A UAV Open Dataset of Rice Paddies for Deep Learning Practice

Remote Sensing

Ming-Hsin Lai¹  Dong-Hong Wu¹  Chin-Ying Yang²  Yu-Chun Hsu³  Ming-Der Yang³  Hsin-Hung Tseng³ 

[1] Crop Science Division, Taiwan Agricultural Research Institute, Taichung 413, Taiwan;Department of Agronomy, National Chung Hsing University, Taichung 40227, Taiwan;Department of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402, Taiwan;

关键词: open dataset;    deep learning;    CNN;    training data;    UAV images;    rice seedling;   

DOI  :  10.3390/rs13071358

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown