Gravity-Dependent Modulation of Downbeat Nystagmus and Subjective Visual Vertical in the Roll Plane.

Downbeat nystagmus (DBN) is the most common form of acquired central vestibular nystagmus. Gravity perception in patients with DBN has previously been investigated by means of subjective visual straight ahead (SVA) and subjective visual vertical (SVV) in the pitch and roll planes only during whole-body tilts. To our knowledge, the effect of head tilt in the roll plane on the SVV and on DBN has not yet been systematically studied in patients. In this study, we investigated static and dynamic graviceptive function in the roll-plane in patients with DBN (patients) and healthy-controls (controls) by assessment of the Subjective Visual Vertical (SVV) and the modulation of slow-phase-velocity (SPV) of DBN. SPV of DBN and SVV were tested at different head-on trunk-tilt positions in the roll-plane (0°,30° clockwise (cw) and 30° counterclockwise (ccw)) in 26 patients suffering from DBN and 13 controls. In patients, SPV of DBN did not show significant modulations at different head-tilt angles in the roll-plane. SVV ratings did not differ significantly between DBN patients vs. controls, however patients with DBN exhibited a higher variability in mean SVV estimates than controls. Our results show that the DBN does not exhibit any modulation in the roll-plane, in contrast to the pitch-plane. Furthermore, patients with DBN show a higher uncertainty in the perception of verticality in the roll-plane in form of a higher variability of responses.

Association of paramagnetic rim lesions and retinal layer thickness in patients with multiple sclerosis.

BACKGROUND: Paramagnetic rim lesions (PRLs) are chronic active lesions associated with a more severe disease course in multiple sclerosis (MS). Retinal layer thinning measured by optical coherence tomography (OCT) is a biomarker of neuroaxonal damage associated with disability progression in MS. OBJECTIVE: We aimed to determine a potential association between OCT parameters (peripapillary retinal nerve fiber layer (pRNFL) ganglion cell-inner plexiform layer (GCIPL), inner nuclear layer (INL) thickness), and PRLs in patients with MS (pwMS). METHODS: In this cross-sectional retrospective study, we included pwMS with both 3T brain MRI and an OCT scan. Regression models were calculated with OCT parameters (pRNFL, GCIPL, INL) as dependent variables, and the number of PRLs as an independent variable adjusted for covariates. RESULTS: We analyzed data from 107 pwMS (mean age 34.7 years (SD 10.9), 64.5% female, median disease duration 6 years (IQR 1-13), median EDSS 1.5 (range 0-6.5)). Higher number of PRLs was associated with lower pRNFL (ß = -0.18; 95% CI -0.98, -0.03; p = 0.038) and GCIPL thickness (ß = -0.21; 95% CI -0.58, -0.02; p = 0.039). CONCLUSION: The association between higher number of PRLs and lower pRNFL and GCIPL thicknesses provides additional evidence that pwMS with PRLs are affected by a more pronounced neurodegenerative process.

Paramagnetic rim lesions lead to pronounced diffuse periplaque white matter damage in multiple sclerosis.

BACKGROUND: Paramagnetic rim lesions (PRLs) are an imaging biomarker in multiple sclerosis (MS), associated with a more severe disease. OBJECTIVES: To determine quantitative magnetic resonance imaging (MRI) metrics of PRLs, lesions with diffuse susceptibility-weighted imaging (SWI)-hypointense signal (DSHLs) and SWI-isointense lesions (SILs), their surrounding periplaque area (PPA) and the normal-appearing white matter (NAWM). METHODS: In a cross-sectional study, quantitative MRI metrics were measured in people with multiple sclerosis (pwMS) using the multi-dynamic multi-echo (MDME) sequence post-processing software "SyMRI." RESULTS: In 30 pwMS, 59 PRLs, 74 DSHLs, and 107 SILs were identified. Beside longer T1 relaxation times of PRLs compared to DSHLs and SILs (2030.5 (1519-2540) vs 1615.8 (1403.3-1953.5) vs 1199.5 (1089.6-1334.6), both p < 0.001), longer T1 relaxation times were observed in the PRL PPA compared to the SIL PPA and the NAWM but not the DSHL PPA. Patients with secondary progressive multiple sclerosis (SPMS) had longer T1 relaxation times in PRLs compared to patients with late relapsing multiple sclerosis (lRMS) (2394.5 (2030.5-3040) vs 1869.3 (1491.4-2451.3), p = 0.015) and also in the PRL PPA compared to patients with early relapsing multiple sclerosis (eRMS) (982 (927-1093.5) vs 904.3 (793.3-958.5), p = 0.013). CONCLUSION: PRLs are more destructive than SILs, leading to diffuse periplaque white matter (WM) damage. The quantitative MRI-based evaluation of the PRL PPA could be a marker for silent progression in pwMS.

Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites.

Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning.

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (AF), area of healthy foliage (AH), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Long-term evolution of multiple sclerosis iron rim lesions in 7 T MRI.

Recent data suggest that multiple sclerosis white matter lesions surrounded by a rim of iron containing microglia, termed iron rim lesions, signify patients with more severe disease course and a propensity to develop progressive multiple sclerosis. So far, however, little is known regarding the dynamics of iron rim lesions over long-time follow-up. In a prospective longitudinal cohort study in 33 patients (17 females; 30 relapsing-remitting, three secondary progressive multiple sclerosis; median age 36.6 years (18.6-62.6), we characterized the evolution of iron rim lesions by MRI at 7 T with annual scanning. The longest follow-up was 7 years in a subgroup of eight patients. Median and mean observation period were 1 (0-7) and 2.9 (±2.6) years, respectively. Images were acquired using a fluid-attenuated inversion recovery sequence fused with iron-sensitive MRI phase data, termed FLAIR-SWI, as well as a magnetization prepared two rapid acquisition gradient echoes, termed MP2RAGE. Volumes and T1 relaxation times of lesions with and without iron rims were assessed by manual segmentation. The pathological substrates of periplaque signal changes outside the iron rims were corroborated by targeted histological analysis on 17 post-mortem cases (10 females; two relapsing-remitting, 13 secondary progressive and two primary progressive multiple sclerosis; median age 66 years (34-88), four of them with available post-mortem 7 T MRI data. We observed 16 nascent iron rim lesions, which mainly formed in relapsing-remitting multiple sclerosis. Iron rim lesion fraction was significantly higher in relapsing-remitting than progressive disease (17.8 versus 7.2%; P < 0.001). In secondary progressive multiple sclerosis only, iron rim lesions showed significantly different volume dynamics (P < 0.034) compared with non-rim lesions, which significantly shrank with time in both relapsing-remitting (P < 0.001) and secondary progressive multiple sclerosis (P < 0.004). The iron rims themselves gradually diminished with time (P < 0.008). Compared with relapsing-remitting multiple sclerosis, iron rim lesions in secondary progressive multiple sclerosis were significantly more destructive than non-iron rim lesions (P < 0.001), reflected by prolonged lesional T1 relaxation times and by progressively increasing changes ascribed to secondary axonal degeneration in the periplaque white matter. Our study for the first time shows that chronic active lesions in multiple sclerosis patients evolve over many years after their initial formation. The dynamics of iron rim lesions thus provide one explanation for progressive brain damage and disability accrual in patients. Their systematic recording might become useful as a tool for predicting disease progression and monitoring treatment in progressive multiple sclerosis.

Treatment of neuromyelitis optica spectrum disorder: revisiting the complement system and other aspects of pathogenesis.

Neuromyelitis optica spectrum disorder (NMOSD) represents a rare neuroimmunological disease causing recurrent attacks and accumulation of permanent disability in affected patients. The discovery of the pathogenic IgG­1 antibody targeting a water channel expressed in astrocytes, aquaporin 4, constitutes a milestone achievement. Subsequently, multiple pathophysiological aspects of this distinct disease entity have been investigated. Demyelinating lesions and axonal damage ensue from autoantibodies targeting an astroglial epitope. This conundrum has been addressed in the current disease model, where activation of the complement system as well as B cells and interleukin 6 (IL-6) emerged as key contributors. It is the aim of this review to address these factors in light of novel treatment compounds which reflect these pathophysiological concepts in aiming for attack prevention, thus reducing disease burden in patients with NMOSD.

Sexuality and Multiple Sclerosis: Patient and Doctor Perspectives.

