Portable multispectral imaging system based on Raspberry Pi

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 322-329 ◽  
Author(s):  
Nuria Lopez-Ruiz ◽  
Fernando Granados-Ortega ◽  
Miguel Angel Carvajal ◽  
Antonio Martinez-Olmos
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Light Emitting Diode ◽  
Low Cost ◽  
Image Sensor ◽  
Raspberry Pi ◽  
Image Processing Algorithm ◽  
Content Type

Purpose In this work, the authors aim to present a compact low-cost and portable spectral imaging system for general purposes. The developed system provides information that can be used for a fast in situ identification and classification of samples based on the analysis of captured images. The connectivity of the instrument allows a deeper analysis of the images in an external computer. Design/methodology/approach The wavelength selection of the system is carried out by light multiplexing through a light-emitting diode panel where eight wavelengths covering the spectrum from ultraviolet (UV) to near-infrared region (NIR) have been included. The image sensor used is a red green blue – infrared (RGB-IR) micro-camera controlled by a Raspberry Pi board where a basic image processing algorithm has been programmed. It allows the visualization in an integrated display of the reflectance and the histogram of the images at each wavelength, including UV and NIRs. Findings The prototype has been tested by analyzing several samples in a variety of applications such as detection of damaged, over-ripe and sprayed fruit, classification of different type of plastic materials and determination of properties of water. Originality/value The designed system presents some advantages as being non-expensive and portable in comparison to other multispectral imaging systems. The low-cost and size of the camera module connected to the Raspberry Pi provides a compact instrument for general purposes.

scholarly journals Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4398
Author(s):  
Steven Hobbs ◽  
Andrew Lambert ◽  
Michael J. Ryan ◽  
David J. Paull ◽  
John Haythorpe
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Raspberry Pi ◽  
Vegetation Analysis ◽  
Processing Pipeline ◽  
Asteroid Exploration ◽  
Hyper Spectral ◽  
Infra Red ◽  
Scientific Results

Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been identified. Recent progress in the availability of processors and sensors has created the possibility of development of low-cost instruments able to return useful scientific results. In this work, two Raspberry Pi camera types and a panchromatic astronomy camera were trialed within a pushbroom sensor to determine their utility in measuring and processing the spectrum in reflectance. Algorithmic classification of all 15 test materials exhibiting spectral phenomenology between 600 and 800 nm was easily performed. Calibration against a spectrometer considers the effects of the sensor, inherent image processing pipeline and compression. It was found that even the color Raspberry Pi cameras that are popular with STEM applications were able to record and distinguish between most minerals and, contrary to expectations, exploited the infra-red secondary transmissions in the Bayer filter to gain a wider spectral range. Such a camera without a Bayer filter can markedly improve spectral sensitivity but may not be necessary.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

Near-infrared fundus imaging system with light illumination from an electronic contact lens

2022 ◽  
Vol 15 (2) ◽  
pp. 027001
Author(s):  
Yang Cui ◽  
Taiki Takamatsu ◽  
Koichi Shimizu ◽  
Takeo Miyake
Keyword(s):  
Contact Lens ◽  
Near Infrared ◽  
Imaging System ◽  
Low Cost ◽  
Light Illumination ◽  
Fundus Images ◽  
Fundus Imaging ◽  
Point Spread ◽  
Spread Function ◽  
Dependent Point

Abstract As for the diagnosis and treatment of eye diseases, an ideal fundus imaging system is expected to be portability, low cost, and high resolution. Here, we demonstrate a non-mydriatic near-infrared fundus imaging system with light illumination from an electronic contact lens (E-lens). The E-lens can illuminate the retinal and choroidal structures for capturing the fundus images when voltage is applied wirelessly to the lens. And we also reconstruct the images with a depth-dependent point-spread function to suppress the scattering effect that eventually visualizes the clear fundus images.

Marker-guided auto-landing on a moving platform

2017 ◽  
Vol 5 (1) ◽  
pp. 28-42 ◽  
Author(s):  
Iryna Borshchova ◽  
Siu O’Young
Keyword(s):  
Image Processing ◽  
Visual Servoing ◽  
Low Cost ◽  
Field Tests ◽  
Processing Algorithm ◽  
Prototype System ◽  
Image Processing Algorithm ◽  
Suggested Approach ◽  
Content Type ◽  
Moving Platform

Purpose The purpose of this paper is to develop a method for a vision-based automatic landing of a multi-rotor unmanned aerial vehicle (UAV) on a moving platform. The landing system must be highly accurate and meet the size, weigh, and power restrictions of a small UAV. Design/methodology/approach The vision-based landing system consists of a pattern of red markers placed on a moving target, an image processing algorithm for pattern detection, and a servo-control for tracking. The suggested approach uses a color-based object detection and image-based visual servoing. Findings The developed prototype system has demonstrated the capability of landing within 25 cm of the desired point of touchdown. This auto-landing system is small (100×100 mm), light-weight (100 g), and consumes little power (under 2 W). Originality/value The novelty and the main contribution of the suggested approach are a creative combination of work in two fields: image processing and controls as applied to the UAV landing. The developed image processing algorithm has low complexity as compared to other known methods, which allows its implementation on general-purpose low-cost hardware. The theoretical design has been verified systematically via simulations and then outdoors field tests.

