摘要
植株数量识别是油菜机械直播效果和质量评估的关键。该文针对油菜机械直播田间植株数量检测中人工统计耗时、费工、效率低下的现实,通过自主搭建的低空无人机遥感平台采集油菜机械直播区域的遥感影像,基于超高分辨率(0.18 cm/pixel)遥感影像计算的颜色植被指数进行油菜目标识别及其形态特征信息提取。结合田间调查数据,采用逐步回归分析方法,建立了机械直播油菜在苗期的株数与遥感特征信息之间的关系。结果显示,油菜目标的株数与其外接矩形的长宽比、像素分布密度和周长栅格数具有较好的线性关系,回归模型的决定系数R^2为0.803,并通过显著性检验,其标准估计误差为0.699。模型检验结果显示,观测值与预测值之间的R^2为0.809,均方根误差RMSE为0.728。研究结果表明,利用集成超高分辨率传感器的低空无人机遥感平台,通过计算颜色植被指数并分析油菜目标数量与形态特征的相关性,能有效识别油菜机械直播的出苗株数,可为后续油菜机械直播效果的快速、准确评估提供技术支持。
Identification of plant number of rape seedlings is the key to evaluating effect and quality of mechanical planting. In terms of the long-lasting and inefficient manual statistics in plant quantity detection of rape seedlings planted mechanically, this article explored a new means to automatically identify the number by a low altitude unmanned aerial vehicles （UAV） remote sensing system with ultra-high resolution. A color vegetation index, excess green - excess red （ExG-ExR）, was chosen for image segmentation which was performed by Otsu algorithm. The features of rape plant like spectral and shape information were extracted after image segmentation. Combined with the field survey data, the research applied stepwise multiple regression analysis to build the relationship between the plant number of rape seedlings and features. The low altitude UAV used in research was integrated with an ultra-high resolution sensor, Nikon D800, an FX-format digital single lens reflex （DSLR） camera with effective pixel count of 36.3 million for definition and image quality. Its ultra-high resolution （0.18 cm/pixel） made the identification of rape seedlings plant number possible, although it captured RGB （red, green, blue） images. In fact, there were several color vegetation indices based on visible band. And the research on them had an outstanding achievement. This article compared the ExG, ExG-ExR, normalized green-red difference （NGRD） and green leaf index （GLI）, which were commonly used in the study, especially in the UAV digital image system. The result of image segmentation showed that all the color vegetation indices could be suitable for the rape seedlings area identification and extraction. In the end, ExG-ExR was chosen in this paper because it was matched with the area of rape seedlings in RGB image best, while GLI had a lot of noise. Before feature extraction, there were some post-processings for the segmentation objects, such as vectorization, buffer analysis, abnormal object elimination an
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2017年第19期115-123,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金(41201364
31501222)
中央高校基本科研业务费专项资金资助(2017JC038
2015BQ026
2014QC013)
关键词
无人机
遥感
模型
油菜
机械直播
株数
多元回归
unmanned aerial vehicles
remote sensing
models
rape
mechanical planting
plant number
multiple regression
