Grass blindness

Photographs as an essential biodiversity resource: drivers of gaps in the vascular plant photographic record

The photographic record is increasingly becoming an important biodiversity resource for primary research and conservation monitoring. However, globally, there are important gaps in this record even in relatively well-researched floras. To quantify the gaps in the Australian native vascular plant photographic record, we systematically surveyed 33 sources of well-curated species photographs, assembling a list of species with accessible and verifiable photographs, as well as the species for which this search failed. Of 21 077 Australian native species, 3715 lack a verifiable photograph across our 33 surveyed resources. There are three major geographic hotspots of unphotographed species in Australia, all far from current population centres. Many unphotographed species are small in stature or uncharismatic, and many are also recently described. The large number of recently described species without accessible photographs was surprising. There are longstanding efforts in Australia to organise the plant photographic record, but in the absence of a global consensus to treat photographs as an essential biodiversity resource, this has not become common practice. Many recently described species are small-range endemics and some have special conservation status. Completing the botanical photographic record across the globe will facilitate a virtuous feedback loop of more efficient identification, monitoring and conservation.

Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant Perspectives

Poaceae represent one of the largest plant families in the world. Many species are of great economic importance as food and forage plants while others represent important weeds in agriculture. Although a large number of studies currently address the question of how plants can be best recognized on images, there is a lack of studies evaluating specific approaches for uniform species groups considered difficult to identify because they lack obvious visual characteristics. Poaceae represent an example of such a species group, especially when they are non-flowering. Here we present the results from an experiment to automatically identify Poaceae species based on images depicting six well-defined perspectives. One perspective shows the inflorescence while the others show vegetative parts of the plant such as the collar region with the ligule, adaxial and abaxial side of the leaf and culm nodes. For each species we collected 80 observations, each representing a series of six images taken with a smartphone camera. We extract feature representations from the images using five different convolutional neural networks (CNN) trained on objects from different domains and classify them using four state-of-the art classification algorithms. We combine these perspectives via score level fusion. In order to evaluate the potential of identifying non-flowering Poaceae we separately compared perspective combinations either comprising inflorescences or not. We find that for a fusion of all six perspectives, using the best combination of feature extraction CNN and classifier, an accuracy of 96.1% can be achieved. Without the inflorescence, the overall accuracy is still as high as 90.3%. In all but one case the perspective conveying the most information about the species (excluding inflorescence) is the ligule in frontal view. Our results show that even species considered very difficult to identify can achieve high accuracies in automatic identification as long as images depicting suitable perspectives are available. We suggest that our approach could be transferred to other difficult-to-distinguish species groups in order to identify the most relevant perspectives.