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    地力夏提•依马木, 周建平, 许燕, 等. 基于Logistic算法与遥感影像的棉花虫害监测研究[J]. 华南农业大学学报, 2022, 43(2): 87-95. DOI: 10.7671/j.issn.1001-411X.202106004
    引用本文: 地力夏提•依马木, 周建平, 许燕, 等. 基于Logistic算法与遥感影像的棉花虫害监测研究[J]. 华南农业大学学报, 2022, 43(2): 87-95. DOI: 10.7671/j.issn.1001-411X.202106004
    shu
    DILIXIATI Yimamu, ZHOU Jianping, XU Yan, et al. Cotton pest monitoring based on Logistic algorithm and remote sensing image[J]. Journal of South China Agricultural University, 2022, 43(2): 87-95. DOI: 10.7671/j.issn.1001-411X.202106004
    Citation: DILIXIATI Yimamu, ZHOU Jianping, XU Yan, et al. Cotton pest monitoring based on Logistic algorithm and remote sensing image[J]. Journal of South China Agricultural University, 2022, 43(2): 87-95. DOI: 10.7671/j.issn.1001-411X.202106004
    shu

    基于Logistic算法与遥感影像的棉花虫害监测研究

    Cotton pest monitoring based on Logistic algorithm and remote sensing image

      摘要
    • 摘要:
      目的  借助多光谱遥感影像和Logistic算法，实现对棉田虫害的田间监测。
      方法  以患虫害棉花区域为研究对象，利用无人机获取棉田多光谱遥感影像，并对影像进行预处理；结合受虫害棉花光谱特征，利用虫害敏感波段反射率与植被指数构建Logistic回归模型，开展棉花虫害识别监测研究。
      结果  由土壤调节植被指数(Soil adjusted vegetation index，SAVI)模型和归一化植被指数(Normalized vegetation index，NDVI)模型构建的棉蚜虫、棉红蜘蛛、棉铃虫识别模型为最优模型，其训练样本准确率达到93.7%，测试样本准确率达到90.5%，召回率为96.6%，F1值为93.5%，对棉蚜虫、棉红蜘蛛和棉铃虫的识别模型决定系数分别为0.942、0.851和0.663。
      结论  该模型可满足棉田中棉蚜虫、棉红蜘蛛和棉铃虫3种虫害的发生区域识别，且可基本满足棉田精准植保作业相关要求。

       

      Abstract:
      Objective  The purpose of this article is to monitor cotton pest in field based on Logistic algorithms and multi-spectral remote sensing images.
      Method  The cotton areas with insect pests were selected as the research object. The multi-spectral remote sensing images of cotton field were acquired by UAV, and then pre-processed. Based on the spectral characteristics of cotton pests, the Logistic regression model was constructed by the reflectivity of pest-sensitive band and vegetation index to identify and monitor cotton pests.
      Result  The cotton aphid, cotton red spider mite, and cotton bollworm identification models constructed by the soil adjusted vegetation index (SAVI) model and the normalized vegetation index (NDVI) model were the optimal models, and their accuracy for training sample and test sample reached 93.7% and 90.5% respectively the recall rate and F1 value were 96.6% and 93.5% respectively and the determination coeffecients of recognition models for three types of pests were 0.942, 0.851 and 0.663 respectively.
      Conclusion  This model can identify the occurrence area of cotton aphid, cotton red spider mite and cotton bollworm, which can basically meet the requirements of precision plant protection operation in cotton field.

       

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