BACKGROUND: Little is known on how to address sexuality in clinical care for patients with multiple sclerosis (pwMS). AIM: To describe and contrast the perception of sexuality and associated aspects of communication in pwMS and their treating neurologists ("MSologists") and provide a standard of care. METHODS: Patients were surveyed using a 13-item questionnaire investigating perception on their own sexuality and opinions on communication about sexuality in context with MS. Certified MSologists in Austria received an 18-item survey regarding their approach to taking a sexual history of their patients. OUTCOMES: We report the frequency of answers given in this survey and propose a possible standard of care how sexuality could be addressed in clinical routine. RESULTS: Ninety-three pwMS (mean age 39 ± 11 years, 57% female) and 75 MSologists (mean age 43 ± 9 years, 63% male) completed this survey. Seventy-six percent of patients report their own sexuality as being (very) important to them and 95% think that sexual dysfunction would influence their quality of life. 84% would like to be asked about their sexuality by their MSologist. In contrast, only 15% of MSologists reported discussing sexuality with every patient. The most common reason for not doing so was a fear of crossing personal borders (34%). There is a strong desire for further medical education on this subject (76%). CLINICAL IMPLICATIONS: Discussing sexuality is important to pwMS and MSologists should consider their patients' wishes and needs to talk about it. STRENGTHS & LIMITATIONS: This is the largest survey contrasting the views of patients and their treating physicians on the topic of communication about sexuality. The use of an empirical unvalidated questionnaire may have introduced bias. Moreover, patients that are open to talk about their sexuality may be potentially overrepresented in this study. CONCLUSION: MSologists should offer their patients an open opportunity and appropriate framework to discuss their sexuality during a consultation. Altmann P, Leithner K, Leutmezer F, et al. Sexuality and Multiple Sclerosis: Patient and Doctor Perspectives. J Sex Med 2021;18:743-749.

A Hyperspectral Library of Foliar Diseases of Wheat.

This work established a hyperspectral library of important foliar diseases of wheat induced by different fungal pathogens, representing a time series from infection to symptom appearance for the purpose of detecting spectral changes. The data were generated under controlled conditions at the leaf scale. The transition from healthy to diseased leaf tissue was assessed, and spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that indicate a specific developmental stage during pathogenesis, defined as turning points, were combined into a spectral library. Machine learning analysis methods were applied and compared to test the potential of this library to detect and quantify foliar diseases in hyperspectral images. All evaluated classifiers had high accuracy (≤99%) for the detection and identification of both biotrophic and necrotrophic fungi. The potential of applying spectral analysis methods in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques for plant diseases under field conditions.

Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets.

BACKGROUND: The efficient and robust statistical analysis of the shape of plant organs of different cultivars is an important investigation issue in plant breeding and enables a robust cultivar description within the breeding progress. Laserscanning is a highly accurate and high resolution technique to acquire the 3D shape of plant surfaces. The computation of a shape based principal component analysis (PCA) built on concepts from continuum mechanics has proven to be an effective tool for a qualitative and quantitative shape examination. RESULTS: The shape based PCA was used for a statistical analysis of 140 sugar beet roots of different cultivars. The calculation of the mean sugar beet root shape and the description of the main variations was possible. Furthermore, unknown and individual tap roots could be attributed to their cultivar by means of a robust classification tool based on the PCA results. CONCLUSION: The method demonstrates that it is possible to identify principal modes of root shape variations automatically and to quantify associated variances out of laserscanned 3D sugar beet tap root models. The introduced approach is not limited to the 3D shape description by laser scanning. A transfer to 3D MRI or radar data is also conceivable.

Increased serum neurofilament light chain concentration indicates poor outcome in Guillain-Barré syndrome.

