Hyperspectral System Research Articles

Sensitivity analysis of the dark spectrum fitting atmospheric correction for metre- and decametre-scale satellite imagery using autonomous hyperspectral radiometry.

The performance of the dark spectrum fitting (DSF) atmospheric correction algorithm is evaluated using matchups between metre- and decametre-scale satellite imagery as processed with ACOLITE and measurements from autonomous PANTHYR hyperspectral radiometer systems deployed in the Adriatic and North Sea. Imagery from the operational land imager (OLI) on Landsat 8, the multispectral instrument (MSI) on Sentinel-2 A and B, and the PlanetScope CubeSat constellation was processed for both sites using a fixed atmospheric path reflectance in a small region of interest around the system's deployment location, using a number of processing settings, including a new sky reflectance correction. The mean absolute relative differences (MARD) between in situ and satellite measured reflectances reach <20% in the Blue and 11% in the Green bands around 490 and 560 nm for the best performing configuration for MSI and OLI. Higher relative errors are found for the shortest Blue bands around 440 nm (30-100% MARD), and in the Red-Edge and near-infrared bands (35-100% MARD), largely influenced by the lower absolute data range in the observations. Root mean squared differences (RMSD) increase from 0.005 in the NIR to about 0.015-0.020 in the Blue band, consistent with increasing atmospheric path reflectance. Validation of the Red-Edge and NIR bands on Sentinel-2 is presented, as well as for the first time, the Panchromatic band (17-26% MARD) on Landsat 8, and the derived Orange contra-band (8-33% MARD for waters in the algorithm domain, and around 40-80% MARD overall). For Sentinel-2, excluding the SWIR bands from the DSF gave better performances, likely due to calibration issues of MSI at longer wavelengths. Excluding the SWIR on Landsat 8 gave good performance as well, indicating robustness of the DSF to the available band set. The DSF performance was found to be rather insensitive to (1) the wavelength spacing in the lookup tables used for the atmospheric correction, (2) the use of default or ancillary information on gas concentration and atmospheric pressure, and (3) the size of the ROI over which the path reflectance is estimated. The performance of the PlanetScope constellation is found to be similar to previously published results, with the standard DSF giving the best results in the visible bands in terms of MARD (24-40% overall, and 18-29% for the turbid site). The new sky reflectance correction gave mixed results, although it reduced the mean biases for certain configurations and improved results for the processing excluding the SWIR bands, giving lower RMSD and MARD especially at longer wavelengths (>600 nm). The results presented in this article should serve as guidelines for general use of ACOLITE and the DSF.

EcoSpec: Highly Equipped Tower-Based Hyperspectral and Thermal Infrared Automatic Remote Sensing System for Investigating Plant Responses to Environmental Changes.

Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.

WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF

Unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral imagery with a high spatial resolution (which we refer to here as H2 imagery). As a result of the low operating cost, high flexibility, and the ability to achieve real-time data acquisition, UAV-borne hyperspectral systems have become an important data source for remote sensing based agricultural monitoring. However, precise crop classification based on UAV-borne H2 imagery is a challenging task when faced with a number of different crop classes. The traditional hyperspectral classification methods, such as the spectral-based and object-oriented classification methods, have difficulty in classifying H2 imagery, faced with the problems of salt-and-pepper (SP) noise and scale selection. In this article, the deep convolutional neural network with a conditional random field classifier (CNNCRF) framework is proposed for precise crop classification with UAV-borne H2 imagery. In the proposed method, a deep convolutional neural network (CNN) is designed to extract and fuse in-depth spectral and local spatial features, and the conditional random field (CRF) model further incorporates the spatial-contextual information to improve the problem of holes and isolated regions in the classification map. Meanwhile, virtual sample augmentation based on the hyperspectral imaging mechanism is used to lessen the issue of the limited labeled samples. To validate the results, a new dataset—the Wuhan UAV-borne hyperspectral image (WHU-Hi) dataset—has been built for precise crop classification. The experimental results obtained using the WHU-Hi dataset confirm the accuracy and visualization performance of the proposed CNNCRF classification method, which outperforms the previous methods. In addition, the WHU-Hi dataset could serve as a benchmark dataset for hyperspectral image classification studies.

