Quantifying plant colour and colour difference as perceived by humans using digital images

Herbaceous ornamental plants with conspicuous aesthetic traits contribute to plant invasion risk in subtropical urban parks

Global ornamental horticulture is a major pathway for plant invasions, while urban parks are key areas for introducing non-native ornamental plants. To react appropriately to the challenges (e.g., biological invasion issues) and opportunities (e.g., urban ecosystem services) of herbaceous ornamentals in urban parks, we conducted a comprehensive invasive risk assessment in 363 urban parks in Chongqing, a subtropical city in China. The results found more than 1/3 of the 119 non-native species recorded in urban parks had a high invasion risk, and more than five species had potential invasion risk in 96.29% of the study area, indicating herbaceous ornamentals in urban parks are potentially a pool of invasive species that deserves attention. Moreover, humans have chosen herbaceous ornamentals with more aesthetic characteristics in urban parks, where exotic species were more prominent than native species in floral traits, such as more conspicuous flowers and longer flowering periods. The findings can inform urban plant management, provide an integrated approach to assessing herbaceous ornamentals' invasion risk, and offer insights into understanding the filtering effects of human aesthetic preferences.

Free and open-source software for object detection, size, and colour determination for use in plant phenotyping

BACKGROUND

Object detection, size determination, and colour detection of images are tools commonly used in plant science. Key examples of this include identification of ripening stages of fruit such as tomatoes and the determination of chlorophyll content as an indicator of plant health. While methods exist for determining these important phenotypes, they often require proprietary software or require coding knowledge to adapt existing code.

RESULTS

We provide a set of free and open-source Python scripts that, without any adaptation, are able to perform background correction and colour correction on images using a ColourChecker chart. Further scripts identify objects, use an object of known size to calibrate for size, and extract the average colour of objects in RGB, Lab, and YUV colour spaces. We use two examples to demonstrate the use of these scripts. We show the consistency of these scripts by imaging in four different lighting conditions, and then we use two examples to show how the scripts can be used. In the first example, we estimate the lycopene content in tomatoes (Solanum lycopersicum) var. Tiny Tim using fruit images and an exponential model to predict lycopene content. We demonstrate that three different cameras (a DSLR camera and two separate mobile phones) are all able to model lycopene content. The models that predict lycopene or chlorophyll need to be adjusted depending on the camera used. In the second example, we estimate the chlorophyll content of basil (Ocimum basilicum) using leaf images and an exponential model to predict chlorophyll content.

CONCLUSION

A fast, cheap, non-destructive, and inexpensive method is provided for the determination of the size and colour of plant materials using a rig consisting of a lightbox, camera, and colour checker card and using free and open-source scripts that run in Python 3.8. This method accurately predicted the lycopene content in tomato fruit and the chlorophyll content in basil leaves.

Multispectral Plant Disease Detection with Vision Transformer-Convolutional Neural Network Hybrid Approaches

Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance accuracy. A multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths. Among the models employed, ViT-B16 notably achieved the highest test accuracy, precision, recall, and F1 score across all filters, with averages of 83.3%, 90.1%, 90.75%, and 89.5%, respectively. Furthermore, a comparative analysis highlights the pivotal role of balanced datasets in selecting the appropriate wavelength and deep learning model for robust disease identification. These findings promise to advance crop disease management in real-world agricultural applications and contribute to global food security. The study underscores the significance of machine learning in transforming plant disease diagnostics and encourages further research in this field.

A deep semantic vegetation health monitoring platform for citizen science imaging data

