Liu et al., 2021 - Google Patents
Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithmsLiu et al., 2021
View PDF- Document ID
- 10210766200085143762
- Author
- Liu S
- Jin X
- Nie C
- Wang S
- Yu X
- Cheng M
- Shao M
- Wang Z
- Tuohuti N
- Bai Y
- Liu Y
- Publication year
- Publication venue
- Plant Physiology
External Links
Snippet
Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to …
- 238000010801 machine learning 0 title description 27
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/314—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry with comparison of measurements at specific and non-specific wavelengths
- G01N2021/3155—Measuring in two spectral ranges, e.g. UV and visible
-
- 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
- G06T2207/10024—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
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
-
- 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
- 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
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
- G01J3/28—Investigating the spectrum
- G01J3/2823—Imaging spectrometer
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- 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
-
- 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
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liu et al. | Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms | |
| Guo et al. | Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping | |
| Zhang et al. | Assessment of defoliation during the Dendrolimus tabulaeformis Tsai et Liu disaster outbreak using UAV-based hyperspectral images | |
| Xu et al. | An improved approach to estimate ratoon rice aboveground biomass by integrating UAV-based spectral, textural and structural features | |
| Khan et al. | Estimation of vegetation indices for high-throughput phenotyping of wheat using aerial imaging | |
| KR102125780B1 (en) | Applaratus for Monitoring Crop Growth through Multispectral Image Histogram Pattern Analysis of Plot Unit | |
| Wan et al. | A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles | |
| CN112147078B (en) | Multi-source remote sensing monitoring method for crop phenotype information | |
| Sharifi | Estimation of biophysical parameters in wheat crops in Golestan province using ultra-high resolution images | |
| US20220307971A1 (en) | Systems and methods for phenotyping | |
| Liu et al. | Exploring multi-features in UAV based optical and thermal infrared images to estimate disease severity of wheat powdery mildew | |
| Jia et al. | UAV remote sensing image mosaic and its application in agriculture | |
| US20250274643A1 (en) | Real-time multi-spectral system and method | |
| Meivel et al. | Monitoring of potato crops based on multispectral image feature extraction with vegetation indices | |
| Zhao et al. | Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model | |
| Chapman et al. | Visible, near infrared, and thermal spectral radiance on-board UAVs for high-throughput phenotyping of plant breeding trials | |
| CN115598071A (en) | Plant growth distribution state detection method and device | |
| Milella et al. | Consumer-grade imaging system for NDVI measurement at plant scale by a farmer robot | |
| Liu et al. | Research on the estimation of wheat AGB at the entire growth stage based on improved convolutional features | |
| Latif et al. | Mapping wheat response to variations in N, P, Zn, and irrigation using an unmanned aerial vehicle | |
| Wu et al. | Accurate estimation of grain number per panicle in winter wheat by synergistic use of UAV imagery and meteorological data | |
| Li et al. | Towards robust registration of heterogeneous multispectral UAV imagery: A two-stage approach for cotton leaf lesion grading | |
| CN120236192A (en) | A method for constructing a high-throughput comprehensive scoring model for salt tolerance of pea based on drones | |
| Izzo et al. | An initial analysis of real-time sUAS-based detection of grapevine water status in the Finger Lakes Wine Country of Upstate New York | |
| Xiao et al. | Investigating the 3D distribution of Cercospora leaf spot disease in sugar beet through fusion methods |