Calibration transfer across multiple hyperspectral imaging-based plant phenotyping systems: I – Spectral space adjustment

Hyperspectral Imaging is one of the most popular technologies in plant phenotyping. Various kinds of hyperspectral imaging systems have been developed in the past years. However, there are always significant differences in sensors and imaging environmental conditions between different facilities, which makes it difficult to share image processing algorithms and plant features prediction models. Calibration transfer between the imaging systems is critically important. The white referencing calibration has been proved effective in removing the major lighting intensity differences, but significant spectral differences between systems still exist. In this study, we considered the calibration transfer as a spectral adjustment process and compared four different methods (DS, PDS, DPDS and SST) for adjusting the spectra. To perform the calibration transfer, maize plants were imaged using push-broom style, top-view VNIR hyperspectral camera in two greenhouse phenotyping facilities (inter-facility) and within the same facility (intra-facility) under different imaging conditions. The suggested spectral adjustment methods were tested using master partial least squares regression (PLSR) based calibration models developed for predicting the relative water content (RWC) and nitrogen content (N) of maize plants. Results showed that spectral space transformation (SST) decreased the RMSEv from 9.450% and 10.636% to 3.280% and 2.424% for the predicted RWC of intra and inter-facility transfer, respectively, while the corresponding measures achieved by direct standardization (DS) were 3.412% and 3.105%, respectively. In case of N predictions, similar results were observed for DS and SST. The other two methods (PDS and DPDS) reduced the prediction error for RWC and N, but their performance was not on par with DS and SST. The results indicated that the proposed methods, especially DS and SST were able to alleviate the perturbations inherited in the spectra and thus can help to avoid the need for time-consuming, labor-intensive and costly full recalibration process that arose due to the intra or inter-facility variations.

Hyperspectral reflectance system for plant diagnostics

The article presents a hyperspectral reflectance system. The spectral range of the system varies from 480 to 900 nm with a spectral resolution of approximately 5 nm and a spatial resolution of at least 0.1 mm. The developed system is able to measure the reflectivity of objects, resulting in a two-dimensional image with full spectral information in each pixel (“hypercube”). The article describes the procedure for image calibration and correction with respect to system artifacts caused by optics and lighting conditions across the entire spectrum. The operation of the hyperspectral imaging system was demonstrated on the samples of the Siberian 12 wheat leaves, healthy and affected by powdery mildew (Blumeria graminis (DC) Speer). The proposed use of the hyperspectral reflectance system is to conduct research in early detection and localization of agricultural plants lesions.

Application of optical technologies in the US poultry slaughter facilities for the detection of poultry carcase condemnation

ABSTRACT 1. This article reviews the studies on optical technologies for automated poultry carcase inspection, discusses challenges and potential solutions in their real-time applications in poultry slaughter facilities. 2. Over the past few decades, extensive research has been underway to develop an optical technology-based machine vision system for automated inspection of poultry carcases and viscera. Such an automated technology will not only aid in carcase inspection to maximise food safety, but it will also support the U.S. New Poultry Inspection System’s aim to foster innovation in poultry processing as well as increase line speed. 3. Many earlier studies based on visible and near-infrared spectroscopy showed promise, but could not be implemented successfully in an on-line poultry processing plant. Currently, multi- and hyper-spectral imaging-based machine vision systems have shown promising outcomes. 5. The critical hurdles for real-time application of automated imaging technology in poultry carcase inspection include high-speed processing lines, slaughter facilities environment and variation in broiler rearing practices. Therefore, further improvement in imaging and machine vision technologies based on physiochemical properties on poultry carcases, the establishment of more technology friendly inspection station, and an integrated data management for different rearing practices are essential to overcome those hurdles.

Evaluation of the Data Quality from a Round-Robin Test of Hyperspectral Imaging Systems.

