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

Abstract

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.

Botanical Collection Patterns and Conservation Categories of the Most Traded Timber Species from the Ecuadorian Amazon: The Role of Protected Areas

“…To advance in the field of biodiversity and maximize the potential of the information available, it is necessary to continue improving the systems for collecting, storing, and accessing data [ 107 , 108 ]. This includes the promotion of common data standards, collaboration between institutions, and the incorporation of new technologies, such as artificial intelligence and machine learning, to facilitate the analysis and interpretation of data [ 95 , 109 , 110 , 111 , 112 , 113 ]. In this way, we will be able to take full advantage of the value of herbarium specimens and databases for scientific advancement and forest and biodiversity conservation.…”

Challenges and opportunities in applying AI to evolutionary morphology

“…For example, in LeafMachine (Weaver et al, 2020;Weaver and Smith, 2023), a CNN algorithm was trained to measure leaf area and perimeter from lowquality images, with a success rate of 60%. In another study, a different CNN algorithm was shown to be capable of discriminating between growth shoots and vegetative structures in tropical plants from French Guiana (Goëau et al, 2022). While this study showed a high false positive rate of 20% when identifying growth shoots, it performed well, given the complexity and variability of these structures.…”

“…AI methods, including CNNs, have been successfully applied on small training datasets to recognise species and extract both discrete and meristic traits (Wäldchen and Mäder, 2018). Other examples include using ML tools to extract, classify and count reproductive structures (Goëau et al, 2022;Love et al, 2021), as well as to produce basic measurements such as leaf size (Hussein et al, 2021;Weaver et al, 2020). These methods have also been shown to work on x-ray scans of fossil leaves (Wilf et al, 2021), including counting stomatal and epidermal cells for palaeoclimatic analysis (Zhang et al, 2023).…”

From leaves to labels: Building modular machine learning networks for rapid herbarium specimen analysis with LeafMachine2

“…These frameworks are extremely flexible, well‐supported, and surprisingly approachable. As a result, many recent projects have also coalesced around these two frameworks with great success, including efforts to segment leaves (Younis et al, 2020; Triki et al, 2020, 2021; Guo et al, 2021; Hussein et al, 2021b; Gu et al, 2022; Ott and Lautenschlager, 2022), segment plant tissue (Love et al, 2021; Goëau et al, 2022; Milleville et al, 2023), isolate plant organs (Davis et al, 2020; Pearson et al, 2020; Triki et al, 2020; Ott and Lautenschlager, 2022), extract specimen label data (Milleville et al, 2023), isolate diseased or damaged leaf tissue (Kaur et al, 2022; Mu et al, 2022; Kavitha Lakshmi and Savarimuthu, 2023), measure bird skeletons (Weeks et al, 2023), isolate preserved snakes (Curlis et al, 2022), segment fossils (Panigrahi et al, 2022), or remotely monitor phenology (Mann et al, 2022). However, rather than relying on a single machine learning architecture to extract trait and archival data from specimens, we developed a modular framework of seven different machine learning algorithms that work in tandem to comprehensively process each image (Table 2, Figure 1).…”

“…To overcome these limitations, many groups turned to machine learning algorithms, typically some kind of convolutional neural network (CNN), which can categorize individual pixels as members of discrete classes (Ott et al, 2020; Weaver et al, 2020; Younis et al, 2020; Triki et al, 2020, 2021; Goëau et al, 2020, 2022; Guo et al, 2021; Love et al, 2021; Hussein et al, 2021b; Gu et al, 2022; Ott and Lautenschlager, 2022; Milleville et al, 2023). For the task of isolating and measuring individual leaves, semantic segmentation algorithms still lack the power to resolve complex situations (e.g., overlapping leaves) because they produce one mask that contains all leaf pixels and require postprocessing to obtain usable results (Weaver et al, 2020; Hussein et al, 2021b, 2022).…”