Not that kind of tree: Assessing the potential for decision tree–based plant identification using trait databases

Abstract

Plant identification is critical for a wide range of biological fields and goals, ranging from understanding ecological processes, such as community assembly, to the conservation of rare and threatened species (Thessen, 2016). Historically, species have been identified using trait-based approaches in the form of dichotomous and polyclave keys (Tilling, 1984; Edwards et al., 1987). These identification keys remain an important and widely used resource for scientists (Gaylard and Kerley, 1995; Randler, 2008), as they are convenient, inexpensive, and enable identification when tissue samples cannot be collected for molecular barcoding (Will and Rubinoff, 2004). Improving trait-based plant identification (e.g., reducing the number of traits required for identification) could be especially useful for improving the efficacy of citizen scientists in large-scale projects where the use of genetic tools is not feasible or cost-effective (Gallo and Waitt, 2011; Roy et al., 2016). Advancements in computational methods such as machine learning, in tandem with the recent rise of online, easily accessible "big data, " could provide an unprecedented opportunity to improve traitbased identification, just as it has proved useful in other important ecological areas. For instance, machine learning has been applied to large databases to predict phenomena such as global surface temperatures (Casaioli et al., 2003), and underpins some of the most

Prediction of the taxonomical classification of theRanunculaceaefamily using a machine learning method

“…A DT classification model is represented by a tree-like structure, where each internal node represents a test of a feature (fingerprint), with each branch representing one of the possible test results and each leaf node representing a target classification (species or genus). 25,26 In our DT analysis, the minimum number of leaf nodes of 15 and a maximum depth of 70 were applied. The CNN is an alternative type of Deep Neural Network (DNN) that models both time and space correlations in a multivariate signal.…”

“…28 The above machine learning methods have been widely applied in the phenotypebased classification of plants, prediction of drug targets, estimation of the plant physiological phenotype, etc. [23][24][25] In order to avoid overfitting when evaluating the model performance, ten-fold CV (cross-validation) was used with 70% data as a training set and 30% data as a testing set. A tenfold CV was performed to split the training data randomly into ten equally sized folds.…”

Decoding family‐level features for modern and fossil leaves from computer‐vision heat maps

“…Machine learning identification of extant and fossil pollen at the species level has advanced significantly (Punyasena et al, 2012; Tcheng et al, 2016; Romero et al, 2020; White, 2020). Automated species identification of leaf images, in particular, is a well‐studied problem in computer vision (Im et al, 1998; Wu et al, 2007; Nam et al, 2008; Park et al, 2008; Caballero and Aranda, 2010; Bama et al, 2011; Hu et al, 2012; Laga et al, 2012; Larese et al, 2012; Mouine et al, 2012; Priya et al, 2012; Charters et al, 2014; Larese et al, 2014a, b; Jamil et al, 2015; Mata‐Montero and Carranza‐Rojas, 2015, 2016; Zhao et al, 2015; Grinblat et al, 2016; Larese and Granitto, 2016; Carranza‐Rojas, Mata‐Montero et al, 2018; Wäldchen and Mäder, 2018; Wäldchen et al, 2018; Almeida et al, 2020; Banerjee and Pamula, 2020; Bryson et al, 2020; Pryer et al, 2020; Soltis et al, 2020; Mukherjee et al, 2021; Zhou et al, 2021). However, there have been few efforts to unpack the diagnostic features revealed from AI for the benefit of botanists.…”

On the identification of some species of the Cassieae tribe harvested in Cameroon using three Machine Learning technics

“…The goal is to identify plants by simply comparing their description taken in the field to that of a database. In order to help users to have easily access for the description and classification of species, it is important to notice that many authors (Wu et al, 2007 ;Backes et al, 2009 ;Priya et al, 2012 ;Arai et al, 2013 ;Jamil et al, 2015 ;Nazarenko et al, 2016 ;Begue et al, 2017 ;Kaur and Kaur 2019 ;Almeida et al, 2020) used Machine Learning methods for plant identification.…”

“…Most of the authors carry out the identification of plants using Machine Learning algorithms on images of these plants. However, Almeida et al (2020) noted that it is not always easy to have the images of all parts. In this study, we describe the morphological and reproductive characters of specimens of 13 species of the Cassieae tribe of the Fabaceae family collected in Cameroon.…”