BACKGROUND: Guillain-Barré syndrome (GBS) is an autoimmune disease that results in demyelination and axonal damage. Five percent of patients die and 20% remain significantly disabled on recovery. Recovery is slow in most cases and eventual disability is difficult to predict, especially early in the disease. Blood or cerebrospinal fluid (CSF) biomarkers that could help identify patients at risk of poor outcome are required. We measured serum neurofilament light chain (sNfL) concentrations from blood taken upon admission and investigated a correlation between sNfL and clinical outcome. METHODS: Baseline sNfL levels in 27 GBS patients were compared with a control group of 22 patients with diagnoses not suggestive of any axonal damage. Clinical outcome parameters for GBS patients included (i) the Hughes Functional Score (HFS) at admission, nadir, and discharge; (ii) the number of days hospitalised; and (iii) whether intensive care was necessary. RESULTS: The median sNfL concentration in our GBS sample on admission was 85.5 pg/ml versus 9.1 pg/ml in controls. A twofold increase in sNfL concentration at baseline was associated with an HFS increase of 0.6 at nadir and reduced the likelihood of discharge with favourable outcome by a factor of almost three. Higher sNfL levels upon admission correlated well with hospitalisation time (rs = 0.69, p < 0.0001), during which transfer to intensive care occurred more frequently at an odds ratio of 2.4. Patients with baseline sNfL levels below 85.5 pg/ml had a 93% chance of being discharged with an unimpaired walking ability. CONCLUSIONS: sNfL levels measured at hospital admission correlated with clinical outcome in GBS patients. These results represent amounts of acute axonal damage and reflect mechanisms resulting in disability in GBS. Thus, sNfL may serve as a convenient blood-borne biomarker to personalise patient care by identifying those at higher risk of poor outcome.

Symptomatology and symptomatic treatment in multiple sclerosis: Results from a nationwide MS registry.

BACKGROUND: Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease. Over time, symptoms accumulate leading to increased disability of patients. OBJECTIVE: The objective of this article is to analyze the prevalence of symptoms and symptomatic treatment patterns in a nationwide MS registry. METHODS: Data sets from 35,755 patients were analyzed. RESULTS: More than two-thirds of patients were women with a mean age of 46.1 (±12.8) years. Median Expanded Disability Status Score (EDSS) was 3.0. The most frequently reported symptoms were fatigue, spasticity, and voiding disorders. In patients with short disease duration, fatigue was reported most frequently. Symptomatic treatment was most common for spasticity and depression, whereas fatigue was treated only in a third of affected patients. Almost a fifth of patients with EDSS ⩽ 3.5 and neuropsychological symptoms had retired from work. CONCLUSION: Whereas treatment for spasticity and depression is common in our cohort, sexual dysfunction, dysphagia, cognitive dysfunction, and fatigue are treated to a far lesser extent. The need for psychological support, physical, and occupational therapy has to be recognized as neuropsychological symptoms have a great impact on retirement at an early stage. Overall symptomatic treatment rates for the most common symptoms have increased over the last years (p < 0.001).

Do elevated autoantibodies in patients with multiple sclerosis matter?

OBJECTIVES: The incidence and clinical impact of serum autoantibodies in patients with multiple sclerosis (MS) are controversially discussed. The aim of the study was to reassess the value of elevated serum autoantibodies in our MS study cohort. MATERIAL & METHODS: In total, 176 MS patients were retrospectively analyzed for coexistence and clinical impact of increased serum autoantibody levels. RESULTS: The 18.8% of the MS cohort showed elevated serum autoantibody levels, but only 10.2% of all MS patients were diagnosed with a further autoimmune disease (AI). Patients with elevated serum autoantibodies (AABS) were not significantly more often diagnosed with a clinical manifest AI as compared to patients with negative autoantibodies (P = 0.338). MS patients with disease duration of more than 10 years showed no significant increase of positive autoantibodies as compared to patients with a more recent disease onset (P = 1). MS patients with elevated serum autoantibodies did not exhibit a significantly worse disease course (P = 0.428). CONCLUSIONS: According to our data, elevated serum autoantibodies do not have the potential to serve as a prognostic tool for disease severity in patients with MS Since MS patients with positive serum AABS did not significantly more often suffer from clinical manifest AIs than MS patients with negative serum AABS, the role of routine testing of serum AABS in MS patients should be critically called into question.

Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.

BACKGROUND: Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation. RESULTS: The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture. CONCLUSION: An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated - even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level.

Accessing a plant's 3D geometry has become of significant importance for phenotyping during the last few years. Close-up laser scanning is an established method to acquire 3D plant shapes in real time with high detail, but it is stationary and has high investment costs. 3D reconstruction from images using structure from motion (SfM) and multi-view stereo (MVS) is a flexible cost-effective method, but requires post-processing procedures. The aim of this study is to evaluate the potential measuring accuracy of an SfM- and MVS-based photogrammetric method for the task of organ-level plant phenotyping. For this, reference data are provided by a high-accuracy close-up laser scanner. Using both methods, point clouds of several tomato plants were reconstructed at six following days. The parameters leaf area, main stem height and convex hull of the complete plant were extracted from the 3D point clouds and compared to the reference data regarding accuracy and correlation. These parameters were chosen regarding the demands of current phenotyping scenarios. The study shows that the photogrammetric approach is highly suitable for the presented monitoring scenario, yielding high correlations to the reference measurements. This cost-effective 3D reconstruction method depicts an alternative to an expensive laser scanner in the studied scenarios with potential for automated procedures.

