Non-Invasive Measurement of Frog Skin Reflectivity in High Spatial Resolution Using a Dual Hyperspectral Approach

Background:Most spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range.Methodology/Principal Findings:We briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum.Conclusion/Significance:Hyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult. © 2013 Pinto et al

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Foliar spectra accurately distinguish the invasive common reed from co-occurring plant species throughout a growing season

Les espèces végétales envahissantes sont l'un des principaux facteurs de changement de la biodiversité dans les écosystèmes terrestres. Une détection précise et précoce des espèces exotiques est donc cruciale pour surveiller les invasions en cours et pour prévenir leur propagation. Présentement, les méthodes de surveillance des invasions biologiques permettent de suivre la propagation des envahisseurs à travers les aires de répartition géographique, mais une attention moindre a été accordée à la surveillance des espèces envahissantes à travers le temps. Les plates-formes de télédétection, capables de fournir des informations détaillées sur les variations des traits foliaires dans le temps et l'espace, sont particulièrement bien placées pour surveiller les plantes envahissantes en temps réel. Les changements temporels des traits fonctionnels sont exprimés dans la signature spectrale des espèces par des caractéristiques d'absorption spécifiques de la lumière associés aux pigments photosynthétiques et aux constituants chimiques tous deux liés à la phénologie. Ainsi, les variations temporelles dans la réponse spectrale des plantes peuvent être utilisées afin de mieux identifier des espèces individuelles. L'un des envahisseurs les plus problématiques au Canada est le roseau commun, Phragmites australis (Cav.) Trin. ex Steudel sous-espèce australis, dont la propagation menace la biodiversité des écosystèmes de zones humides en Amérique du Nord. Déterminer la période de l'année où cet envahisseur se distingue d’avantage, du point de vue spectral et fonctionnel, des autres plantes de la communauté serait centrale dans une meilleure gestion du roseau commun. Pour ce faire, nous avons utilisé des traits fonctionnels et une série temporelle de données spectrales foliaires à haute résolution au cours d'une saison de croissance à Boucherville, Québec, Canada, afin de déterminer la séparabilité spectrale de l'envahisseur par rapport aux espèces co-occurrentes et comment cette dernière varie à travers le temps. Nos résultats ont révélé que la spectroscopie foliaire a permis de distinguer le phragmite des espèces co-occurrentes avec une précision de plus de 95% tout au long de la saison de croissance – un résultat prometteur pour le futur de la télédétection des espèces végétales envahissantes.Invasive plant species are one of the main drivers of biodiversity change in terrestrial ecosystems. Accurate detection of exotic species is critical to monitor on-going invasions and early detection of incipient invasions is necessary to prevent further spread. At present, surveillance methods of biological invasions allow to track the spread of invaders across geographic ranges, but less attention has been given to invasive species monitoring across time. Remote sensing platforms, capable of providing detailed information on foliar trait variations across time and space, are uniquely positioned for monitoring invasive plants in real time. Temporal changes in foliar traits are expressed in a species spectral profile through specific absorption features related to variation in photosynthetic pigments and chemical constituents driven by phenology. Thus, variations in a plant’s spectral response can be used to improve the identification of individual species. One of Canada’s most problematic invaders is the common reed, Phragmites australis (Cav.) Trin. ex Steudel subspecies australis, whose spread threatens biodiversity in wetland ecosystems in North America. Determining the time of year when the invader is spectrally and functionally more distinct from other plants in the community would be central to better management of common reed. To do so, we collected a time-series of foliar traits and high-resolution leaf spectral data over the course of a growing season at Boucherville, Quebec, Canada, to determine the spectral separability of the invader from co-occurring species and how its detection varies over time. Our results revealed that leaf-level spectroscopy distinguished Phragmites and co-occurring species with > 95% accuracy throughout the growing season – a promising result for the future remote detection of invasive plant species

