Brachiaria species identification using imaging techniques based on fractal descriptors

TL;DR

This study presents a rapid, accurate method for identifying Brachiaria species using leaf imaging and fractal descriptors analyzed by support vector machines, enhancing forage crop classification.

Contribution

The paper introduces a novel approach combining fractal descriptors and SVMs for species identification in forage plants, improving classification accuracy.

Findings

High classification accuracy achieved

Effective differentiation among Brachiaria species

Potential for rapid forage crop diagnosis

Abstract

The use of a rapid and accurate method in diagnosis and classification of species and/or cultivars of forage has practical relevance, scientific and trade in various areas of study. Thus, leaf samples of fodder plant species \textit{Brachiaria} were previously identified, collected and scanned to be treated by means of artificial vision to make the database and be used in subsequent classifications. Forage crops used were: \textit{Brachiaria decumbens} cv. IPEAN; \textit{Brachiaria ruziziensis} Germain \& Evrard; \textit{Brachiaria Brizantha} (Hochst. ex. A. Rich.) Stapf; \textit{Brachiaria arrecta} (Hack.) Stent. and \textit{Brachiaria spp}. The images were analyzed by the fractal descriptors method, where a set of measures are obtained from the values of the fractal dimension at different scales. Therefore such values are used as inputs for a state-of-the-art classifier, the Support Vector Machine, which finally discriminates the images according to the respective species.