Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest

• Published in: Plants (Basel) Vol. 11; no. 4; pp. 530 - 552
• Main Authors: Goëau, Hervé, Lorieul, Titouan, Heuret, Patrick, Joly, Alexis, Bonnet, Pierre
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.02.2022; MDPI
• Subjects: Artificial intelligence; Automation; Biodiversity; Biodiversity and Ecology; Botanics; Carbohydrates; Carbon sequestration; Climate change; Collections; Deep learning; Digitization; Ecology, environment; Ecosystems; Environmental Sciences; Flora; Forest growth; Growth patterns; herbarium specimens; Leaf fall; Life Sciences; Low temperature; machine learning; natural history collection; Phenology; plant growth; Plant species; Seasonal variations; Shoots; Species diversity; Systematics, Phylogenetics and taxonomy; Trees; Tropical environments; Vegetal Biology; vegetative phenology; Winter; French Guiana; plant growth; deep learning; visual analysis; herbarium specimens; machine learning; natural history collection; neural network; vegetative phenology; Deep learning; Natural history collection; Vegetative phenology; Plant growth; Herbarium specimens; Machine learning; Visual analysis; Neural network
• ISSN: 2223-7747; 2223-7747
• DOI: 10.3390/plants11040530

A better knowledge of tree vegetative growth phenology and its relationship to environmental variables is crucial to understanding forest growth dynamics and how climate change may affect it. Less studied than reproductive structures, vegetative growth phenology focuses primarily on the analysis of growing shoots, from buds to leaf fall. In temperate regions, low winter temperatures impose a cessation of vegetative growth shoots and lead to a well-known annual growth cycle pattern for most species. The humid tropics, on the other hand, have less seasonality and contain many more tree species, leading to a diversity of patterns that is still poorly known and understood. The work in this study aims to advance knowledge in this area, focusing specifically on herbarium scans, as herbariums offer the promise of tracking phenology over long periods of time. However, such a study requires a large number of shoots to be able to draw statistically relevant conclusions. We propose to investigate the extent to which the use of deep learning can help detect and type-classify these relatively rare vegetative structures in herbarium collections. Our results demonstrate the relevance of using herbarium data in vegetative phenology research as well as the potential of deep learning approaches for growing shoot detection.