Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since it allows to discover non-destructively spectral characteristics of plants governed primarily by scattering and absorption characteristics of the leaf internal structure and biochemical constituents. Several drought stress indices have been derived using hyper-spectral imaging. However, they are typically based on few hyper-spectral images only, rely on interpretations of experts, and consider few wavelengths only. In this study, we present the first data-driven approach to discovering spectral drought stress indices, treating it as an unsupervised labeling problem at massive scale. To make use of short range dependencies of spectral wavelengths, we develop an online variational Bayes algorithm for latent Dirichlet allocation with convolved Dirichlet regularizer. This approach scales to massive datasets and, hence, provides a more objective complement to plant physiological practices. The spectral topics found conform to plant physiological knowledge and can be computed in a fraction of the time compared to existing LDA approaches.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012

A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved

In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral-temporal features

Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases whilst minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of >300 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding

Hyperspectral imaging of human skin aided by artificial neural networks

We developed a compact, hand-held hyperspectral imaging system for 2D neural network-based visualization of skin chromophores and blood oxygenation. State-of-the-art micro-optic multichannel matrix sensor combined with the tunable Fabry-Perot micro interferometer enables a portable diagnostic device sensitive to the changes of the oxygen saturation as well as the variations of blood volume fraction of human skin. Generalized object-oriented Monte Carlo model is used extensively for the training of an artificial neural network utilized for the hyperspectral image processing. In addition, the results are verified and validated via actual experiments with tissue phantoms and human skin in vivo. The proposed approach enables a tool combining both the speed of an artificial neural network processing and the accuracy and flexibility of advanced Monte Carlo modeling. Finally, the results of the feasibility studies and the experimental tests on biotissue phantoms and healthy volunteers are presented

Knochenaugmentation mittels periimplantärer Elevation des ortsständigen Periosts

1.1 Hintergrund und Ziele Die Periostelevation, nach dem dynamischen und statischen Prinzip durchgeführt, wur-de bereits als neues Verfahren der Knochenaugmentation beobachtet. Allerdings ergab sich hierbei in Voruntersuchungen der Nachteil, die Elevationsapparatur in einem Zweiteingriff wieder entfernen zu müssen. Dies stellte den Hauptgrund zur Erforschung einer neuen Vorgehensweise dar. In dieser tierexperimentellen Studie sollte untersucht werden, ob es zum einen durch die Elevation des ortsständigen Periostes mittels über-stehender Implantate und somit unter Verzicht der Elevationsapparatur möglich ist eine Knochenregeneration zu erzielen und, ob diese zum anderen durch ein resorbierbares Kunststoffkäppchen steuerbar ist. Als Vergleichsgruppe dienten überstehende Implanta-te, bei denen eine epiperiostale Insertion erfolgte. 1.2 Material und Methoden Aufgrund der Ähnlichkeit der Knochenneubildungsrate und Knochendefektheilung zwischen Menschen und Hausschweinen wurden Hausschweine als Versuchstiere für diese Studie gewählt. Den Opferungszeitpunkten von 20, 40 und 60 Tagen wurden jeweils sechs Tiere zugeordnet. Insgesamt wurden somit 12 Tiere mit jeweils vier im Os frontale der Tiere befindlichen Implantate bei den Versuchsgruppen und sechs Tiere mit jeweils zwei epiperiostal inserierten Implantaten bei den Kontrollgruppen geopfert. Demzufolge ergab sich eine Anzahl von 60 Proben, welche sowohl mikroradiographisch als auch histologisch untersucht wurden. 1.3 Ergebnisse Bei den Implantaten mit den Kunststoffkäppchen zeigte sich in allen drei Opferungs-zeitpunkten ein geringer Anteil an neugebildetem Knochen, wobei die höchsten Werte zum dritten Opferungszeitpunkt mit 21,27 (± 8,17)% erreicht wurden. Im Vergleich dazu erreichte die Gruppe der Periostelevation ohne Kunststoffkäppchen mit einem Anteil an neugebildetem Knochen von bis zu 76,11 (± 12)% die höchsten Werte zum zwei-ten Opferungszeitpunkt. Die Kontrollgruppe zeigte einen prozentualen Knochenzu-wachs von bis zu 85,38 (± 7,88)% zum dritten Opferungszeitpunkt. Bei der histologi-schen Untersuchung zeigte sich vor allem bei der Periostelevation eine Knochenregene-ration bis zur vollen Implantathöhe. Auffallend war, dass die Knochenneubildung bei der Gruppe mit den PDS-Käppchen vor allem seitlich der Käppchen zu erkennen war und sich unter diesen nur wenig Knochen neu gebildet hat. 1.4 Praktische Schlussfolgerungen Durch eine Elevation des Periostes mittels inserierter Implantate ist generell eine Kno-chenregeneration zu erzielen, da diese unter Umgehung der Nachteile bisher angewand-ter Möglichkeiten eine einfachere Methode zur Knochenaugmentation darstellt, welches von großem Vorteil für die Klinik sein könnte. Die Verwendung des Kunststoffkäppchens hat sich nicht bewährt, da sich unter diesem nicht viel Knochen gebildet hat. Auffallend sind die Ergebnisse bei der Kontrollgruppe, da man sich vorher aufgrund der epiperiostalen Implantatinsertion keine Knochenrege-neration erhofft hatte

Matrix Factorization as Search ⋆

Abstract. Simplex Volume Maximization (SiVM) exploits distance geometry for e ciently factorizing gigantic matrices. It was proven successful in game, social media, and plant mining. Here, we review the distance geometry approach and argue that it generally suggests to factorize gigantic matrices using search-based instead of optimization techniques. 1 Interpretable Matrix Factorization Many modern data sets are available in form of a real-valued m × n matrix V of rank r ≤ min(m, n). The columns v1,..., vn of such a data matrix encode information about n objects each of which is characterized by m features. Typical examples of objects include text documents, digital images, genomes, stocks, or social groups. Examples of corresponding features are measurements such as term frequency counts, intensity gradient magnitudes, or incidence relations among the nodes of a graph. In most modern settings, the dimensions of the data matrix are large so that it is useful to determine a compressed representation that may be easier to analyze and interpret in light of domain-speci c knowledge

Metro maps of plant disease dynamics-automated mining of differences using hyperspectral images

Understanding the response dynamics of plants to biotic stress is essential to improve management practices and breeding strategies of crops and thus to proceed towards a more sustainable agriculture in the coming decades. In this context, hyperspectral imaging offers a particularly promising approach since it provides non-destructive measurements of plants correlated with internal structure and biochemical compounds. In this paper, we present a cascade of data mining techniques for fast and reliable data-driven sketching of complex hyperspectral dynamics in plant science and plant phenotyping. To achieve this, we build on top of a recent linear time matrix factorization technique, called Simplex Volume Maximization, in order to automatically discover archetypal hyperspectral signatures that are characteristic for particular diseases. The methods were applied on a data set of barley leaves (Hordeum vulgare) diseased with foliar plant pathogens Pyrenophora teres, Puccinia hordei and Blumeria graminis hordei. Towards more intuitive visualizations of plant disease dynamics, we use the archetypal signatures to create structured summaries that are inspired by metro maps, i.e. schematic diagrams of public transport networks. Metro maps of plant disease dynamics produced on several real-world data sets conform to plant physiological knowledge and explicitly illustrate the interaction between diseases and plants. Most importantly, they provide an abstract and interpretable view on plant disease progression