Automated monitoring of vegetation health in a landscape is often attributed to calculating values of various vegetation indexes over a period of time. However, such approaches suffer from an inaccurate estimation of vegetational change due to the over-reliance of index values on vegetation’s colour attributes and the availability of multi-spectral bands. One common observation is the sensitivity of colour attributes to seasonal variations and imaging devices, thus leading to false and inaccurate change detection and monitoring. In addition, these are very strong assumptions in a citizen science project. In this article, we build upon our previous work on developing a Semantic Vegetation Index (SVI) and expand it to introduce a semantic vegetation health monitoring platform to monitor vegetation health in a large landscape. However, unlike our previous work, we use RGB images of the Australian landscape for a quarterly series of images over six years (2015–2020). This Semantic Vegetation Index (SVI) is based on deep semantic segmentation to integrate it with a citizen science project (Fluker Post) for automated environmental monitoring. It has collected thousands of vegetation images shared by various visitors from around 168 different points located in Australian regions over six years. This paper first uses a deep learning-based semantic segmentation model to classify vegetation in repeated photographs. A semantic vegetation index is then calculated and plotted in a time series to reflect seasonal variations and environmental impacts. The results show variational trends of vegetation cover for each year, and the semantic segmentation model performed well in calculating vegetation cover based on semantic pixels (overall accuracy = 97.7%). This work has solved a number of problems related to changes in viewpoint, scale, zoom, and seasonal changes in order to normalise RGB image data collected from different image devices.

A field experiment characterizing variable detection rates during plant surveys

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.

Exploring the Integration Between Colour Theory and Biodiversity Values in the Design of Living Walls

Designing green infrastructure in cities requires vegetation that has multiple outcomes and functions, particularly using plants that have both attractive visual or aesthetic features and high biodiversity values. Plantings that have high visual appeal are more highly valued by people and increase their feeling of wellbeing. Increasing biodiversity in cities is one of the major challenges facing urban planning and design. However, balancing biodiversity and aesthetic outcomes in urban planting design is complex, and to date there are few methods that can be used to guide plant selection. To address this knowledge gap, we investigated the use of a colour theory framework for planting arrangements to see if we could design vegetation that is highly aesthetic and has high biodiversity. We did this by configuring planting combinations for living walls in Malmö, Sweden, using principles based on Johannes Itten’s colour theories. The plant combinations on each wall were graphically arranged using (1) colour analysis of each plant and (2) design of the plant species into two colour schemes: light-dark colour concept and a complementary colour concept. For each species used in the compositions we created a biodiversity classification, based on its pollination value, “nativeness” and conservation value as a cultivar; and a plant visual quality classification, based on the performance from living walls studies. The graphical colour composition and interlinked biodiversity value were then compared to designs created with randomly selected plant species. The results showed that it is possible to design a living wall based on colour theory without compromising with biodiversity outcomes, namely species richness, pollination and the nativeness of the species. The results also indicate the potential application of this design approach to deliver greater aesthetic appreciation and enjoyment from plantings. While more work is needed, this study has shown that a theoretical colour framework can be a useful tool in designing green infrastructure to improve delivery of both cultural and regulatory ecosystem services.

A Data Driven Approach to Assess Complex Colour Profiles in Plant Tissues

The ability to quantify the colour of fruit is extremely important for a number of applied fields including plant breeding, postharvest assessment, and consumer quality assessment. Fruit and other plant organs display highly complex colour patterning. This complexity makes it challenging to compare and contrast colours in an accurate and time efficient manner. Multiple methodologies exist that attempt to digitally quantify colour in complex images but these either require a priori knowledge to assign colours to a particular bin, or fit the colours present within segment of the colour space into a single colour value using a thresholding approach. A major drawback of these methodologies is that, through the process of averaging, they tend to synthetically generate values that may not exist within the context of the original image. As such, to date there are no published methodologies that assess colour patterning using a data driven approach. In this study we present a methodology to acquire and process digital images of biological samples that contain complex colour gradients. The CIE (Commission Internationale de l'Eclairage/International Commission on Illumination) ΔE2000 formula was used to determine the perceptually unique colours (PUC) within images of fruit containing complex colour gradients. This process, on average, resulted in a 98% reduction in colour values from the number of unique colours (UC) in the original image. This data driven procedure summarised the colour data values while maintaining a linear relationship with the normalised colour complexity contained in the total image. A weighted ΔE2000 distance metric was used to generate a distance matrix and facilitated clustering of summarised colour data. Clustering showed that our data driven methodology has the ability to group these complex images into their respective binomial families while maintaining the ability to detect subtle colour differences. This methodology was also able to differentiate closely related images. We provide a high quality set of complex biological images that span the visual spectrum that can be used in future colorimetric research to benchmark colourimetric method development.

