Ing. Lukáš Krauz, Ph.D.

The assessment of plant health and quality is a critical aspect of plant biology, agriculture, and the food industry. With the global population continuously increasing, the demand for high-quality plant products is expected to surge. Consequently, there is a necessity for the development of automated systems that are user-friendly, accurate, affordable, and capable of rapid evaluation of plant health in a multitude of settings, including fields, farms, and laboratories. Such systems would apply to various applications, including investigating the impact of environmental conditions or developing new potential biostimulants and biopesticides. This paper introduces a novel, low-cost, and innovative multifunctional high-throughput plant phenotyping system that addresses an unmet need in the market. The system was designed to be affordable, scalable, applicable, and reliable to diverse customers. The key components of the system include a robotic arm AR4 (Annin Robotics, USA), a three-dimensional (3D) scanner POP 3 (Revopoint, USA), and a multispectral (MS) visible near-infrared (VNIR) camera FS 3200D 10GE (JAI Ltd., Japan). The paper describes these devices, their calibration, performance evaluation, and final applicability assessment. In particular, the motion characteristics of the AR4 are evaluated by a pose repeatability with obtained values of 0.273 mm and 0.682 mm without load and with load, respectively. Furthermore, it is shown that the calibrated camera provides comparable NDVI index data with the content of plant pigments R2 > 0.92 and also against the reference VNIR hyperspectral (HS) camera SPECIM PFD4K-65-V10E (R2 > 0.99). In addition, the presented 3D scanner demonstrated superior 3D models with a high degree of fit compared to the more expensive 3D scanners such as Shining 3D EinScan Pro 2X 2020 or Shining 3D EinScan-SP V2. The paper concludes with a discussion of the results, limitations, future improvements, and potential applications of the device in laboratory, educational, and field settings.

In recent years, the study of parallel or cluster meteor events has become increasingly popular. Many imaging systems currently focus on meteor detection, but the algorithms exploiting the data from such systems do not investigate the probability of cluster or parallel meteor events. This paper presents a novel approach to indicate a potential meteor cluster or parallel meteor event based on variable-length astronomical video sequences. The presented algorithm consists of two main parts: meteor event pre-detection and meteor cluster event probability evaluation. The first part of the algorithm involves a meteor pre-detection method based on the Hough transform (HT) and the exact event location within the time domain. In addition to pre-detecting meteor events, the method outputs event trajectory parameters that are further exploited in a second part of the algorithm. This subsequent part of the algorithm then operates over these meteor trajectory parameters and indicates the probability of cluster occurrence. The algorithm is experimentally evaluated on video sequences generated by the Meteor Automatic Imager and Analyzer (MAIA) astronomical imaging system, covering the Draconid and September ε Perseid (SPE) meteor showers. Compared to the current MAIA meteor detection software, the proposed part of the pre-detection algorithm shows promising results, especially the increased rate of correct meteor detection. The meteor cluster evaluation part of the algorithm then demonstrates its ability to successfully select related meteor event candidates (disintegrated from the same parental object) and reject unrelated ones.

Fine art photography, paper documents, and other parts of printing that aim to keep value are searching for credible techniques and mediums suitable for long-term archiving purposes. In general, long-lasting pigment-based inks are used for archival print creation. However, they are very often replaced or forged by dye-based inks, with lower fade resistance and, therefore, lower archiving potential. Frequently, the difference between the dye- and pigment-based prints is hard to uncover. Finding a simple tool for countrified identification is, therefore, necessary. This paper assesses the spectral characteristics of dye- and pigment-based ink prints using visible near-infrared (VNIR) hyperspectral imaging. The main aim is to show the spectral differences between these ink prints using a hyperspectral camera and subsequent hyperspectral image processing. Two diverse printers were exploited for comparison, a hobby dye-based EPSON L1800 and a professional pigment-based EPSON SC-P9500. The identical prints created via these printers on three different types of photo paper were recaptured by the hyperspectral camera. The acquired pixel values were studied in terms of spectral characteristics and principal component analysis (PCA). In addition, the obtained spectral differences were quantified by the selected spectral metrics. The possible usage for print forgery detection via VNIR hyperspectral imaging is discussed in the results.

The paper proposes a Wollaston-type crystal polarizer suitable for broadband operation within the visible spectral band up to the far infrared band based on unique optical materials, mercurous halides (Hg2X2). This paper introduces the general characteristics and optical properties of these birefringent tetragonal optical materials, as well as the general description of a Wollaston prism and the process of its parameter optimization. In general, the Wollaston polarizer is constructed from two combined wedge-shaped prisms. The key parameters that affect the properties of the Wollaston polarizer are then the cut angle of these two prisms and the refractive index of the exploited optical cement (immersion) that bonds the prisms together. The optimal prism cut angles and immersion refractive index are investigated to maximize the Wollaston parameters, such as the transmittance of the polarized radiation and the separation angle of the output orthogonally polarized beams. This process is significantly dependent on the characteristics of all selected mercurous halides (Hg2Cl2, Hg2Br2, Hg2I2). The optimal values of the prism cut angle for each material are selected based on the outlined results. In addition, the Wollaston prism behaviour regarding real radiation propagation is modelled in detail via the Zemax optical studio. The presented models aim to aid in the real design and fabrication of a broadband Wollaston polarizer based on mercurous halides.

The paper aims to show the advantages of the infrared-optimised quasi-collinear AOTF (acousto-optic tunable filter) for the spatio-spectral hyperspectral imaging system. The optimisation process is presented based on the selected tetragonal anisotropic materials with exceptional optical and acousto-optical properties in IR (infrared) spectral region. These materials are further compared in terms of their features and suitability for AOTF design. The spectral resolution is considered as the main optimising parameter. Resulting from the analysis, the mercurous chloride (Hg2Cl2) single crystal is selected as a representative of the mercurous halide family for the presentation of the quasi-collinear AOTF model operating in LWIR (long-wave infrared) spectral band. The overall parameters of the AOTF model such as spectral resolution, chromatic field of view, acoustic frequency, and operational power requirements are estimated and discussed in results.

All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images.