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Research on Boundary Recognition and Extraction Method of Field Operation Area based on UAV Remote Sensing Images

机译:基于UAV遥感图像的现场操作区域边界识别与提取方法研究

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Precision agriculture is a mode of modern agricultural production and management on the basis of information technology, and it is an important way to achieve low consumption, high efficiency, fine quality and safety in agriculture. Moreover, accurate recognition and extraction of field operation area (FOA) boundary is a crucial basic data to implement precision agriculture. Due to the irregular shape, different planting ways and inconsistent size of FOA, it is difficult to recognize and extract the boundary. Therefore, the boundary of FOA based on UAV remote sensing images was classified in this paper, and a priori rule was proposed according to the boundary characteristics. Combined with the classical algorithm of image processing, a boundary recognition and extraction system was developed by LabVIEW to obtain the effective boundary of FOA. At last, the boundary recognition method and the accuracy and real-time performance of the extraction system were tested, and the result shows the method and system can accurately recognize and extract the actual boundary of FOA, and they are adapted to three different types of boundaries and image resolutions. The system has good real-time performance, and the single-frame image processing time does not exceed 100ms when the image resolution is lower than 1920*1280.
机译:精密农业是在信息技术的基础上的现代农业生产和管理模式,这是实现低消耗,高效率,农业质量和安全的重要途径。此外,精确识别和提取现场操作区域(FOA)边界是实现精密农业的重要基础数据。由于不规则的形状,不同的种植方式和FOA不一致,难以识别和提取边界。因此,根据本文对基于UAV遥感图像的FOA的边界进行了分类,并且根据边界特征提出了先验规则。结合图像处理的经典算法,LabVIEW开发了边界识别和提取系统,以获得FOA的有效边界。最后,测试了辐射系统的边界识别方法和准确性和实时性能,结果显示了方法和系统可以准确识别和提取FOA的实际边界,它们适用于三种不同类型的边界和图像分辨率。该系统具有良好的实时性能,并且当图像分辨率低于1920 * 1280时,单帧图像处理时间不超过100ms。

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