A Multiview Semantic Vegetation Index for Robust Estimation of Urban Vegetation Cover

Urban vegetation growth is vital for developing sustainable and liveable cities in the contemporary era since it directly helps people’s health and well-being. Estimating vegetation cover and biomass is commonly done by calculating various vegetation indices for automated urban vegetation management and monitoring. However, most of these indices fail to capture robust estimation of vegetation cover due to their inherent focus on colour attributes with limited viewpoint and ignore seasonal changes. To solve this limitation, this article proposed a novel vegetation index called the Multiview Semantic Vegetation Index (MSVI), which is robust to color, viewpoint, and seasonal variations. Moreover, it can be applied directly to RGB images. This Multiview Semantic Vegetation Index (MSVI) is based on deep semantic segmentation and multiview field coverage and can be integrated into any vegetation management platform. This index has been tested on Google Street View (GSV) imagery of Wyndham City Council, Melbourne, Australia. The experiments and training achieved an overall pixel accuracy of 89.4% and 92.4% for FCN and U-Net, respectively. Thus, the MSVI can be a helpful instrument for analysing urban forestry and vegetation biomass since it provides an accurate and reliable objective method for assessing the plant cover at street level.

Divergent Responses of Floral Traits of Lonicera nervosa to Altitudinal Gradients at the Eastern Margin of Hengduan Mountains

Understanding phenotypic responses is crucial for predicting and managing the effects of environmental change on native species. Color and display size are typically used to evaluate the utilization value of ornamental plants, which are also important ornamental characters of Lonicera nervosa Maxim. (L. nervosa). However, there is limited documentation of its floral environmental adaptation. The environmental conditions for the development of an organism changes with altitudinal variation. The aim of this research was to find flower trait variability maintenance and the tradeoff among the organs in five different populations of L. nervosa growing at distinct altitudes. We investigated the distribution patterns of floral color, floral display, and biomass tradeoff along a 700-m altitude gradient from 2,950 to 3,650 m. One-way ANOVA analysis was performed to assess the variability of flower traits and floral color across different altitudes. Moreover, correlations and tradeoffs between flowers and vegetative organs were also observed at different altitude ranges. The results indicated that L. nervosa flowers had a strong adaptability along the elevation and divergent altitude-range-specific patterns, which was divided by an altitude breakpoint at around 3,300 m. Below 3,300 m, petal lightness (petal L) decreased, but total floral display area (TFDA), individual floral dry mass (IFDM), and total floral dry mass (TFDM) increased with an increase in altitude. Whereas, above 3,300 m no significant difference was observed in petal L, TFDA, IFDM, and TFDM decreased slightly with an increase in altitude. The responsibility for the selection on floral color at a lower altitude was stronger than that at a higher altitude, while the selection agents on floral biomass had significant effects within the entire altitude range. However, the effects on floral biomass were opposite on both sides of 3,300 m. Thus, floral trait and floral color can be useful indicators for the domestication of horticultural plants and help to evaluate and initiate management and conservation actions.

High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.

Evaluation of red-colored carotenoids in yeasts by the biomass color

Production of carotenoids with red yeasts is a promising area of industrial biotechnology. All spectrophotometrical ("classic") analyses of carotenoids are based on preliminary extraction of the water-insoluble carotenoids; thus, these analyses are precise but complicated and time consuming. This paper presents a simple method to evaluate the red-colored carotenoids in yeast biomass by its color, without extraction. The method is based on digital characteristics of the biomass whole coloring, and it has already been successfully applied in other areas of biology: to compare plant and animal objects. In contrast to spectrophotometry measuring the amount of light that can pass through a solution, the biomass photo is a reflected color of the insoluble compounds. Application of this method to microorganisms permitted to compare the yeast strains and the effects of substrates or culturing regimes for any change in the red-colored pigments. The proposed rapid method was compared with the classic analyses of the carotenoids and showed that evaluation of red-colored carotenoids by the whole coloring of biomass can be used to discover changes in the yeast carotenoid production. In whole, the paper contributes method which is new for pigmented microorganisms and has a potential application in biotechnology.

