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A UAV Open Dataset of Rice Paddies for Deep Learning Practice

Authors :
Yu-Chun Hsu
Hsin-Hung Tseng
Ming-Hsin Lai
Dong-Hong Wu
Chin-Ying Yang
Ming-Der Yang
Source :
Remote Sensing, Vol 13, Iss 1358, p 1358 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

Recently, unmanned aerial vehicles (UAVs) have been broadly applied to the remote sensing field. For a great number of UAV images, deep learning has been reinvigorated and performed many results in agricultural applications. The popular image datasets for deep learning model training are generated for general purpose use, in which the objects, views, and applications are for ordinary scenarios. However, UAV images possess different patterns of images mostly from a look-down perspective. This paper provides a verified annotated dataset of UAV images that are described in data acquisition, data preprocessing, and a showcase of a CNN classification. The dataset collection consists of one multi-rotor UAV platform by flying a planned scouting routine over rice paddies. This paper introduces a semi-auto annotation method with an ExGR index to generate the training data of rice seedlings. For demonstration, this study modified a classical CNN architecture, VGG-16, to run a patch-based rice seedling detection. The k-fold cross-validation was employed to obtain an 80/20 dividing ratio of training/test data. The accuracy of the network increases with the increase of epoch, and all the divisions of the cross-validation dataset achieve a 0.99 accuracy. The rice seedling dataset provides the training-validation dataset, patch-based detection samples, and the ortho-mosaic image of the field.

Details

ISSN :
20724292
Volume :
13
Database :
OpenAIRE
Journal :
Remote Sensing
Accession number :
edsair.doi.dedup.....e25bf74baa3e348770155ba37c9f6432
Full Text :
https://doi.org/10.3390/rs13071358