Classification of Leaf Diseases in Soybean Culture by Haar Wavelet Transform
Abstract
Soybean (Glycine max), a plant of great economic importance in Brazil and other countries, is the target of a variety of diseases which attack not only leaf part of plant, but also stem, roots and seed itself. Approximately 40 diseases were identified in country, being caused by fungi, bacteria, viruses and nematodes, negatively affecting production, thus limiting crop yield. The objective of this work is to extract texture characteristics of image using the wavelet transform, to analyze regions of soybean leaves affected by pathogens, making the image decomposition, obtaining approximation coeficients. Data that allow classification of the diseases analyzed. The Haar Wavelet Transform was used in feature extraction process and Euclidean distance in classification process. An exploratory database was used, the results were obtained for a proposal effectiveness, whereas adjustments may improve the method presented.
Text in Portuguese, Original Title: Classificacao de Doencas Foliares em Culturas de Soja com Transformadas Wavelet de Haar.
Fabrizzio Soares
Associate Professor and CS Chair
Fabrizzio Soares is a professor of Computer Science, Information Systems and Software Engineering at INF/UFG. His research interests include Computer Vision, Human Computer Interaction, Machine Learning and Programming topics. He is the leader of the Pixellab group, which develops solutions for accesibilty, Precision Agriculture, and Interactive Systems.