Effects of the Relatedness of Neighbours on Floral Colour

The reproductive success of plants depends both on their phenotype and the local neighbourhood in which they grow. Animal-pollinated plants may benefit from increased visitation when surrounded by attractive conspecific individuals, via a “magnet effect.” Group attractiveness is thus potentially a public good that can be exploited by individuals, with selfish exploitation predicted to depend on genetic relatedness within the group. Petal colour is a potentially costly trait involved in floral signalling and advertising to pollinators. Here, we assessed whether petal colour was plastically sensitive to the relatedness of neighbours in the annual herb Moricandia moricandioides, which produces purple petals through anthocyanin pigment accumulation. We also tested whether petal colour intensity was related to nectar volume and sugar content in a context-dependent manner. Although both petal colour and petal anthocyanin concentration did not significantly vary with the neighbourhood configuration, plants growing with kin made a significantly higher investment in petal anthocyanin pigments as a result of the greater number and larger size of their flowers. Moreover the genetic relatedness of neighbours significantly modified the relationship between floral signalling and reward quantity: while focal plants growing with non-kin showed a positive relationship between petal colour and nectar production, plants growing with kin showed a positive relationship between number of flowers and nectar volume, and sugar content. The observed plastic response to group relatedness might have important effects on pollinator behaviour and visitation, with direct and indirect effects on plant reproductive success and mating patterns, at least in those plant species with patchy and genetically structured populations.

Drivers of population divergence and species differentiation in a recent group of indigenous orchids (Vanilla spp.) in Madagascar

With over 25,000 species, orchids are among families with remarkable high rate of diversification. Since Darwin's time, major advances attributed the exceptional diversity of orchids to plant-pollinator interactions. However, unraveling the processes and factors that determine the phenotypic and genotypic variation of natural orchid populations remains a challenge. Here, we assessed genetic population structure and floral differentiation in recently diverged leafless Vanilla species in a world biodiversity hotspot, Madagascar, using seven microsatellite loci and 26 morphometric variables. Additionally, analyses were performed to test for the occurrence of any patterns of isolation by distance, isolation by environment, and isolation by adaptation and to detect possible physical barriers that might have caused genetic discontinuities between populations. Positive inbreeding coefficients detected in 22 populations were probably due to the presence of null alleles, geitonogamy and/or some admixture (sympatric species). In contrast, the only high-altitude population showed an important rate of clonality leading to heterozygote excess. Genetic diversity was maximum in western populations, suggesting a postglacial colonization to the north and south. Clustering analyses identified seven genetic groups characterized by specific floral traits that matched five botanical descriptions in the literature. A contribution of montane refugia and river barriers on population differentiation was detected. We also detected combined effects of IBD/IBE and IBE/IBA on genetic differentiation and suggested this pattern is more likely determined by ecological isolation, although pollinator-mediated divergent selection could not be ruled out for some of the species. Overall, this study provides further insights on speciation in orchids, a group for which Madagascar shows one of the world's highest level of endemism and confirms the importance of the peculiar biogeography of the island in shaping species differentiation.

Digital Image Analysis Using FloCIA Software for Ornamental Sunflower Ray Floret Color Evaluation

As an esthetic trait, ray floret color has a high importance in the development of new sunflower genotypes and their market value. Standard methodology for the evaluation of sunflower ray florets is based on International Union for the Protection of New Varieties of Plants (UPOV) guidelines for sunflower. The major deficiency of this methodology is the necessity of high expertise from evaluators and its high subjectivity. To test the hypothesis that humans cannot distinguish colors equally, six commercial sunflower genotypes were evaluated by 100 agriculture experts, using UPOV guidelines. Moreover, the paper proposes a new methodology for sunflower ray floret color classification - digital UPOV (dUPOV), that relies on software image analysis but still leaves the final decision to the evaluator. For this purpose, we created a new Flower Color Image Analysis (FloCIA) software for sunflower ray floret digital image segmentation and automatic classification into one of the categories given by the UPOV guidelines. To assess the benefits and relevance of this method, accuracy of the newly developed software was studied by comparing 153 digital photographs of F₂ genotypes with expert evaluator answers which were used as the ground truth. The FloCIA enabled visualizations of segmentation of ray floret images of sunflower genotypes used in the study, as well as two dominant color clusters, percentages of pixels belonging to each UPOV color category with graphical representation in the CIE (International Commission on Illumination) L^∗a^∗b^∗ (or simply Lab) color space in relation to the mean vectors of the UPOV category. Precision (repeatability) of ray flower color determination was greater between dUPOV based expert color evaluation and software evaluation than between two UPOV based evaluations performed by the same expert. The accuracy of FloCIA software used for unsupervised (automatic) classification was 91.50% on the image dataset containing 153 photographs of F₂ genotypes. In this case, the software and the experts had classified 140 out of 153 of images in the same color categories. This visual presentation can serve as a guideline for evaluators to determine the dominant color and to conclude if more than one significant color exists in the examined genotype.

