[go: up one dir, main page]

Yang et al., 2024 - Google Patents

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor

Yang et al., 2024

View HTML
Document ID
2765889592828380556
Author
Yang L
Nasrat L
Badawy M
Mbadjoun Wapet D
Ourapi M
El-Messery T
Aleksandrova I
Mahmoud M
Hussein M
Elwakeel A
Publication year
Publication venue
PLoS One

External Links

Snippet

Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt …
Continue reading at journals.plos.org (HTML) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • G01N21/359Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light using near infra-red light
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/314Investigating 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
    • G01N2021/3155Measuring in two spectral ranges, e.g. UV and visible
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/47Scattering, i.e. diffuse reflection
    • G01N21/49Scattering, i.e. diffuse reflection within a body or fluid
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/85Investigating moving fluids or granular solids
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/84Systems specially adapted for particular applications
    • G01N21/88Investigating the presence of flaws or contamination
    • G01N21/89Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles

Similar Documents

Publication Publication Date Title
Yang et al. A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor
Qin et al. Line-scan hyperspectral imaging techniques for food safety and quality applications
Surya Prabha et al. Assessment of banana fruit maturity by image processing technique
Liu et al. Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit
Mahendran et al. Application of computer vision technique on sorting and grading of fruits and vegetables
Xiaobo et al. In vivo noninvasive detection of chlorophyll distribution in cucumber (Cucumis sativus) leaves by indices based on hyperspectral imaging
Chen et al. The review of food safety inspection system based on artificial intelligence, image processing, and robotic
Tan et al. Applications of photonics in agriculture sector: A review
Gupta et al. An image processing approach for measurement of chili plant height and width under field conditions
KR102297913B1 (en) Plant growth monitoring system using hyperspectral reflected light and fluorescence scattering, and method thereof
Lu et al. Bruise detection on red bayberry (Myrica rubra Sieb. & Zucc.) using fractal analysis and support vector machine
CN106290238A (en) A kind of apple variety method for quick identification based on high light spectrum image-forming
Ramos et al. Non‐invasive setup for grape maturation classification using deep learning
Zhang et al. A novel image detection method for internal cracks in corn seeds in an industrial inspection line
Kong et al. Off-nadir hyperspectral sensing for estimation of vertical profile of leaf chlorophyll content within wheat canopies
Durmuş et al. Detection of aflatoxin and surface mould contaminated figs by using Fourier transform near‐infrared reflectance spectroscopy
Elwakeel et al. Designing, optimizing, and validating a low-cost, multi-purpose, automatic system-based RGB color sensor for sorting fruits
Sun et al. Detection of the soluble solid contents from fresh jujubes during different maturation periods using NIR hyperspectral imaging and an artificial bee colony
Eron et al. Computer vision-aided intelligent monitoring of coffee: Towards sustainable coffee production
Pham et al. Hyperspectral imaging system with rotation platform for investigation of jujube skin defects
Pamornnak et al. An automatic and rapid system for grading palm bunch using a Kinect camera
Guo et al. Field‐based individual plant phenotyping of herbaceous species by unmanned aerial vehicle
Vignati et al. Hyperspectral imaging for fresh-cut fruit and vegetable quality assessment: Basic concepts and applications
Abdulridha et al. Evaluation of stem rust disease in wheat fields by drone hyperspectral imaging
Figorilli et al. Olive fruit selection through ai algorithms and RGB imaging