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    王林惠, 甘海明, 岳学军, 兰玉彬, 王健, 刘永鑫, 凌康杰, 岑振钊. 基于图像识别的无人机精准喷雾控制系统的研究[J]. 华南农业大学学报, 2016, 37(6): 23-30. DOI: 10.7671/j.issn.1001-411X.2016.06.004
    引用本文: 王林惠, 甘海明, 岳学军, 兰玉彬, 王健, 刘永鑫, 凌康杰, 岑振钊. 基于图像识别的无人机精准喷雾控制系统的研究[J]. 华南农业大学学报, 2016, 37(6): 23-30. DOI: 10.7671/j.issn.1001-411X.2016.06.004
    shu
    WANG Linhui, GAN Haiming, YUE Xuejun, LAN Yubin, WANG Jian, LIU Yongxin, LING Kangjie, CEN Zhenzhao. Design of a precision spraying control system with unmanned aerial vehicle based on image recognition[J]. Journal of South China Agricultural University, 2016, 37(6): 23-30. DOI: 10.7671/j.issn.1001-411X.2016.06.004
    Citation: WANG Linhui, GAN Haiming, YUE Xuejun, LAN Yubin, WANG Jian, LIU Yongxin, LING Kangjie, CEN Zhenzhao. Design of a precision spraying control system with unmanned aerial vehicle based on image recognition[J]. Journal of South China Agricultural University, 2016, 37(6): 23-30. DOI: 10.7671/j.issn.1001-411X.2016.06.004
    shu

    基于图像识别的无人机精准喷雾控制系统的研究

    Design of a precision spraying control system with unmanned aerial vehicle based on image recognition

      摘要
    • 摘要:
      目的  针对传统的植保无人机喷雾作业时化肥农药浪费大，利用率低，造成环境污染的问题，研制一种基于图像识别的无人机精准喷雾控制系统。
      方法  利用中值滤波算法对田间航拍图像进行去噪，采用分层K_means硬聚类算法实现对农田航拍图像的分割，提取非作物区域的颜色、纹理特征空间的22个特征参数，设计支持向量机分类器进行分类识别。根据优选的17个特征参数，利用以径向基函数作为核函数的支持向量机对非作物区域图像进行识别，并根据识别结果控制喷头, 实现精准喷雾。
      结果  测试样本的识别率可达为76.56%，在无干扰风场情况下，当P阀为10%时，减施率可达32.7%。
      结论  本系统为农业航空精准喷雾控制技术的应用提供了参考方向和决策支持。

       

      Abstract:
      Objective In order to improve the efficiency and utilization of conventional unmanned aerial vehicle (UAV) spraying in fertilizer and pesticide applications, an variable rate UAV spraying system was developed based on image recognition.
      Method Median filter was applied to the images for denoising. K_means clustering algorithm was then used to segment the UAV images to extract 22 texture features and the color of non-crop region. Support vector machine (SVM) classifier was designed for classification. According to the 17 selected characteristic parameters, the non-crop region was recognized through the SVM classifier with Radial basis function (RBF) as the kernel function. Finally, precision spraying was achieved with controllable nozzles based on the recognition results.
      Result The recognition accuracy reached up to 76.56%. In undisturbed wind farm, the reduction rate reached 32.7% with the threshold P of 10%.
      Conclusion This research can serve as reference guides for application of precise spraying control technology in agricultural aviation.

       

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