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COLOR MACHINE LEARNING UPDATE
Ĭhase MW, Christenhusz MJM, Fay MF, Byng JW, Judd WS, Soltis DE et al (2016) An update of the angiosperm phylogeny group classification for the orders and families of flowering plants: APG IV. Īrafat SY, Saghir MI, Ishtiaq M, Bashir U (2016) Comparison of techniques for leaf classification. Based on the results, we present the Inovtaxon Plant Species Identification Software, available at. The algorithms SVM, random forest and deep learning performed more efficiently than AdaBoost. 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 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). Features analyzed comprised color, shape, and texture. Here, we compared two popular image capture devices: a scanner and a mobile phone. In this study, were collected 40 leaves from 30 trees and shrub species from 19 different families. 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 context, leaves are the most available plant organ used, given their perenniality and diversity. Morphological characteristics are still the most used tools for the identification of plant species.
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