In this study, the results from a round-robin test of hyperspectral imaging systems are presented and analyzed. Fourteen different pushbroom hyperspectral systems from eight different institutions were used to acquire spectral cubes from the visible, near infra-red and short-wave infra-red regions. Each system was used to acquire a common set of targets under their normal operating conditions with the data calibrated and processed using the standard processing pipeline for each system. The test targets consisted of a spectral wavelength standard and of a custom-made pigment panel featuring Renaissance-era pigments frequently found in paintings from that period. The quality and accuracy of the resulting data was assessed with quantitative analyses of the spectral, spatial and colorimetric accuracy of the data. The results provide a valuable insight into the accuracy, reproducibility and precision of hyperspectral imaging equipment when used under routine operating conditions. The distribution and type of error found within the data can provide useful information on the fundamental and practical limits of such equipment when used for applications such as spectral classification, change detection, colorimetry and others.

High-resolution hyperspectral-based continuous mineralogical and total organic carbon analysis of the Eagle Ford Group and associated formations in south Texas

Hyperspectral sensing is used to generate mineral maps of a fine-grained, vertical Eagle Ford Group cored section and underlying Buda Limestone and Del Rio Formation in south Texas. This technology produces a map that shows the distribution of minerals on the core surface. Three different cameras within the hyperspectral core-imaging system were used to image a 99.1-m (325-ft) core: a (1) line scan camera, which produces a high-resolution red–green–blue (120-μm) natural-color photograph of the dry core from the visible light spectrum; (2) short-wave infrared (SWIR) spectrometer (300–500-μm resolution); and (3) long-wave infrared (LWIR) spectrometer (300–500-μm resolution). In 2016, the introduction of a new high-resolution LWIR spectrometer made it possible to identify minerals in cores that were not detected by previous SWIR systems. High-resolution hyperspectral imaging technology using both the SWIR and LWIR spectra provides a significant step toward quantifying mineralogy and total organic carbon. Hyperspectral imaging is a powerful tool for studying textural and fabric relationships because it detects and highlights the major mineralogical changes that occur between depositional beds and among depositional features. Calcite compositional variations and cyclicity appear to be closely related. The Sr-rich calcite appears to indicate a smaller allochem size dominance as well as the slower sedimentation rates that are required for nannofossils to settle from suspension. Finally, the observed diagnostic cyclicity of the lower Eagle Ford can be related to coccolithophore productivity.

Computer Vision and Spectral Analysis Technologies for Non-Invasive Plant Studying

Computer vision and spectral analysis of digital images are technologies that allow the use of automated and robotic systems for non-invasive plant studying, production and harvesting of agricultural products, phenotyping and selection of new plant species. (Research purpose) The research purpose is in analyzing the application of modern digital non-invasive methods of plant research using computer (technical) vision and prospects for their implementation. (Materials and methods) Authors have reviewed the works on the use of non-invasive methods for obtaining information about the state of plants. The article presents classification and analyze of the collected materials according to the criteria for collecting and analyzing digital data, the scope of application and prospects for implementation. Authors used the methods of a systematic approach to the research problem. (Results and discussion) The article presents the main directions of using computer vision systems and digital image analysis. The use of computer vision technologies in plant phenotyping and selection reduces the labor cost of research, allowing the formation of digital databases with a clear structure and classification by morphological features. It was found that the introduction of neural networks in the process of digital image processing increases the accuracy of plant recognition up to 99.9 percent, and infectious diseases up to 80 percent on average. (Conclusions) The article shows that in studies using hyperspectral optical cameras and sensors are used cameras with an optical range from 400 to 1000 nanometers, and in rare cases, hyperspectral camera systems with a total coverage of the optical range from 350 to 2000 nanometers. These optical systems are mainly installed on unmanned aerial vehicles to determine vegetation indices, foci of infection and the fertility of agricultural fields. It was found that computer vision systems with hyperspectral cameras could be used in conjunction with fluorescent plant markers, which makes it possible to solve complex problems of crop recognition without involving computational resources.

Compressed single-shot hyperspectral imaging for combustion diagnostics.

