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A UAV Open Dataset of Rice Paddies for Deep Learning Practice
- 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.
- Subjects :
- 010504 meteorology & atmospheric sciences
Computer science
Science
0211 other engineering and technologies
02 engineering and technology
computer.software_genre
01 natural sciences
Field (computer science)
Annotation
UAV images
Data acquisition
rice seedling
open dataset
021101 geological & geomatics engineering
0105 earth and related environmental sciences
training data
business.industry
Deep learning
Perspective (graphical)
deep learning
General Earth and Planetary Sciences
Paddy field
Data mining
Data pre-processing
Artificial intelligence
business
computer
CNN
Test data
Subjects
Details
- ISSN :
- 20724292
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....e25bf74baa3e348770155ba37c9f6432
- Full Text :
- https://doi.org/10.3390/rs13071358