Onwimol et al., 2021 - Google Patents
An Automatic Method for Rice Seed Vigor Classification Via Radicle Emergence Test Using Image Processing, Curve Fitting and Clustering MethodOnwimol et al., 2021
View PDF- Document ID
- 2465136552480327799
- Author
- Onwimol D
- Phimcharoen W
- Sermwuthisarn P
- Tejakhod S
- Chaisan T
- Publication year
External Links
Snippet
Background: Rice seed vigor classi cation is important for seed storage management by seed producers and by farmers planning their cultivation activities. Field emergence is a direct method of seed vigor testing but is laborious, time-consuming and subjective. The …
- 235000007164 Oryza sativa 0 abstract description 47
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
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- 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
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection circuits for computing concentration
- G01N21/274—Calibration, base line adjustment, drift correction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/02—Investigating or analysing materials by specific methods not covered by the preceding groups food
- G01N33/12—Investigating or analysing materials by specific methods not covered by the preceding groups food meat; fish
-
- 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
- G01N21/84—Systems specially adapted for particular applications
- G01N21/87—Investigating jewels
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Electro-optical investigation, e.g. flow cytometers
-
- 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
- G06T2207/30108—Industrial image inspection
-
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bock et al. | From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy | |
| Hassanijalilian et al. | Chlorophyll estimation in soybean leaves infield with smartphone digital imaging and machine learning | |
| Bianchini et al. | Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality | |
| US7218775B2 (en) | Method and apparatus for identifying and quantifying characteristics of seeds and other small objects | |
| Shahin et al. | A machine vision system for grading lentils | |
| Venora et al. | Identification of Italian landraces of bean (Phaseolus vulgaris L.) using an image analysis system | |
| Mahendran et al. | Application of computer vision technique on sorting and grading of fruits and vegetables | |
| US20200334804A1 (en) | Methods of yield assessment with crop photometry | |
| WO2009023110A1 (en) | Method and system for digital image analysis of maize | |
| Kuchekar et al. | Rice grain quality grading using digital image processing techniques | |
| Lottering et al. | Optimising the spatial resolution of WorldView-2 pan-sharpened imagery for predicting levels of Gonipterus scutellatus defoliation in KwaZulu-Natal, South Africa | |
| Lai et al. | Application of pattern recognition techniques in the analysis of cereal grains | |
| Shahin et al. | Lentil type identification using machine vision | |
| Diaz-Garcia et al. | GiNA, an efficient and high-throughput software for horticultural phenotyping | |
| Donis-González et al. | Color vision system to assess English walnut (Juglans Regia) kernel pellicle color | |
| Chopin et al. | Land-based crop phenotyping by image analysis: consistent canopy characterization from inconsistent field illumination | |
| Qiao et al. | Vigour testing for the rice seed with computer vision-based techniques | |
| Horgan et al. | Changes in reflectance of rice seedlings during planthopper feeding as detected by digital camera: Potential applications for high-throughput phenotyping | |
| Al-Saif et al. | Identification of Indian jujube varieties cultivated in Saudi Arabia using an artificial neural network | |
| Aznan et al. | Rice seed varieties identification based on extracted colour features using image processing and artificial neural network (ANN) | |
| Silva et al. | X-ray, multispectral and chlorophyll fluorescence images: innovative methods for evaluating the physiological potential of rice seeds | |
| Behera et al. | Image processing based detection & size estimation of fruit on mango tree canopies | |
| Onwimol et al. | An Automatic Method for Rice Seed Vigor Classification Via Radicle Emergence Test Using Image Processing, Curve Fitting and Clustering Method | |
| Limão et al. | Classification of lentil seed vigor based on seedling image analysis techniques and interactive machine learning | |
| Onwimol et al. | An Automatic Method for Rice Seed Vigor Classification Via Radicle Emergence Testing Using Image-Processing, Curve-Fitting and Clustering Methods |