Optical Micromanipulation Techniques Combined with Microspectroscopic Methods

Předložená dizertační práce se zabývá kombinací optických mikromanipulací s mikrospektroskopickými metodami. Využili jsme laserovou pinzetu pro transport a třídění živých mikroorganismů, například jednobuněčných řas, či kvasinek. Ramanovskou spektroskopií jsme analyzovali chemické složení jednotlivých buněk a tyto informace jsme využili k automatické selekci buněk s vybranými vlastnostmi. Zkombinovali jsme pulsní amplitudově modulovanou fluorescenční mikrospektroskopii, optické mikromanipulace a jiné techniky ke zmapování stresové odpovědi opticky zachycených buněk při různých časech působení, vlnových délkách a intenzitách chytacího laseru. Vyrobili jsme různé typy mikrofluidních čipů a zkonstruovali jsme Ramanovu pinzetu pro třídění mikro-objektů, především živých buněk, v mikrofluidním prostředí.The subject of the presented Ph.D. thesis is a combination of optical micromanipulation and microspectroscopic methods. We used laser tweezers to transport and sort various living microorganisms, such as microalgal or yeast cells. We employed Raman microspectroscopy to analyze chemical composition of individual cells and we used the information about chemical composition to automatically select the cells of interest. We combined pulsed amplitude modulation fluorescence microspectroscopy, optical micromanipulation and other techniques to map the stress response of cells to various laser wavelengths, intensities and durations of optical trapping. We fabricated microfluidic chips of various designs and we constructed Raman-tweezers sorter of micro-objects such as living cells on a microfluidic platform.

A novel method for detecting morphologically similar crops and weeds based on the combination of contour masks and filtered Local Binary Pattern operators

Background: Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops, making them difficult to discriminate. Results: We propose a novel method using a combination of filtered features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at 4 stages of growth were collected using a testbed system. Mask-based local binary pattern features were combined with filtered features and a coefficient k. The classification of crops and weeds was achieved using support vector machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with 4 classes in the bccr-segset dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method. Conclusions: The proposed method enhances the identification of crops and weeds with similar appearance and demonstrates its capabilities in real-time weed detection. © 2020 The Author(s) 2020

Fungal Assemblages Associated with Roots of Halophytic and Non-halophytic Plant Species Vary Differentially Along a Salinity Gradient.

Structure of fungal communities is known to be influenced by host plants and environmental conditions. However, in most cases, the dynamics of these variation patterns are poorly understood. In this work, we compared richness, diversity, and composition between assemblages of endophytic and rhizospheric fungi associated to roots of two plants with different lifestyles: the halophyte Inula crithmoides and the non-halophyte I. viscosa (syn. Dittrichia viscosa L.), along a spatially short salinity gradient. Roots and rhizospheric soil from these plants were collected at three points between a salt marsh and a sand dune, and fungi were isolated and characterized by ITS rDNA sequencing. Isolates were classified in a total of 90 operational taxonomic units (OTUs), belonging to 17 fungal orders within Ascomycota and Basidiomycota. Species composition of endophytic and soil communities significantly differed across samples. Endophyte communities of I. crithmoides and I. viscosa were only similar in the intermediate zone between the salt marsh and the dune, and while the latter displayed a single, generalist association of endophytes, I. crithmoides harbored different assemblages along the gradient, adapted to the specific soil conditions. In the lower salt marsh, root assemblages were strongly dominated by a single dark septate sterile fungus, also prevalent in other neighboring salt marshes. Interestingly, although its occurrence was positively correlated to soil salinity, in vitro assays revealed a strong inhibition of its growth by salts. Our results suggest that host lifestyle and soil characteristics have a strong effect on endophytic fungi and that environmental stress may entail tight plant-fungus relationships for adaptation to unfavorable conditions

Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

Recovery of Phytophthora Ramorum and Other Phytophthora Spp. in a Forest Adjacent to a Mississippi Ornamental Plant Nursery

The movement of the exotic and destructive plant pathogen Phytophthora ramorum into unquarantined areas via the plant nursery trade provides a potential outlet for transmission into eastern United States forests. A two-year survey of Phytophthora species in a forest adjacent to an ornamental plant nursery in Mississippi isolated P. ramorum 20 times from water and once from vegetation, with an additional detection of 14 Phytophthora species and one provisional species. Isolates were recovered from soil, water, and vegetation using baiting and filtering techniques, and verified by their DNA through Polymerase Chain Reaction (PCR) followed by genomic sequencing. This study confirms the ability of P. ramorum to sustain itself in Mississippi, although disease progression appears to be inhibited by the relatively small window of favorable environmental conditions