This paper demonstrates a compressed sensing-based single-shot hyperspectral imaging system for combustion diagnostics. The hyperspectral system can capture well-resolved spectra in a 2D plane through a single shot, i.e., converting a 3D data cube of 2D spatial and 1D spectral information into a compressed 2D hyperspectral image. Experimentally, the light emissions are first coded by a random binary pattern to generate the hyperspectral content, which is then sent through a spectrometer. The resulting compressed hyperspectral image is computationally analyzed to recover original 2D spatial and 1D spectral information. C2∗ and CH∗ chemiluminescence emissions of a methane/air flame at various equivalence ratios are measured using the compressed hyperspectral imaging technique. Comparison to traditional measurements shows good agreement in the correlation of emission ratio to equivalence ratio. The technique can be further applied to other laser-based combustion diagnostics.

Yield modeling of snap bean based on hyperspectral sensing: a greenhouse study

Farmers and growers typically use approaches based on the crop environment and local meteorology, many of which are labor-intensive, to predict crop yield. These approaches have found broad acceptance but lack real-time and physiological feedback for near-daily management purposes. This is true for broad-acre crops, such as snap bean, which is valued at hundreds of millions of dollars in the annual agricultural market. We aim to investigate the relationships between snap bean yield and plant spectral and biophysical information, collected using a hyperspectral spectroradiometer (400 to 2500 nm). The experiment focused on 48 single snap bean plants (cv. Huntington) in a controlled greenhouse environment during the growth period (69 days). We used applicable accuracy and precision metrics from partial least squares regression and cross-validation methods to evaluate the predictive ability of two harvest stages, namely an early-harvest and late-harvest stage, against our yield indicator (bean pod weight). Four different spectral data sets were used to investigate whether such oversampled, hyperspectral data sets could accurately and precisely model observed variability in yield, in terms of the coefficient of determination (R2) and root-mean-square error (RMSE). The objective of our approach hinges on the philosophy that selected spectral bands from this study, i.e., those that best explain yield variability, can be downsampled from a hyperspectral system for use in a more cost-effective, operational multispectral sensor. Our results suggested the optimal period for spectral evaluation of snap bean yield is 20 to 25 or 32 days prior to harvest for the early- and late-harvest stages, respectively, with the best model performing at a low RMSE (3.02 g plant − 1) and a high coefficient of determination (R2 = 0.72). An unmanned aerial systems-mounted, affordable, and wavelength-programmable multispectral imager, with bands corresponding to those identified, could provide a near real-time and reliable yield estimate prior to harvest.

DEVELOPMENT OF A METHOD FOR SYNTHESIZING AN APERTURE DIAPHRAGM IN HYPERSPECTRAL REMOTE SENSING SYSTEMS FOR EARTH

The aim of the work is to research and develop methods for synthesizing aperture in hyperspectral systems for remote sensing of the Earth to reduce weight and size characteristics by applying methods of program-algorithmic processing of the input signal and implementing the synthesized aperture. A method of neural networks for deconvolution on the construction of a radial basis network is developed. A method has been developed to increase the synthesis of apertures in hyperspectral systems for remote sensing of the Earth. A method for increasing the spatial resolution of images obtained by optical systems for remote sensing of the Earth is described. A method for radiometric calibration of output data has been developed, which allows universalizing the analysis of spectral characteristics. In the process, to achieve the goals were used: methods of spectral optics, mathematical analysis and statistics, methods of processing images and signals. The project results contribute to the reduction of overall weight and cost characteristics and the possibility of synthesizing the aperture at the exit of the polychromator, which will avoid the use of expensive camera lenses in hyperspectral systems of remote sensing of the Earth. The developed methods for synthesizing aperture in hyperspectral systems of remote sensing of the Earth differ from the existing ones in that the receiving device for the video signal does not contain structural changes, and they contain the function of the algorithmic apparatus, which includes the analysis of the functions of the scattering point, the deconvolution of the recorded signal is performed by the method of neural networks after the stage learning.