A simple genetic architecture and low constraint allow rapid floral evolution in a diverse and recently radiating plant genus

Genetic correlations among different components of phenotypes, especially those resulting from pleiotropy, can constrain or facilitate trait evolution. These factors could especially influence the evolution of traits that are functionally integrated, such as those comprising the flower. Indeed, pleiotropy is proposed as a main driver of repeated convergent trait transitions, including the evolution of phenotypically similar pollinator syndromes. We assessed the role of pleiotropy in the differentiation of floral and other reproductive traits between two species - Jaltomata sinuosa and J. umbellata (Solanaceae) - that have divergent suites of floral traits consistent with bee and hummingbird pollination, respectively. To do so, we generated a hybrid population and examined the genetic architecture (trait segregation and quantitative trait locus (QTL) distribution) underlying 25 floral and fertility traits. We found that most floral traits had a relatively simple genetic basis (few, predominantly additive, QTLs of moderate to large effect), as well as little evidence of antagonistic pleiotropy (few trait correlations and QTL colocalization, particularly between traits of different classes). However, we did detect a potential case of adaptive pleiotropy among floral size and nectar traits. These mechanisms may have facilitated the rapid floral trait evolution observed within Jaltomata, and may be a common component of rapid phenotypic change more broadly.

Application of Adaptive Neuro-Fuzzy Inference System-Non-dominated Sorting Genetic Algorithm-II (ANFIS-NSGAII) for Modeling and Optimizing Somatic Embryogenesis of Chrysanthemum

A hybrid artificial intelligence model and optimization algorithm could be a powerful approach for modeling and optimizing plant tissue culture procedures. The aim of this study was introducing an Adaptive Neuro-Fuzzy Inference System- Non-dominated Sorting Genetic Algorithm-II (ANFIS-NSGAII) as a powerful computational methodology for somatic embryogenesis of chrysanthemum, as a case study. ANFIS was used for modeling three outputs including callogenesis frequency (CF), embryogenesis frequency (EF), and the number of somatic embryo (NSE) based on different variables including 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), sucrose, glucose, fructose, and light quality. Subsequently, models were linked to NSGAII for optimizing the process, and the importance of each input was evaluated by sensitivity analysis. Results showed that all of the R² of training and testing sets were over 92%, indicating the efficiency and accuracy of ANFIS on the modeling of the embryogenesis. Also, according to ANFIS-NSGAII, optimal EF (99.1%), and NSE (13.1) can be obtained from a medium containing 1.53 mg/L 2,4-D, 1.67 mg/L BAP, 13.74 g/L sucrose, 57.20 g/L glucose, and 0.39 g/L fructose under red light. The results of the sensitivity analysis showed that embryogenesis was more sensitive to 2,4-D, and less sensitive to fructose. Generally, the hybrid ANFIS-NSGAII can be recognized as a powerful computational tool for modeling and optimizing in plant tissue culture.

