基于遥感技术的烟草花叶病监测研究

发布时间：2018-05-25 09:57

本文选题：烟草花叶病 + 高光谱　；参考：《中国烟草学报》2016年01期

【摘要】：为实现烟草花叶病快速、无损监测。基于染病植株冠层高光谱数据,优选病情指示变量,利用偏最小二乘法建立病害程度估测模型,并进一步将模型应用于资源3号卫星遥感数据,建立烟草花叶病病害等级分布图。结果显示:1)以比值植被指数(RVI),差值植被指数(DVI),再归一化植被指数(RDVI),变换植被指数(TVI),土壤可调植被指数(SAVI)作为烟草花叶病病情的指示因子,能有效估测花叶病的严重程度,决定系数达0.8165。2)在烟草花叶病病害等级分布图上,随机抽取140个样点进行实测,并将实测值与估测值进行线性拟合,拟合优度R2达77.13%。因此该方法能实现对烟草花叶病的大范围实时监测。
[Abstract]:In order to realize fast and nondestructive monitoring of tobacco mosaic disease. Based on the hyperspectral data of infected plant canopy, the disease indication variables were selected, and the disease degree estimation model was established by partial least square method, and the model was further applied to the remote sensing data of Resource-3 satellite. To establish the distribution map of tobacco mosaic disease. The results showed that the ratio vegetation index (RVI), the difference vegetation index (DVI), the normalized vegetation index (RDVI), the transformed vegetation index (TVI) and the soil adjustable vegetation index (SAVI) were used as indicators of tobacco mosaic disease, and the severity of tobacco mosaic disease could be estimated effectively. The coefficient of determination was 0.8165.2) on the distribution map of tobacco mosaic disease grade, 140 samples were randomly selected and measured, and the measured values were fitted linearly with the estimated values, and the goodness of fit R2 was 77.13%. Therefore, this method can be used to monitor tobacco mosaic disease in a wide range of real-time.
【作者单位】：山东农业大学资源与环境学院;山东临沂烟草有限公司;中国农业科学院烟草研究所;
【基金】：国家自然科学基金(No:41171425) 山东省烟草公司重点项目资助(No:2014-7-1) 上海烟草集团公司重点科技项目(No:SZBCW2013-01140)
【分类号】：S435.72;S127

【共引文献】