Nondestructive detection for egg freshness grade based on hyperspectral imaging technology

AbstractIn order to identify the freshness grade of eggs nondestructively and rapidly, hyperspectral imaging technology was used in this article. The hyperspectral data of 200 samples of three freshness grades was acquired by using hyperspectral image acquisition system (400.68–1,001.612 nm), and then the freshness grade of egg samples was measured by stoichiometry. First, Mahalanobis distance algorithm was used to remove abnormal sample data. Second, savitzky–golay and wavelet threshold denoising combined with standard normalized variable were used to pretreat the spectral data, respectively. Third, iteratively retains informative variables (IRIV), variable iterative space shrinkage approach, and competitive adaptive reweighted sampling were used for feature wavelength selection. Since the classification accuracy of support vector machine (SVM) model was affected by the selection of parameters, genetic algorithm (GA) was introduced to search the optimal parameters in SVM and compared with grid search algorithm. Finally, the result indicated that the classification accuracy of training set and test set of the optimal classification model (IRIV‐GA‐SVM) reached 99.29% and 97.87%, respectively. Thus, it is feasible to use hyperspectral image technology to detect egg freshness grade.Practical ApplicationsThe freshness grade is one of the most important indexes to measure the quality of eggs. Traditional methods of detecting egg freshness grade are time‐consuming and destructive, which cannot meet the test needs of modern agriculture. Hyperspectral imaging, an emerging technology, can provide both spectral and spatial information simultaneously, and has the advantages of nondestructive, fast and nonpollution. The result indicated that hyperspectral imaging technology for the detection of freshness grade of eggs is feasible.

Hyperspectral Imaging System with Rotation Platform for Investigation of Jujube Skin Defects

A novel object rotation hyperspectral imaging system with the wavelength range of 468–950 nm for investigating round-shaped fruits was developed. This system was used to obtain the reflection spectra of jujubes for the application of surface defect detection. Compared to the traditional linear scan system, which can scan about 49% of jujube surface in one scan pass, this novel object rotation scan system can scan 95% of jujube surface in one scan pass. Six types of jujube skin condition, including rusty spots, decay, white fungus, black fungus, cracks, and glare, were classified by using hyperspectral data. Support vector machine (SVM) and artificial neural network (ANN) models were used to differentiate the six jujube skin conditions. Classification effectiveness of models was evaluated based on confusion matrices. The percentage of classification accuracy of SVM and ANN models were 97.3% and 97.4%, respectively. The object rotation scan method developed for this study could be used for other round-shaped fruits and integrated into online hyperspectral investigation systems.

Editorial for the Special Issue “Forestry Applications of Unmanned Aerial Vehicles (UAVs)”

Unmanned aerial vehicles (UAVs) are new platforms that have been increasingly used in the last few years for forestry applications that benefit from the added value of flexibility, low cost, reliability, autonomy, and capability of timely provision of high-resolution data. This special issue (SI) collects nine papers reporting research on different forestry applications using UAV imagery. The special issue covers seven Red-Green-Blue (RGB) sensor papers, three papers on multispectral imagery, and one further paper on hyperspectral data acquisition system. Several data processing and machine learning methods are presented. The special issue provides an overview regarding potential applications to provide forestry characteristics in a timely, cost-efficient way. With the fast development of sensors technology and image processing algorithms, the forestry potential applications will growing fast, but future work should consider the consistency and repeatability of these novel techniques.

Combining pixel selection with covariance similarity approach in hyperspectral face recognition based on convolution neural network

A Hyperspectral camera provides discriminating features for capturing human faces that cannot be obtained by any other imaging technique. Nevertheless, it has new issues comprising curse of dimensionality, physical parameter retrieval, fast computing and inter band misalignment. As a result of, the literature of Hyperspectral Face Recognition is more scanty and confined to improvised dimensionality reduction and minimization of wide-ranging bands, and thus the objective can be obtained by the use of the Convolution Neural Network (ConvNet). Since ConvNet is of great attention in recent times, it can offer outstanding performance in face recognition systems, where the quantity of training data is amply large. We propose a Hyperspectral Face Recognition system using Firefly algorithm for band fusion and the Convolution Neural Network for classification. In addition to this, the present work is extended 11 exiting face recognition methods to perform Hyperspectral Face Recognition task. Thus the work has been framed as Hyperspectral Face Recognition problem to an image-set classification problem and assessment of the performance has been done on six state-of-the-art image-set problem techniques, and similarly it was examined on five state-of-the-art RGB and gray scale face recognition system, subsequently applied improved Firefly band selection algorithm on Hyperspectral Images to get appropriate band. Assessment with the eleven extended and five existing HSI Face Recognition system on two benchmark datasets (CMU-HSFD & UWA-HSFD) demonstrates that the proposed system overtakes all by a noteworthy margin. Lastly, we execute the band selection demonstration to get the novelty for most informative bands in Visible Near Infrared (VNIR).

