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Developing a small UAV platform to detect sheath blight of rice

机译：开发小型无人机平台以检测水稻的鞘枯病

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Small UAVs (Unmanned Aerial Vehicles) have high potential to be used to detect and manage the diseases of various crops because they are low cost and user-friendly. Objective of this study is to develop a UAV platform to detect sheath blight in rice in the field. A quadrotor UAV equipped with high-resolution RGB-/multispectral camera was developed and images from these sensors were collected over research plots of different rice varieties with different levels of sheath blight. The ground truth-NDVIs (Normalized Difference Vegetation Indexes) of these rice varieties were also collected. Through comparison and analysis, there appeared to have a good correlation between the ground truth-NDVIs values and the NDVI values extracting from the UAV images. The determination coefficient and Root Mean Square Error (RMSE) are 0.907 and 0.0854, respectively. The results indicate that the multispectral images even provide higher accuracy to differentiate different levels of sheath blight. Therefore, small UAV platform mounted the proper sensors can effective detect the development of sheath blight in rice.
机译：小型无人机（低成本无人机）价格低廉且易于使用，具有很高的潜力可用于检测和管理各种农作物的病害。这项研究的目的是开发一种可在田间检测水稻鞘枯病的无人机平台。开发了配备高分辨率RGB- /多光谱摄像机的四旋翼无人机，并在不同稻瘟病水平不同水稻品种的研究地块上收集了来自这些传感器的图像。还收集了这些水稻品种的地面真NDVIs（归一化植被指数）。通过比较和分析，地面真实NDVIs值与从无人机图像提取的NDVI值之间似乎具有良好的相关性。确定系数和均方根误差（RMSE）分别为0.907和0.0854。结果表明，多光谱图像甚至可以提供更高的精度来区分不同程度的鞘枯萎病。因此，在小型无人机平台上安装适当的传感器可以有效地检测水稻鞘枯病的发展。

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