基于高分辨率无人机影像的喷药除草效果评估
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摘要
针对快速检测农药喷洒效果和农药利用率问题,通过设计1组喷药对比试验,分析不同区域杂草的颜色特征(RGB)、颜色空间转换特征(HLS、HSV)、纹理特征(mean、variance等)、可见光波段植被指数(EXG、EXR、VDVI等),比较各个特征及指数在喷药和对照区的差异,筛选出最优特征或指数进行阈值分割;在此基础上,计算喷药前后杂草像元数评估无人机喷药的除草效果。试验结果表明,利用归一化过红、过绿植被指数之差(EXG-EXR)可有效检测喷药除草效果,该方法能够为提高农田喷药效果提供技术支持。
引文
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[2]李冰,刘镕源,刘素红,等. 基于低空无人机遥感的冬小麦覆盖度变化监测[J]. 农业工程学报,2012,28(13):160-165.
[3]Zhang C H,Kovacs J M. The application of small unmanned aerial system for precision agriculture:a review[J]. Precision Agriculture,2012,13(6):693-712.
[4]Everaerts J. The use of unmanned aerial vehicles(UAVs)for remote sensing and mapping[J]. Remote Sensing and Spatial Information Sciences,2008,144:1187-1191.
[5]汪沛,罗锡文,周志艳,等. 基于微小型无人机的遥感信息获取关键技术综述[J]. 农业工程学报,2014,30(18):1-12.
[6]王玲,兰玉彬,Hoffmann W C,等. 微型无人机低空变量喷药系统设计与雾滴沉积规律研究[J]. 农业机械学报,2016,47(1):15-22.
[7]汪小钦,王苗苗,王绍强,等. 基于可见光波段无人机遥感的植被信息提取[J]. 农业工程学报,2015,31(5):152-159.
[8]Vapnik V N. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks,1999,10(5):988-999.
[9]郭鹏,武法东,戴建国,等. 基于无人机可见光影像的农田作物分类方法比较[J]. 农业工程学报,2017,33(13):112-119.
[10]Miao R H,Tang J L,Chen X Q. Classification of farmland images based on color features[J]. Journal of Visual Communication and Image Representation,2015,29(2):138-146.
[11]Sklenicka P. Classification farmland ownership fragmentation as a cause of land degradation:a review on typology,con-sequences,and remedies[J]. Land Use Policy,2016,57(3):694-701.
[12]Mesas-Carrascosay F J,Notario-Garcia M D,de Larriva J E M,et al. Validation of measurements of land plot area using UAV imagery[J]. International Journal of Applied Earth Observation and Geoinformation,2014,33(5):270-279.
[13]Mao W H,Wang Y M,Wang Y Q. Real time detection of between-row weeds using machine vision[C]. Las Vegas,Nv July,2003.
[14]龙满生,何东健. 玉米苗期杂草的计算机识别技术研究[J]. 农业工程学报,2007,23(7):139-144.
[15]杨柳,陈延辉,岳德鹏,等. 无人机遥感影像的城市绿地信息提取[J]. 测绘科学,2017,42(2):59-64.
[16]江洪,汪小钦,吴波,等. 地形调节植被指数构建及在植被覆盖度遥感监测中的应用[J]. 福州大学学报(自然科学版),2010,38(4):527-532.