Traits influence detection of exotic plant species in tropical forests

Detecting exotic plant species is essential for invasive species management. By accounting for factors likely to affect species’ detection rates (e.g. survey conditions, observer experience), detectability models can help choose search methods and allocate search effort. Integrating information on species’ traits can refine detectability models, and might be particularly valuable if these traits can help improve estimates of detectability where data on particular species are rare. Analysing data collected during line transect distance sampling surveys in Indonesia, we used a multi-species hierarchical distance sampling model to evaluate how plant height, leaf size, leaf shape, and survey location influenced plant species detectability in secondary tropical rainforests. Detectability of the exotic plant species increased with plant height and leaf size. Detectability varied among the different survey locations. We failed to detect a clear effect of leaf shape on detectability. This study indicates that information on traits might improve predictions about exotic species detection, which can then be used to optimise the allocation of search effort for efficient species management. The innovation of the study lies in the multi-species distance sampling model, where the distance-detection function depends on leaf traits and height. The method can be applied elsewhere, including for different traits that may be relevant in other contexts. Trait-based multispecies distance sampling can be a practical approach for sampling exotic shrubs, herbs, or grasses species in the understorey of tropical forests.

Automating analysis of vegetation with computer vision: Cover estimates and classification

This study develops an approach to automating the process of vegetation cover estimates using computer vision and pattern recognition algorithms. Visual cover estimation is a key tool for many ecological studies, yet quadrat-based analyses are known to suffer from issues of consistency between people as well as across sites (spatially) and time (temporally). Previous efforts to estimate cover from photograps require considerable manual work. We demonstrate that an automated system can be used to estimate vegetation cover and the type of vegetation cover present using top-down photographs of 1 m by 1 m quadrats. Vegetation cover is estimated by modelling the distribution of color using a multivariate Gaussian. The type of vegetation cover is then classified, using illumination robust local binary pattern features, into two broad groups: graminoids (grasses) and forbs. This system is evaluated on two datasets from the globally distributed experiment, the Nutrient Network (NutNet). These NutNet sites were selected for analyses because repeat photographs were taken over time and these sites are representative of very different grassland ecosystems-a low stature subalpine grassland in an alpine region of Australia and a higher stature and more productive lowland grassland in the Pacific Northwest of the USA. We find that estimates of treatment effects on grass and forb cover did not differ between field and automated estimates for eight of nine experimental treatments. Conclusions about total vegetation cover did not correspond quite as strongly, particularly at the more productive site. A limitation with this automated system is that the total vegetation cover is given as a percentage of pixels considered to contain vegetation, but ecologists can distinguish species with overlapping coverage and thus can estimate total coverage to exceed 100%. Automated approaches such as this offer techniques for estimating vegetation cover that are repeatable, cheaper to use, and likely more reliable for quantifying changes in vegetation over the long-term. These approaches would also enable ecologists to increase the spatial and temporal depth of their coverage estimates with methods that allow for vegetation sampling over large spatial scales quickly.

Digital photography provides a fast, reliable, and noninvasive method to estimate anthocyanin pigment concentration in reproductive and vegetative plant tissues

Anthocyanin pigments have become a model trait for evolutionary ecology as they often provide adaptive benefits for plants. Anthocyanins have been traditionally quantified biochemically or more recently using spectral reflectance. However, both methods require destructive sampling and can be labor intensive and challenging with small samples. Recent advances in digital photography and image processing make it the method of choice for measuring color in the wild. Here, we use digital images as a quick, noninvasive method to estimate relative anthocyanin concentrations in species exhibiting color variation. Using a consumer‐level digital camera and a free image processing toolbox, we extracted RGB values from digital images to generate color indices. We tested petals, stems, pedicels, and calyces of six species, which contain different types of anthocyanin pigments and exhibit different pigmentation patterns. Color indices were assessed by their correlation to biochemically determined anthocyanin concentrations. For comparison, we also calculated color indices from spectral reflectance and tested the correlation with anthocyanin concentration. Indices perform differently depending on the nature of the color variation. For both digital images and spectral reflectance, the most accurate estimates of anthocyanin concentration emerge from anthocyanin content‐chroma ratio, anthocyanin content‐chroma basic, and strength of green indices. Color indices derived from both digital images and spectral reflectance strongly correlate with biochemically determined anthocyanin concentration; however, the estimates from digital images performed better than spectral reflectance in terms of r² and normalized root‐mean‐square error. This was particularly noticeable in a species with striped petals, but in the case of striped calyces, both methods showed a comparable relationship with anthocyanin concentration. Using digital images brings new opportunities to accurately quantify the anthocyanin concentrations in both floral and vegetative tissues. This method is efficient, completely noninvasive, applicable to both uniform and patterned color, and works with samples of any size.

