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; machine learning; natural history collection; Phenology; plant growth; Plant species; Seasonal variations; Shoots; Species diversity; Systematics, Phylogenetics and taxonomy; Trees; Tropical environments; Tropical forests; 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

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...