生態農業：農業地景生態監測及復育【土木工程學系/楊明德特聘教授】
| 論文篇名 | 英文：A UAV Open Dataset of Rice Paddies for Deep Learning Practice 中文：用於深度學習實踐的稻田無人機開放資料集 |
| 期刊名稱 | REMOTE SENSING |
| 發表年份,卷數,起迄頁數 | 2021, 13(7), no.1358 |
| 作者 | Yang, Ming-Der(楊明德); Tseng, Hsin-Hung(曾信鴻)*; Hsu, Yu-Chun(許鈺群); Yang, Chin-Ying; Lai, Ming-Hsin; Wu, Dong-Hong |
| DOI | 10.3390/rs13071358 |
| 中文摘要 | 近年來，無人機（UAV）已廣泛應用於遙感領域。對於大量無人機影像，深度學習增添煥發活力，並在農業應用中取得許多成果。用於深度學習模型訓練的流行影像資料集是為一般用途，對象、視圖和應用皆於一般場景。然而，無人機影像主要從俯視角度，具有不同的影像視角。本文提供經過驗證的 UAV 影像標釋資料集，並對這些資料在數據採集、資料預處理和 CNN 分類及展示進行了描述。資料集內容為透過一個多旋翼無人機在稻田上飛行任務的偵察程序所蒐集的資料。本文並介紹一種帶有 ExGR 指數的半自動標示方法來生成水稻幼苗的訓練資料。本研究也修改了經典的 CNN 架構 VGG-16，以應用於補秧的水稻秧苗檢測。採用訓練/測試數據的 80/20比率及 k 折交叉驗證，CNN模型的準確率隨著epoch的增加而增加，交叉驗證資料集的都達到0.99的準確率。此一水稻秧苗資料集提供訓練驗證資料、基於區塊的檢測樣本，和田地的正射鑲嵌像。 |
| 英文摘要 | 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. |
| 發表成果與本中心研究主題相關性 | 農地的人工智慧辨識 |
