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.
Highlights
Underlying the global climate change and a two billion increase of world population in projected 30 years [1,2], sufficient yielding of grain crops has been considered in many countries as one of the most important issues to maintain food security
The aim of this paper is to provide a platform of unmanned aerial vehicles (UAVs) image dataset of rice paddy for data sharing by making labeled and unlabeled data findable and accessible through domain-specific repositories
This paper performed the image classification with the dataset using a convolutional neural network (CNN) algorithm, which was modified from the classical algorithm, VGG16, due to its promising classification architecture
Summary
Underlying the global climate change and a two billion increase of world population in projected 30 years [1,2], sufficient yielding of grain crops has been considered in many countries as one of the most important issues to maintain food security. Oh et al [29] applied the object detection technique to cotton seedling counting in UAV images to analyze the plant density for precision field management. The aim of this paper is to provide a platform of UAV image dataset of rice paddy for data sharing by making labeled and unlabeled data findable and accessible through domain-specific repositories. For this scope, this paper focuses on the description of the dataset, including what methods used for collecting and producing the data, where the dataset may be found, and how to use the data with useful information and a showcase
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