scholarly journals Monitoring Tuberculosis Drug Activity in Live Animals by Using Near-Infrared Fluorescence Imaging

2019 ◽  
Vol 63 (12) ◽  
Author(s):  
Raphael Sommer ◽  
Stewart T. Cole
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Near Infrared ◽  
Imaging System ◽  
Limit Of Detection ◽  
Bacterial Load ◽  
Genetically Engineered ◽  
Fluorescent Imaging ◽  
Effective Vaccine ◽  
Content Type ◽  
Immunodeficient Mice

ABSTRACT Worldwide, tuberculosis (TB) is the leading cause of death due to infection with a single pathogenic agent, Mycobacterium tuberculosis. In the absence of an effective vaccine, new, more powerful antibiotics are required to halt the growing spread of multidrug-resistant strains and to shorten the duration of TB treatment. However, assessing drug efficacy at the preclinical stage remains a long and fastidious procedure that delays the progression of drugs down the pipeline and towards the clinic. In this investigation, we report the construction, optimization, and characterization of genetically engineered near-infrared (NIR) fluorescent reporter strains of the pathogens Mycobacterium marinum and Mycobacterium tuberculosis that enable the direct visualization of bacteria in infected zebrafish and mice, respectively. Fluorescence could be measured precisely in infected immunodeficient mice, while its intensity appeared to be below the limit of detection in immunocompetent mice, probably because of the lower bacterial load obtained in these animals. Furthermore, we show that the fluorescence level accurately reflects the bacterial load, as determined by CFU enumeration, thus enabling the efficacy of antibiotic treatment to be assessed in live animals in real time. The NIR fluorescent imaging system disclosed here is a valuable resource for TB research and can serve to accelerate drug development.

scholarly journals Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Tasya Vadya Sarira ◽  
Kenneth Clarke ◽  
Philip Weinstein ◽  
Lian Pin Koh ◽  
Megan Lewis
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
South Australia ◽  
Rapid Identification ◽  
Breeding Habitat ◽  
Mapping Accuracy ◽  
Normalised Difference Vegetation Index ◽  
Mosquito Breeding

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen’s Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

Non-destructively sensing pork quality using near infrared multispectral imaging technique

RSC Advances ◽  
2015 ◽  
Vol 5 (116) ◽  
pp. 95903-95910 ◽  
Author(s):  
Qiping Huang ◽  
Huanhuan Li ◽  
Jiewen Zhao ◽  
Gengping Huang ◽  
Quansheng Chen
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Imaging Technique ◽  
Water Holding Capacity ◽  
Pork Quality ◽  
Water Holding ◽  
Non Destructive

Near infrared multispectral imaging system based on three wavebands—1280 nm, 1440 nm and 1660 nm—was developed for the non-destructive sensing of the tenderness and water holding capacity of pork.

Non-destructive determination of total polyphenols content and classification of storage periods of Iron Buddha tea using multispectral imaging system

2015 ◽  
Vol 176 ◽  
pp. 130-136 ◽  
Author(s):  
Chuanwu Xiong ◽  
Changhong Liu ◽  
Wenjuan Pan ◽  
Fei Ma ◽  
Can Xiong ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Total Polyphenols ◽  
Non Destructive

OBD-II and raspberry Pi technology to diagnose car’s machine current condition: study literature

2017 ◽  
Vol 34 (10) ◽  
pp. 15-21 ◽  
Author(s):  
Sonya Rapinta Manalu ◽  
Jurike Moniaga ◽  
Dionisius Andrian Hadipurnawan ◽  
Firda Sahidi
Keyword(s):  
Real Time ◽  
Mobile Technology ◽  
Design Methodology ◽  
Low Cost ◽  
Raspberry Pi ◽  
Data Link ◽  
Content Type ◽  
History Of ◽  
To Receive ◽  
Technology Comparison

Purpose Low-cost microcomputers such as the Raspberry Pi are common in library makerspaces. This paper aims to create an OBD-II technology to diagnose a vehicle’s condition. Design/methodology/approach An OBD-II scanner plugged into the OBD-II port or usually called the data link connector (DLC), sends diagnostics to the Raspberry Pi. Findings Compared with other microcontrollers such as Arduino, the Raspberry Pi was chosen because it sustains the application to receive real-time diagnostics, process the diagnostics and send commands to automobiles at the same time, rather than Arduino that must wait for another process finished to run another process. Originality/value This paper also represents the history of mobile technology and OBD-II technology, comparison between Arduino and Raspberry Pi and Node.

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