Automatic Aquatic Weed Classification Using Shape Analysis and Optimum-Path Forest

• Grant number: 10/12222-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: May 01, 2011
• End date: December 31, 2011
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: João Paulo Papa
• Grantee: Luis Augusto Martins Pereira
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Associated research grant: 09/16206-1 - New trends on optimum-path forest-based pattern recognition, AP.JP

Abstract

The aquatic weed control through chemical substances has attracted much attention in the past years, mainly because of the ecological disorder caused by such plants with respect to the financial activities derived from the river, such as fishing and energy production, for instance. The fast growing of such plants may affect the whole ecosystem, degrading other essential plants that keep stable the local fauna and flora. Other negative aspect is the indiscriminate application of wrong chemical reagents, and also many times in inadequate quantities aiming the aquatic weed control, given that each specie has its own chemical control. Recent works have applied computer vision techniques aiming the identification of weeds in a certain region, for further application of chemical control in that river region. However, none study took into account the identification of the weed specie itself for further application of the correct chemical control, given that some of them are more effective than others for specific species. Therefore, the present research project has as the main goal a initial study of the automatic identification of three aquatic weed species, aiming a composition of a future expert system for the correct chemical control of such plants, using shape descriptors and supervised pattern recognition techniques, such as Optimum-Path Forest - OPF, Support Vector Machines - SVMs and Artificial Neural Networks - ANNs. It can be noticed that is the first time that OPF is used in this context, as well a comparison among supervised pattern recognition using shape features for the automatic recognition of aquatic weeds.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

PEREIRA, LUIS A. M.; NAKAMURA, RODRIGO Y. M.; DE SOUZA, GUILHERME F. S.; MARTINS, DAGOBERTO; PAPA, JOAO P.. Aquatic weed automatic classification using machine learning techniques. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 87, p. 56-63, SEP 2012. (11/14094-1, 10/12222-0, 11/14058-5)