Determining the community composition of herbaceous species from images using convolutional neural networks

Global change has a detrimental impact on the environment and changes biodiversity patterns, which can be observed, among others, via analyzing changes in the composition of plant communities. Typically, vegetation releve ' es are done manually, which is time-consuming, laborious, and subjective. Applying an automatic system for such an analysis that can also identify co-occurring species would be beneficial as it is fast, effortless to use, and consistent. Here, we introduce such a system based on Convolutional Neural Networks for automatically predicting the species-wise plant cover. The system is trained on freely available image data of herbaceous plant species from web sources and plant cover estimates done by experts. With a novel extension of our original approach, the system can even be applied directly to vegetation images without requiring such cover estimates. Our extended approach, not utilizing dedicated training data, performs similarly to humans concerning the relative species abundances in the vegetation releve ' es. When trained on dedicated training annotations, it reflects the original estimates more closely than (independent) human experts, who manually analyzed the same sites. Our method is, with little adaptation, usable in novel domains and could be used to analyze plant community dynamics and responses of different plant species to environmental changes.