Low-cost Multispectral Imaging Research Articles

Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.

Canopy imaging offers a non-destructive, efficient way to objectively measure canopy size, detect stress symptoms, and assess pigment concentrations. While it is faster and easier than traditional destructive methods, manual image analysis, including segmentation and evaluation, can be time-consuming. To make imaging more widely accessible, it's essential to reduce the cost of imaging systems and automate the analysis process. We developed a low-cost imaging system with automated analysis using an embedded microcomputer equipped with a monochrome camera and a filter for a total hardware cost of ~USD 500. Our imaging system takes images under blue, green, red, and infrared light, as well as chlorophyll fluorescence. The system uses a Python-based program to collect and analyze images automatically. The multi-spectral imaging system separates plants from the background using a chlorophyll fluorescence image, which is also used to quantify canopy size. The system then generates normalized difference vegetation index (NDVI, "greenness") images and histograms, providing quantitative, spatially resolved information. We verified that these indices correlate with leaf chlorophyll content and can easily add other indices by installing light sources with the desired spectrums. The low cost of the system can make this imaging technology widely available.

A multispectral imaging system developed based on the spectral feature selection method for identification of housefly pupae

Hyperspectral imaging systems can simultaneously obtain spectral and spatial information of fly pupae, especially achieving a large area of spectral information collection in a short period. However, hyperspectral imaging systems are expensive and require much computation. This study attempts to develop a four-channel spectral imaging system composed of a camera and four filters to achieve spectral discrimination of housefly pupae over a large area. It also aims to verify the feasibility of using spectral feature selection methods to guide the development of low-cost specialized spectral systems. Meanwhile, a strategy combining spectral degradation with the traversal method was proposed for determining the parameter indicators of optical components. Firstly, use a microspectrometer to acquire the spectra of fly pupae. Secondly, the successive projections algorithm (SPA) extracts four feature wavelengths related to fly pupa recognition from the full spectrum variables. Then, based on the strategy of spectral degradation combined with the traversal method, the parameters of the four filters are determined. Finally, a 4-channel spectral imaging system was constructed, and a prediction model was built with random forest (RF), while the spectral index (SI) method was used to improve model performance. The results indicate that this low-cost multispectral imaging system can achieve high-performance discrimination of housefly pupae (accuracy = 97.44 %). At the same time, SPA, spectral degradation, and traversal methods can provide effective central wavelength and resolution indicators for constructing low-cost multispectral imaging systems, offering a practical and feasible example to guide the development of specialized low-cost spectral devices using spectral feature selection methods.

Development and performance validation of a low-cost algorithms-based hyperspectral imaging system for radiodermatitis assessment.

Whilst radiotherapy (RT) is widely used for cancer treatment, radiodermatitis caused by RT is one most common severe side effect affecting 95% cancer patients. Accurate radiodermatitis assessment and classification is essential to adopt timely treatment, management and monitoring, which all depend on reliable and objective tools for radiodermatitis grading. We therefore, in this work, reported the development and grading performance validation of a low-cost (∼2318.2 CNY) algorithms-based hyperspectral imaging (aHSI) system for radiodermatitis assessment. The low-cost aHSI system was enabled through Monte Carlo (MC) simulations conducted on multi-spectra acquired from a custom built low-cost multispectral imaging (MSI) system, deriving algorithms-based hyper-spectra with spectral resolution of 1 nm. The MSI system was based on sequentially illuminated narrow-band light-emitting diodes (LEDs) and a CMOS camera. Erythema induced artificially on healthy volunteers was measured by the aHSI system developed, with algorithms-based hyper-spectra and skin layer resolved physiological parameters (i.e., the blood volume fraction (BVF) and the oxygen saturation of hemoglobin in blood, et. al.) derivation using MC simulations. The MC simulations derived BVF and the oxygen saturation of hemoglobin in blood showed significant (P < 0.001, analysis of variance: ANOVA) increase with erythema. Further 1D-convolution neural network (CNN) implemented on the algorithms-based hyper-spectra leads to an overall classification accuracy of 93.1%, suggesting the great potential of low-cost aHSI system developed for radiodermatitis assessment.

Multispectral imaging of nailfold capillaries using light-emitting diode illumination.

