Publication:
Deep Convolutional Neural Networks for Plant Species Characterization Based on Leaf Midrib

Simple item page
dc.contributor.author Scabini, Leonardo F. S. es_PE
dc.contributor.author Condori, Rayner M. es_PE
dc.contributor.author Munhoz, Isabella C. L. es_PE
dc.contributor.author Bruno, Odemir M. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2019
dc.description.abstract The automatic characterization and classification of plant species is an important task for plant taxonomists. On this work, we propose the use of well-known pre-trained Deep Convolutional Neural Networks (DCNN) for the characterization of plants based on their leaf midrib. The samples studied are microscope images of leaf midrib cross-sections taken from different specimens under varying conditions. Results with traditional handcrafted image descriptors demonstrate the difficulty to effectively characterize these samples. Our proposal is to use DCNN as a feature extractor through Global Average Pooling (GAP) over the raw output of its last convolutional layers without the application of summarizing functions such as ReLU and local poolings. Results indicate considerably performance improvements over previous approaches under different scenarios, varying the image color-space (gray-level or RGB) and the classifier (KNN or LDA). The highest result is achieved by the deeper network analyzed, ResNet (101 layers deep), using the LDA classifier, with 99.20% of accuracy rate. However, shallower networks such as AlexNet also provide good classification results (97.36%), which is still a significant improvement over the best previous result (83.67% of combined fractal descriptors).
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1007/978-3-030-29891-3_34
dc.identifier.uri https://hdl.handle.net/20.500.12390/2803
dc.language.iso eng
dc.publisher Springer International Publishing
dc.relation.ispartof COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT II
dc.rights info:eu-repo/semantics/openAccess
dc.subject Plant classification
dc.subject Deep Convolutional Neural Networks es_PE
dc.subject Global average pooling es_PE
dc.subject Leaf midrib es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#2.02.04
dc.title Deep Convolutional Neural Networks for Plant Species Characterization Based on Leaf Midrib
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
Files
Collections
1.1 Eventos institucionales
 4.1 Organización de eventos