基于移动终端的稻田飞虱调查方法
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摘要
【目的】建立一种基于移动终端的稻田飞虱调查方法,以减轻测报人员劳动强度,提高稻田飞虱调查的客观性,实现稻飞虱调查结果可追溯。【方法】利用Android相机、可伸缩手持杆和装载控制相机APP的Android手机研制了稻田飞虱图像采集仪。在Android开发环境下,利用socket通信和视频编码等技术,实现Android相机的视频采集与编码模块、视频传输模块和相机命令控制模块等。利用Android NDK开发和Java web等技术,实现手机端的视频预览模块、手机控制模块、图像上传模块等。相机实时拍摄的视频将压缩成H.264格式,通过RTSP/RTP协议控制其传输至手机端。手机端通过解压缩,实现实时预览相机所拍摄的视频,并控制相机拍摄水稻茎基部飞虱图像,同时将图像传输到手机端。稻飞虱识别算法部署在云服务器上。手机端可选择稻飞虱图像上传至云服务器,云服务器运行稻飞虱自动识别算法,结果返回至手机端。【结果】基于移动终端的稻田飞虱调查方法利用手机可以实时预览相机拍摄的水稻茎基部飞虱画面,控制相机拍照。云服务器上稻飞虱自动识别算法对图像中的飞虱平均检测率为86.9%,虚警率为11.2%;对稻飞虱各虫态平均检测率为81.7%,虚警率为16.6%。【结论】基于移动终端的稻田飞虱调查方法可以便捷地采集到水稻茎基部飞虱图像,实现稻田飞虱不同虫态的识别与计数。该方法可大大减轻测报人员的劳动量,避免稻飞虱田间调查的主观性,实现稻飞虱田间调查的可追溯。
【Aim】 This study aims to design and develop a survey method based on mobile terminal for rice planthoppers in paddy fields, so as to reduce the labor intensity, improve the objectivity of rice planthopper survey in paddy fields and trace the survey results of rice planthoppers. 【Methods】 An image acquisition device for rice planthoppers in paddy fields was developed by an Android camera, a handheld extendable pole and an Android mobile phone loaded with camera APP. In the Android development environment, the video capture and encoding module, the video transport module and the camera controlling module of the Android camera were developed by the socket communication and video coding technologies. The video preview module, the mobile controlling module and the image uploading module on the mobile phone were developed by the Android NDK and Java web technologies. The video in the camera was compressed into H.264 format file in real time, which was transmitted to the mobile phone by the RTSP/RTP protocol. The phone can preview the video captured by the camera by decompression, control the camera to capture the rice planthopper images, and simultaneously transmit the images to the mobile phone. The automatic identification algorithm for rice planthoppers was deployed in the cloud server. The clients can select the rice planthopper images to upload to the cloud server. The identification results would be returned to the mobile phone after running the identification algorithm in the server. 【Results】 By using the survey method based on the mobile terminal for rice planthoppers in paddy fields, the mobile phone can preview the video of rice planthoppers at the base of rice stem captured by the camera in real time and control the camera to take pictures. The automatic identification algorithm for rice planthoppers in the cloud server can identify both the rice planthoppers in the image and their developmental stages, with the average detection rates of 86.9% and 81.7%, and the false alarm rates of 11.2% and 16.6%, respectively. 【Conclusion】 The survey method based on the mobile terminal for rice planthoppers can easily collect the images of rice planthoppers at the base of rice plants, and quickly identify and count the number of rice planthoppers in different developmental stages. This method can not only reduce the labor of forecasting technicians, but also avoid the survey subjectivity of rice planthoppers in paddy fields and realize the traceability of rice planthopper survey.
【Aim】 This study aims to design and develop a survey method based on mobile terminal for rice planthoppers in paddy fields, so as to reduce the labor intensity, improve the objectivity of rice planthopper survey in paddy fields and trace the survey results of rice planthoppers. 【Methods】 An image acquisition device for rice planthoppers in paddy fields was developed by an Android camera, a handheld extendable pole and an Android mobile phone loaded with camera APP. In the Android development environment, the video capture and encoding module, the video transport module and the camera controlling module of the Android camera were developed by the socket communication and video coding technologies. The video preview module, the mobile controlling module and the image uploading module on the mobile phone were developed by the Android NDK and Java web technologies. The video in the camera was compressed into H.264 format file in real time, which was transmitted to the mobile phone by the RTSP/RTP protocol. The phone can preview the video captured by the camera by decompression, control the camera to capture the rice planthopper images, and simultaneously transmit the images to the mobile phone. The automatic identification algorithm for rice planthoppers was deployed in the cloud server. The clients can select the rice planthopper images to upload to the cloud server. The identification results would be returned to the mobile phone after running the identification algorithm in the server. 【Results】 By using the survey method based on the mobile terminal for rice planthoppers in paddy fields, the mobile phone can preview the video of rice planthoppers at the base of rice stem captured by the camera in real time and control the camera to take pictures. The automatic identification algorithm for rice planthoppers in the cloud server can identify both the rice planthoppers in the image and their developmental stages, with the average detection rates of 86.9% and 81.7%, and the false alarm rates of 11.2% and 16.6%, respectively. 【Conclusion】 The survey method based on the mobile terminal for rice planthoppers can easily collect the images of rice planthoppers at the base of rice plants, and quickly identify and count the number of rice planthoppers in different developmental stages. This method can not only reduce the labor of forecasting technicians, but also avoid the survey subjectivity of rice planthoppers in paddy fields and realize the traceability of rice planthopper survey.