The capillaries are the smallest blood vessels in the body, typically imaged using video capillaroscopy to aid diagnosis of connective tissue diseases, such as systemic sclerosis. Video capillaroscopy allows visualization of morphological changes in the nailfold capillaries but does not provide any physiological information about the blood contained within the capillary network. Extracting parameters such as hemoglobin oxygenation could increase sensitivity for diagnosis and measurement of microvascular disease progression. To design, construct, and test a low-cost multispectral imaging (MSI) system using light-emitting diode (LED) illumination to assess relative hemoglobin oxygenation in the nailfold capillaries. An LED ring light was first designed and modeled. The ring light was fabricated using four commercially available LED colors and a custom-designed printed circuit board. The experimental system was characterized and results compared with the illumination model. A blood phantom with variable oxygenation was used to determine the feasibility of using the illumination-based MSI system for oximetry. Nailfold capillaries were then imaged in a healthy subject. The illumination modeling results were in close agreement with the constructed system. Imaging of the blood phantom demonstrated sensitivity to changing hemoglobin oxygenation, which was in line with the spectral modeling of reflection. The morphological properties of the volunteer capillaries were comparable to those measured in current gold standard systems. LED-based illumination could be used as a low-cost approach to enable MSI of the nailfold capillaries to provide insight into the oxygenation of the blood contained within the capillary network.

Two-wavelength image detection of early decayed oranges by coupling spectral classification with image processing

Citrus decay is one of the most serious postharvest diseases, which can cause great economic losses and food safety problem. Early decay has no obvious features, which makes rapid detection and grading of decayed citrus fruit a major challenge for citrus industry. This study utilized the unique image-spectrum fusion property of hyperspectral imaging, and proposed a strategy to quickly identify the citrus with early decay using only two wavelength images. The typical average spectra of sound and decayed tissues were extracted. The linear partial least squares-discriminant analysis (PLS-DA) and nonlinear back-propagation artificial neural network (BP-ANN) models were constructed for classifying two types of tissues. MC-UVE-SPA algorithm by combining Monte Carlo cross-validation (MC-UVE) with successive projections algorithm (SPA) was used to extract 16 variables characterizing two types of tissues. The wavelength images corresponding to the extracted variables were performed principal component analysis (PCA) to find the optimal PC image. Only two wavelength images at 568.8 nm and 771.2 nm were selected by analyzing the weighting coefficients of the third principal component (PC3) image. An improved watershed segmentation method was proposed to segment decay region in oranges based on PC2 image of the selected two wavelength images. Classification performance of the proposed algorithm was evaluated by all samples. The results showed that the overall classification accuracy of 96.6% was achieved, with 100% and 91.3% for the decayed and sound oranges respectively. The proposed detection strategy only involves two wavelength images, which will contribute to the establishment of a fast and low-cost multispectral imaging system for detection of citrus with early decay.

A comparison of ground-based hyperspectral imaging and red-edge multispectral imaging for façade material classification

This paper compares the feasibility of classifying building façade materials in situ via a low-cost multispectral imaging camera with 5 bands compared to a high-cost, short-wave infrared hyperspectral unit with 240 bands. The sensors were used to classify five common façade materials: brick, mortar, stone, painted window frames, and rendering. The data were subjected to a Partial Least Squares Discriminant Analysis by selecting 95 training pixels and 65 validation pixels for each material. Across all materials, the hyperspectral imaging demonstrated a moderate advantage over multispectral imaging (with a correct classification rate of 99% versus 80%) for the selected data and stronger qualitative matching to the entire façade. While the hyperspectral data were consistently superior, the multispectral data still produced valuable results, thereby demonstrating potential as a fast, easy, and much cheaper technology compared to hyperspectral units for in situ façade materials classification.

The AgriQ: A low-cost unmanned aerial system for precision agriculture

Precision agriculture currently presents significant growth thanks to the development of three main technologies: drones, vision-based systems, and embedded electronics. To take advantage of these technologies, services and products have launched cameras, drones, and algorithms in the cloud, from which the farmer can easily obtain valuable information for the optimization of their production. Notwithstanding the previous, in Latin America (and in general in the undeveloped countries), these technologies are costly and sometimes difficult to access for most farmers. This study proposes developing the low-cost Unmanned Aerial System (UAS) for precision agriculture tasks called AgriQ. It comprises three subsystems: (a) the drone; (b) the multispectral imaging system; and (c) the open-source software responsible for computing valuable information for the farmers. Intending to obtain a competitive UAS, we tackle the problem from four main points: (1) the construction of the drone; (2) the vision algorithms; (3) the autonomous trajectory considering all the parameters for properly recovering all the crops’ visual information; and (4) the construction of a low-cost multispectral imaging system with multiple bands. The multispectral imaging system is onboard the quadrotor and is composed of two low-cost cameras that have been modified to output multispectral imagery. With all this unmanned aerial system, we compute 8 vegetation indices that provide useful information to the farmers. To verify that the AgriQ is competitive versus commercial systems, we compared its performance to the professional multispectral camera RedEdge-M and popular Pix4D software. For this purpose, we have conducted experiments in two real environments, one of which is a real crop field. The experiments’ data show that our AgriQ UAS achieved prominent results at a fraction of the commercial system cost. Furthermore, to provide the user with a complete low-cost tool for precision agriculture, all the parts of the AgriQ are presented and explained in detail. The software is developed using open-source code where our contribution is available online on our GitHub site.

