Open AccessPatch-Based Model for the Classification of Soybean Leaf Diseases
Author(s) -
Gustavo Vigilato G. S.,
Pablo G. Cavalcanti
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/wvc.2021.18899
Subject(s) - context (archaeology) , support vector machine , convolutional neural network , computer science , artificial intelligence , contextual image classification , pattern recognition (psychology) , machine learning , leaf spot , image (mathematics) , agronomy , biology , paleontology
The disease detection is vital to increase the productivity and quality of soybean cultivation and this detection is usually carried out in a laboratory, which is time consuming and costly. To overcome these issues, there is a growing demand for technologies that aim at a faster detection and classification of diseases. In this context, this work proposes the extraction of several patches from a leaf image and combining a convolutional neural network with a support vector machine, we present a complete model for the classification of soybean leaf diseases. In this approach, an image dataset with evidence of diseases commonly observed in soybean crops was analyzed and our experiments achieved precisions greater than 90%.