Multiple strong postmating and intrinsic postzygotic reproductive barriers isolate florally diverse species of Jaltomata (Solanaceae)

Divergence in phenotypic traits often contributes to premating isolation between lineages, but could also promote isolation at postmating stages. Phenotypic differences could directly result in mechanical isolation or hybrids with maladapted traits; alternatively, when alleles controlling these trait differences pleiotropically affect other components of development, differentiation could indirectly produce genetic incompatibilities in hybrids. Here, we determined the strength of nine postmating and intrinsic postzygotic reproductive barriers among 10 species of Jaltomata (Solanaceae), including species with highly divergent floral traits. To evaluate the relative importance of floral trait diversification for the strength of these postmating barriers, we assessed their relationship to floral divergence, genetic distance, geographical context, and ecological differences, using conventional tests and a new linear-mixed modeling approach. Despite close evolutionary relationships, all species pairs showed moderate to strong isolation. Nonetheless, floral trait divergence was not a consistent predictor of the strength of isolation; instead this was best explained by genetic distance, although we found evidence for mechanical isolation in one species, and a positive relationship between floral trait divergence and fruit set isolation across species pairs. Overall, our data indicate that intrinsic postzygotic isolation is more strongly associated with genome-wide genetic differentiation, rather than floral divergence.

Quantifying Human Visible Color Variation from High Definition Digital Images of Orb Web Spiders

Digital processing and analysis of high resolution images of 30 individuals of the orb web spider Verrucosa arenata were performed to extract and quantify human visible colors present on the dorsal abdomen of this species. Color extraction was performed with minimal user intervention using an unsupervised algorithm to determine groups of colors on each individual spider, which was then analyzed in order to quantify and classify the colors obtained, both spatially and using energy and entropy measures of the digital images. Analysis shows that the colors cover a small region of the visible spectrum, are not spatially homogeneously distributed over the patterns and from an entropic point of view, colors that cover a smaller region on the whole pattern carry more information than colors covering a larger region. This study demonstrates the use of processing tools to create automatic systems to extract valuable information from digital images that are precise, efficient and helpful for the understanding of the underlying biology.

The Shortlist Method for fast computation of the Earth Mover's Distance and finding optimal solutions to transportation problems

Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method.

Flower colours through the lens: quantitative measurement with visible and ultraviolet digital photography

Background

The study of the signal-receiver relationship between flowering plants and pollinators requires a capacity to accurately map both the spectral and spatial components of a signal in relation to the perceptual abilities of potential pollinators. Spectrophotometers can typically recover high resolution spectral data, but the spatial component is difficult to record simultaneously. A technique allowing for an accurate measurement of the spatial component in addition to the spectral factor of the signal is highly desirable.

Methodology/Principal findings

Consumer-level digital cameras potentially provide access to both colour and spatial information, but they are constrained by their non-linear response. We present a robust methodology for recovering linear values from two different camera models: one sensitive to ultraviolet (UV) radiation and another to visible wavelengths. We test responses by imaging eight different plant species varying in shape, size and in the amount of energy reflected across the UV and visible regions of the spectrum, and compare the recovery of spectral data to spectrophotometer measurements. There is often a good agreement of spectral data, although when the pattern on a flower surface is complex a spectrophotometer may underestimate the variability of the signal as would be viewed by an animal visual system.

Conclusion

Digital imaging presents a significant new opportunity to reliably map flower colours to understand the complexity of these signals as perceived by potential pollinators. Compared to spectrophotometer measurements, digital images can better represent the spatio-chromatic signal variability that would likely be perceived by the visual system of an animal, and should expand the possibilities for data collection in complex, natural conditions. However, and in spite of its advantages, the accuracy of the spectral information recovered from camera responses is subject to variations in the uncertainty levels, with larger uncertainties associated with low radiance levels.