Limits of active laser triangulation as an instrument for high precision plant imaging.

Laser scanning is a non-invasive method for collecting and parameterizing 3D data of well reflecting objects. These systems have been used for 3D imaging of plant growth and structure analysis. A prerequisite is that the recorded signals originate from the true plant surface. In this paper we studied the effects of species, leaf chlorophyll content and sensor settings on the suitability and accuracy of a commercial 660 nm active laser triangulation scanning device. We found that surface images of Ficus benjamina leaves were inaccurate at low chlorophyll concentrations and a long sensor exposure time. Imaging of the rough waxy leaf surface of leek (Allium porrum) was possible using very low exposure times, whereas at higher exposure times penetration and multiple refraction prevented the correct imaging of the surface. A comparison of scans with varying exposure time enabled the target-oriented analysis to identify chlorotic, necrotic and healthy leaf areas or mildew infestations. We found plant properties and sensor settings to have a strong influence on the accuracy of measurements. These interactions have to be further elucidated before laser imaging of plants is possible with the high accuracy required for e.g., the observation of plant growth or reactions to water stress.

Automated analysis of barley organs using 3D laser scanning: an approach for high throughput phenotyping.

Due to the rise of laser scanning the 3D geometry of plant architecture is easy to acquire. Nevertheless, an automated interpretation and, finally, the segmentation into functional groups are still difficult to achieve. Two barley plants were scanned in a time course, and the organs were separated by applying a histogram-based classification algorithm. The leaf organs were represented by meshing algorithms, while the stem organs were parameterized by a least-squares cylinder approximation. We introduced surface feature histograms with an accuracy of 96% for the separation of the barley organs, leaf and stem. This enables growth monitoring in a time course for barley plants. Its reliability was demonstrated by a comparison with manually fitted parameters with a correlation R(2) = 0:99 for the leaf area and R(2) = 0:98 for the cumulated stem height. A proof of concept has been given for its applicability for the detection of water stress in barley, where the extension growth of an irrigated and a non-irrigated plant has been monitored.

A multi-resolution approach for an automated fusion of different low-cost 3D sensors.

The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation. The requirements on the measuring devices are versatile, because spacious scenes have to be imaged with a high level of detail for selected objects. Thus, the used measuring systems are expensive and require an experienced operator. With the rise of low-cost 3D imaging systems, their integration into the digital documentation process is possible. However, common low-cost sensors have the limitation of a trade-off between range and accuracy, providing either a low resolution of single objects or a limited imaging field. Therefore, the use of multiple sensors is desirable. We show the combined use of two low-cost sensors, the Microsoft Kinect and the David laserscanning system, to achieve low-resolved scans of the whole scene and a high level of detail for selected objects, respectively. Afterwards, the high-resolved David objects are automatically assigned to their corresponding Kinect object by the use of surface feature histograms and SVM-classification. The corresponding objects are fitted using an ICP-implementation to produce a multi-resolution map. The applicability is shown for a fictional crime scene and the reconstruction of a ballistic trajectory.

Low-cost 3D systems: suitable tools for plant phenotyping.

Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs. The aim of the present study is a comparison between two current 3D imaging low-cost systems and a high precision close-up laser scanner as a reference method. As low-cost systems, the David laser scanning system and the Microsoft Kinect Device were used. The 3D measuring accuracy of both low-cost sensors was estimated based on the deviations of test specimens. Parameters extracted from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears were evaluated. These parameters are compared regarding accuracy and correlation to reference measurements. The evaluation scenarios were chosen with respect to recorded plant parameters in current phenotyping projects. In the present study, low-cost 3D imaging devices have been shown to be highly reliable for the demands of plant phenotyping, with the potential to be implemented in automated application procedures, while saving acquisition costs. Our study confirms that a carefully selected low-cost sensor.