Detection of early decayed oranges based on multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method

Detection of early decay caused by fungal infections in citrus fruit still remains one of the major problems in the post-harvest processing and automatic quality grading. A new combination algorithm by merging multispectral principal component image, bi-dimensional empirical mode decomposition and image reconstruction as well as improved watershed segmentation was proposed to detect the early decay in oranges. Segmented principal component analysis based on three wavelength regions including visible and short wavelength near-infrared (500–1050 nm), visible (500–780 nm) and near-infrared (781–1050 nm) was performed to determine the optimal principal component (PC) image that was used to extract the effective wavelength images by weighting coefficient analysis. Seven wavelength images in the spectral region of 500–1050 nm were finally determined to build the multispectral PC images. The bi-dimensional empirical mode decomposition (BEMD) was used to remove noise in the multispectral PC images and further reconstruct images. An improved watershed segmentation method with morphological gradient reconstruction, marker extraction as well as image amendment, was proposed to segment decay regions in fruit by using the reconstructed multispectral PC images. All samples including 220 each of decayed and sound fruit were utilized to assess classification ability of the proposed combination algorithm. The results indicated that identification accuracies of decayed and sound fruit were 97.3% and 100%, respectively. The multispectral principal component image combining both bi-dimensional empirical mode decomposition and watershed segmentation method can be used as an effective tool for detection of early decayed oranges, and it was also promising for development of a fast and low-cost multispectral imaging system.

Hyperspectral image reconstruction using Multi-colour and Time-multiplexed LED illumination

The rapidly rising industrial interest in hyperspectral imaging (HSI) has generated an increased demand for cost effective HSI devices. We are demonstrating a mobile and low-cost multispectral imaging system, enabled by time-multiplexed RGB Light Emitting Diodes (LED) illumination, which operates at video framerate. Critically, a deep Multi-Layer Perceptron (MLP) with HSI prior in the spectral range of 400–950 nm is trained to reconstruct HSI data. We incorporate regularisation and dropout to compensate for overfitting in the largely ill-posed problem of reconstructing the HSI data. The MLP is characterised by a relatively simple design and low computational burden. Experimental results on 51 objects of various references and naturally occurring materials show the effectiveness of this approach in terms of reconstruction error and classification accuracy. We were also able to show that utilising additional colour channels to the three R, G and B channels adds increased value to the reconstruction.

Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed.

Portable, low-cost multispectral imaging system: design, development, validation, and utilization.

Optical spectral images can be used to estimate the amount of bulk absorbers in tissues, specifically oxy- and deoxyhemoglobin, as well as scattering parameters. Most systems that capture spectral image data are large, heavy, and expensive. This paper presents a full end-to-end analysis of a low-cost reflectance-mode multispectral imaging system operating in the visible and near-infrared spectra. The system consists of 13 LEDs mounted on a printed circuit board, a monochrome machine vision camera, and a tablet computer to control the hardware. The bill of materials for the system is less than $1000. Hardware design and implementation are detailed. Calibration, image capture, and preprocessing are also discussed. In validation experiments, excellent agreement is observed in diffuse reflectance measurements between the spectral camera setup and a spectrometer. To demonstrate that such spectral image data can yield meaningful optical measurements in vivo, the forearms of eight volunteers are imaged in the system. Their data are then analyzed to estimate the tissue optical properties of different skin layers using a Monte Carlo lookup table. In three volunteers, spectral images are captured before and after inducing erythema using a warm wet towel. Across the three subjects, a clear increase in the blood content of the superficial plexus layer was observed as a result of the erythema. Collectively, these findings suggest that a low-cost system can capture accurate spectral data and that clinically meaningful information can be derived from it.

Low-cost multispectral imaging for remote sensing of lettuce health

In agricultural remote sensing, unmanned aerial vehicle (UAV) platforms offer many advantages over conventional satellite and full-scale airborne platforms. One of the most important advantages is their ability to capture high spatial resolution images (1–10 cm) on-demand and at different viewing angles. However, UAV platforms typically rely on the use of multiple cameras, which can be costly and difficult to operate. We present the development of a simple low-cost imaging system for remote sensing of crop health and demonstrate it on lettuce (Lactuca sativa) grown in Hong Kong. To identify the optimal vegetation index, we recorded images of both healthy and unhealthy lettuce, and used them as input in an expectation maximization cluster analysis with a Gaussian mixture model. Results from unsupervised and supervised clustering show that, among four widely used vegetation indices, the blue wide-dynamic range vegetation index is the most accurate. This study shows that it is readily possible to design and build a remote sensing system capable of determining the health status of lettuce at a reasonably low cost (<US$100). When combined with recent advances in UAV technology, this system could lead to yield increases for lettuce growers.

