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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Semi-supervised learning with convolutional neural networks for UAV images automatic recognition

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Author(s):
Amorim, Willian Paraguassu [1] ; Tetila, Everton Castelao [2] ; Pistori, Hemerson [2] ; Papa, Joao Paulo [3]
Total Authors: 4
Affiliation:
[1] Fed Univ Grande Dourados, BR-79804970 Dourados - Brazil
[2] Univ Catolica Dom Bosco, BR-79117900 Campo Grande - Brazil
[3] Sao Paulo State Univ UNESP, BR-17033360 Bauru - Brazil
Total Affiliations: 3
Document type: Journal article
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE; v. 164, SEP 2019.
Web of Science Citations: 0
Abstract

The annotation of large datasets is an issue whose challenge increases as the number of labeled samples available to train the classifier reduces in comparison to the amount of unlabeled data. In this context, semi-supervised learning methods aim at discovering and propagating labels to unsupervised samples, such that their correct labeling can improve the classification performance. Our proposal makes use of semi-supervised methodologies to classify an unlabeled training set that is used to train a Convolution Neural Network using different training strategies. The proposed approach is experimentally validated for soybean leaf and herbivorous pest identification using images captured by Unmanned Aerial Vehicles and can support specialists and farmers in the pest control management in soybean fields, especially when they have a limited amount of labeled samples. (AU)

FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support type: Research Projects - Thematic Grants
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 15/25739-4 - Ón “The study of semantics ín deep learning models
Grantee:Gustavo Henrique de Rosa
Support type: Scholarships in Brazil - Master
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support type: Regular Research Grants