Evaluating uptake of multiple fluorescent contrast agents in brain tumors simultaneously using whole animal multi-spectral cryo-imaging.

MRI images of gadolinium-based contrast agents (GBCA's) acquired before surgery are often registered to patients and used to guide surgical resection of intracranial tumors. Yet, the accuracy of these MR images in describing the surgical field degrades as surgery progresses; a well-recognized problem which has prompted efforts to develop new techniques that provide updated guidance information on residual tumor location. These efforts span a wide array of technologies, including image updating with deformation models, intraoperative MRI, and fluorescence guided surgery, among others. However, introduction of a straightforward technique that provides surgeons with a current view of GBCA distribution in real time remains an important goal. In this context, development of a fluorescent agent that recapitulates the kinetic behavior of GBCA's could provide familiar information directly in the surgical field in real time. To advance this strategy, we have begun identifying fluorescent contrast agents that show similar kinetic behavior to GBCA's. Using a novel hyperspectral whole body cryo-imaging system, we acquired high-resolution 3-D volumes of the distribution of multiple candidate fluorophores in whole heads bearing orthotopic brain tumors. Preliminary results reveal significant differences in the distribution of candidate optical agents, some of which show strong similarity to the GBCA uptake. Identification and eventual translation of a reliable GBCA-optical analog could improve and simplify surgical resection of brain tumors.

Hyperspectral Microscopic Imaging for Automatic Detection of Head and Neck Squamous Cell Carcinoma Using Histologic Image and Machine Learning.

The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system. The proposed nuclei segmentation method based on principle component analysis (PCA) can extract most nuclei in the hyperspectral image without extracting other sub-cellular components. Both spectra-based support vector machine (SVM) and patch-based convolutional neural network (CNN) are used for nuclei classification. CNNs were trained with both hyperspectral images and pseudo RGB images of extracted nuclei, in order to evaluate the usefulness of extra information provided by hyperspectral imaging. The average accuracy of spectra-based SVM classification is 68%. The average AUC and average accuracy of the HSI patch-based CNN classification is 0.94 and 82.4%, respectively. The hyperspectral microscopic imaging and classification methods provide an automatic tool to aid pathologists in detecting SCC on histologic slides.

Editorial for the Special Issue “Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”

High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants’ phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.

Optical simulations for determining efficacy of new light source designs for excitation-scanning high-speed hyperspectral imaging systems.

Positive outcomes for colorectal cancer treatment have been linked to early detection. The difficulty in detecting early lesions is the limited contrast with surrounding mucosa and minimal definitive markers to distinguish between hyperplastic and carcinoma lesions. Colorectal cancer is the 3rd leading cancer for incidence and mortality rates which is potentially linked to missed early lesions which allow for increased growth and metastatic potential. One potential technology for early-stage lesion detection is hyperspectral imaging. Traditionally, hyperspectral imaging uses reflectance spectroscopic data to provide a component analysis, per pixel, of an image in fields such as remote sensing, agriculture, food processing and archaeology. This work aims to acquire higher signal-to-noise fluorescence spectroscopic data, harnessing the autofluorescence of tissue, adding a hyperspectral contrast to colorectal cancer detection while maintaining spatial resolution at video-rate speeds. We have previously designed a multi-furcated LED-based spectral light source to prove this concept. Our results demonstrated that the technique is feasible, but the initial prototype has a high light transmission loss (~98%) minimizing spatial resolution and slowing video acquisition. Here, we present updated results in developing an optical ray-tracing model of light source geometries to maximize irradiance throughput for excitation-scanning hyperspectral imaging. Results show combining solid light guide branches have a compounding light loss effect, however, there is potential to minimize light loss through the use of optical claddings. This simulation data will provide the necessary metrics to verify and validate future physical optical components within the hyperspectral endoscopic system for detecting colorectal cancer.