| Full text | |
| Author(s): |
Florindo, Joao Batista
[1]
;
da Silva, Nubia Rosa
[2, 1]
;
Romualdo, Liliane Maria
[3]
;
da Silva, Fernanda de Fatima
[3]
;
de Cerqueira Luz, Pedro Henrique
[3]
;
Herling, Valdo Rodrigues
[3]
;
Bruno, Odemir Martinez
[2, 1]
Total Authors: 7
|
| Affiliation: | [1] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, BR-13560970 Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, BR-13566590 Sao Carlos, SP - Brazil
[3] Univ Sao Paulo, Coll Anim Sci & Food Engn, Dept Anim Sci, BR-13635900 Pirassununga, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 103, p. 48-54, APR 2014. |
| Web of Science Citations: | 13 |
| 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, since it has broad representation in grazing from tropical regions. Nowadays it occupies about 90% of the grazing area along Brazil and, besides the grazing areas to feed ruminants, Brachiaria also corresponds to about 80% of seeds being traded in all the world, bringing a large amount of money to Brazil. To identify species and/or cultivars of this genus is of fundamental importance in the fields that produce seeds, to ensure varietal purity and the effectiveness of improvement programs. Thus, leaf samples of fodder plant species Bra chiaria 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: Brachiaria decumbens cv. IPEAN; Brachiaria ruziziensis Germain \& Evrard; Brachiaria brizantha (Hochst. ex. A. Rich.) Stapf; Brachiaria arrecta (Hack.) Stent. and 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. The proposed method outperforms other state-of-the-art image analysis methods and makes possible the correct prediction of species in more than 93% of the samples. Such remarkable result is consequence of the better suitability of representing complex structures like those arising in the plant leaves by measures of complexity from fractal geometry. Finally, this high correctness rate suggests that the fractal method is an important tool to help the botanist. (C) 2014 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 11/01523-1 - Computer vision methods applied to the identification and analysis of plants |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 11/21467-9 - Heterogeneous Pattern Recognition and its Applications in Biology and Nanotechnology. |
| Grantee: | Núbia Rosa da Silva |
| Support Opportunities: | Scholarships in Brazil - Doctorate |