MULTISPECTRAL IMAGING AS A TOOL FOR ENHANCING THE READING OF OSTRACA

A recent study shows that multispectral (MS) imaging can improve the legibility of ostraca. Several examples of Iron Age ostraca unearthed over twenty years ago in Israel (Ḥorvat ʿUza and Ḥorvat Radum) are presented, showing how new images taken with an MS system improve the reading of inscriptions that have significantly faded over time. The article provides instructions for constructing a simple and low-cost MS imaging system that yields comparable results to commercial systems.

Low-cost multispectral vegetation imaging system for detecting leaking CO2 gas

As a component of a multisensor approach to monitoring carbon sequestration sites for possible leaks of the CO₂ gas from underground reservoirs, a low-cost multispectral imaging system has been developed for indirect detection of gas leaks through observations of the resulting stress in overlying vegetation. The imager employs front-end optics designed to provide a full 50° field of view with a small, low-cost CMOS detector, while still maintaining quasi-collimated light through the angle-dependent interference filters used to define the spectral bands. Red and near-infrared vegetation reflectances are used to compute the normalized difference vegetation index (NDVI) and spatial and temporal patterns are analyzed statistically to identify regions of anomalous stress, which are then flagged for closer inspection with in-situ CO₂ sensors. The system is entirely self-contained with an onboard compact computer and is housed in a weather-proof housing to enable extended outdoor deployment.

Visualization of basal cell carcinoma by fluorescence diagnosis and independent component analysis

Photodynamic detection (PDD) of skin tumours is based on the visualization of a fluorophores, with the ability to accumulate in tumour tissue, by the use of fluorescence imaging. Of particular importance is the application of δ-5-aminolaevulinic acid (ALA) that, through the process of biosynthesis causes formation of the protoporphyrin IX (PpIX). The PpIX has the ability of selective fluorescence after basal cell carcinoma (BCC) has been treated with ALA. Higher concentration of PpIX in tumour tissue compared to surrounding normal skin is the basis for PDD. Our contribution in this preliminary study is application of the independent component analysis (ICA) to extract the BCC spatial map, by processing fluorescent RGB image acquired under excitation with 405nm light. Comparative performance analysis with other two widely used image processing methods: ratio imaging and optimal threshold based imaging, reveals that ICA produces BCC spatial map that is most consistent in term of diagnostic quality by both visual assessment and calculation of the BCC demarcation line. We believe this represents a solid basis for the design of a compact and low-cost multi-spectral fluorescence imaging system, capable for real time calculation of the skin tumour demarcation.

The Application of a Compact Multispectral Imaging System with Integrated Excitation Source to In vivo Monitoring of Fluorescence During Topical Photodynamic Therapy of Superficial Skin Cancers¶

A novel, compact and low-cost multispectral fluorescence imaging system with an integrated excitation light source is described. Data are presented demonstrating the application of this method to in vivo monitoring of fluorescence before, during and after topical 5-aminolevulinic acid photodynamic therapy of superficial skin cancers. The excitation source comprised a fluorescent tube with the phosphor selected to emit broadband violet light centered at 394 nm. The camera system simultaneously captured spectrally specific images of the fluorescence of the photosensitizer, protoporphyrin IX, the illumination profile and the skin autofluorescence. Real-time processing enabled images to be manipulated to create a composite image of high contrast. The application and validation of this method will allow further detailed studies of the characteristics and time-course of protoporphyrin IX fluorescence, during topical photodynamic therapy in human skin in vivo.

The Application of a Compact Multispectral Imaging System with Integrated Excitation Source to In vivo Monitoring of Fluorescence During Topical Photodynamic Therapy of Superficial Skin Cancers¶

A novel, compact and low-cost multispectral fluorescence imaging system with an integrated excitation light source is described. Data are presented demonstrating the application of this method to in vivo monitoring of fluorescence before, during and after topical 5-aminolevulinic acid photodynamic therapy of superficial skin cancers. The excitation source comprised a fluorescent tube with the phosphor selected to emit broadband violet light centered at 394 nm. The camera system simultaneously captured spectrally specific images of the fluorescence of the photosensitizer, protoporphyrin IX, the illumination profile and the skin autofluorescence. Real-time processing enabled images to be manipulated to create a composite image of high contrast. The application and validation of this method will allow further detailed studies of the characteristics and time-course of protoporphyrin IX fluorescence, during topical photodynamic therapy in human skin in vivo.