Plant species identification using color learning resources, shape, texture, through machine learning and artificial neural networks

Deborah Bambil (), Hemerson Pistori, Francielli Bao, Vanessa Weber, Flávio Macedo Alves, Eduardo Gomes Gonçalves, Lúcio Flávio Alencar Figueiredo, Urbano G. P. Abreu, Rafael Arruda and Ieda Maria Bortolotto
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Deborah Bambil: Federal University of Mato Grosso do Sul (UFMS)
Hemerson Pistori: Catholic University Dom Bosco
Francielli Bao: São Paulo State University
Vanessa Weber: Catholic University Dom Bosco
Flávio Macedo Alves: Federal University of Mato Grosso do Sul (UFMS)
Eduardo Gomes Gonçalves: Catholic University Dom Bosco
Lúcio Flávio Alencar Figueiredo: UnB
Urbano G. P. Abreu: Embrapa Pantanal
Rafael Arruda: Federal University of Mato Grosso
Ieda Maria Bortolotto: Federal University of Mato Grosso do Sul (UFMS)

Environment Systems and Decisions, 2020, vol. 40, issue 4, 480-484

Abstract: Abstract Morphological characteristics are still the most used tools for the identification of plant species. In this context, leaves are the most available plant organ used, given their perenniality and diversity. Computer-based image analysis help extract morphological features for botanical identification and maybe a solution to taxonomic problems requiring extensively trained specialists that use visual identification as the primary method for this approach. In this study, were collected 40 leaves from 30 trees and shrub species from 19 different families. Here, we compared two popular image capture devices: a scanner and a mobile phone. Features analyzed comprised color, shape, and texture. The performance of both devices was compared through three machine learning algorithms (adaptive boosting—AdaBoost, random forest, support vector machine—SVM) and an artificial neural network model (deep learning). Computer vision showed to be efficient in the identification of species (higher than 93%), with similar results obtained for both mobile phones and scanners. The algorithms SVM, random forest and deep learning performed more efficiently than AdaBoost. Based on the results, we present the Inovtaxon Plant Species Identification Software, available at https://github.com/DeborahBambil/Inovtaxon .

Keywords: Computer vision; Deep learning; Inovtaxon; Machine learning; Morphology; Neural networks; Taxonomy (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10669-020-09769-w

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