fire blight in fruit orchardsReim et al., 2022
- Document ID
- 2907991347689104810
- Author
- Reim S
- Pflanz M
- Maß V
- Geyer M
- Seidl-Schulz J
- Leipnitz M
- Fritzsche E
- Flachowsky H
- Publication year
- Publication venue
- XXXI International Horticultural Congress (IHC2022): III International Symposium on Mechanization, Precision Horticulture, and 1360
External Links
Snippet
The increasing introduction and spread of quarantine phytopathogens, promoted by changing climate conditions, is a major challenge of European commercial fruit growing and breeding. Control measures usually aim to detect and contain an infestation at an early …
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
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US12082541B2 (en) | Plant treatment based on morphological and physiological measurements | |
WO2021255458A1 (en) | System and method for crop monitoring | |
Costa et al. | Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve | |
Yu et al. | Progress in the application of cnn-based image classification and recognition in whole crop growth cycles | |
Kierdorf et al. | GrowliFlower: An image time‐series dataset for GROWth analysis of cauLIFLOWER | |
Diago et al. | On‐the‐go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis | |
Palacios et al. | Deep learning and computer vision for assessing the number of actual berries in commercial vineyards | |
Thakur et al. | An extensive review on agricultural robots with a focus on their perception systems | |
Olenskyj et al. | End-to-end deep learning for directly estimating grape yield from ground-based imagery | |
Mahmud et al. | Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management | |
Ouyang et al. | UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications | |
Gregorio et al. | Sensing crop geometry and structure | |
Kurtser et al. | RGB-D datasets for robotic perception in site-specific agricultural operations—A survey | |
McCarthy et al. | Automated variety trial plot growth and flowering detection for maize and soybean using machine vision | |
Cavalcanti et al. | Use of RGB images from unmanned aerial vehicle to estimate lettuce growth in root-knot nematode infested soil | |
Fevgas et al. | Detection of biotic or abiotic stress in vineyards using thermal and RGB images captured via IoT sensors | |
Liakos et al. | In-season prediction of yield variability in an apple orchard | |
Aldana-Aguilar et al. | Estimation of Shade Levels in Coffee Cultivation Using Segmentation Methods and Deep Learning | |
Reim et al. | Development of a digital monitoring system for pear rust and fire blight in fruit orchards | |
Negrete | Artificial vision in Mexican agriculture, a new techlogy for increase food security | |
Junior et al. | Weed mapping using a machine vision system | |
Reim et al. | Establishment of a UAV-based phenotyping method for European pear rust in fruit orchards | |
JP2025503183A (en) | Detecting plant diseases at the onset stage | |
Abdalla et al. | Maintaining Optimum Closeup in Wheat FHB Detection Using 360-Degree Deep Scanning Method | |
Sugiura et al. | Development of high-throughput field phenotyping system using imagery from unmanned aerial vehicle |