引文
Chen S,2015.The Design and Implementation of Programming Trading System.MSc Thesis,South China University of Technology,Guangzhou.[陈硕,2015.程序化交易系统的设计与实现.广州:华南理工大学硕士学位论文]
Cheng JA,Zhu JL,Zhu ZR,Zhang LG,2008.Rice planthopper outbreak and environment regulation.J.Environ.Entomol.,30(2):176-182.[程家安,朱金良,祝增荣,章连观,2008.稻田飞虱灾变与环境调控.环境昆虫学报,30(2):176-182]
Li ZC,2014.Research and Implementation of Multi-Channel Video Monitoring User Platform Based on Android.MSc Thesis,Nanjing University of Posts and Telecommunications,Nanjing.[李宗辰,2014.基于Android的多路视频监控用户平台的研究与实现.南京:南京邮电大学硕士学位论文]
Liu DY,Ding WM,Chen KJ,2011.Automatic acquisition system for insects images in field environment.Trans.Chin.Soc.Agric.Mach.,42(6):184-187.[刘德营,丁为民,陈坤杰,2011.野外环境昆虫图像自动采集装置.农业机械学报,42(6):184-187]
Liu DY,Ding WM,Zhao SQ,2009.Extracting shape and color features of rice hopper images.Acta Agric.Jiangxi.,21(5):97-98.[刘德营,丁为民,赵三琴,2009.稻飞虱图像形状和颜色特征提取的研究.江西农业学报,21(5):97-98]
Liu DY,Zhao SQ,Ding WM,Chen KJ,2012.Identification method for rice plant hoppers based on image spectral characteristics.Trans.Chin.Soc.Agric.Eng.,28(7):184-188.[刘德营,赵三琴,丁为民,陈坤杰,2012.基于图像频谱特征的稻飞虱识别方法.农业工程学报,28(7):184-188]
Ni HJ,2014.Research and implementation of message push based on Android.Res.Explor.Lab.,33(5):96-100.[倪红军,2014.基于 Android平台的消息推送研究与实现.实验室研究与探索,33(5):96-100]
Park YS,Han MW,Kim HY,Uhm KB,Park CG,Lee J,Chon TS,2003.Density estimation of rice planthoppers using digital image processing algorithm.Korean J.Appl.Entomol.,(1):57-63.
Ren JW,Lin DD,2005.Research of platform independent programming using JNI technology.Appl.Res.Comput.,(7):180-184.[任俊伟,林东岱,2005.JNI技术实现跨平台开发的研究.计算机应用研究,(7):180-184]
Schulzrinne H,Casner S,Frederick R,Jacobson V,2003.RTP:a transport protocol for real-time applications.NO.RFC 3550.
Vun N,Ooi YH,2010.Implementation of an Android phone based video streamer.In:Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber,Physical and Social Computing,18-20 Dec.2010,Hangzhou,China.IEEE Computer Society.912-915.
Wang JQ,Zhang YJ,Liu Y,2009.Rules of Investigation and Forecast for the Rice Planthopper (GB/T15794-2009).National Agro-Technical Extension and Service Center.[王建强,张跃进,刘宇,2009.稻飞虱测报调查规范(GB/T15794-2009).全国农业技术推广服务中心]
Wang YH,2017.Design and Implementation of Mobile Terminal Information Query System Based on Android.MSc Thesis,University of Electronic Science and Technology of China,Chengdu.[王彦浩,2017.基于Android移动终端信息查询系统设计与实现.成都:电子科技大学硕士学位论文]
Watcharabutsarakham S,Methasate I,Watcharapinchai N,Sinthupinyo W,Sriratanasak,W,2016.An approach for density monitoring of brown planthopper population in simulated paddy fields.In:Proceedings of the 13th International Joint Conference on Computer Science and Software Engineering (JCSSE),12-14 July,2016,Khon Kaen,Thailand.1-4.
Wenger S,2003.H.264/avc over ip.IEEE T.Circ.Syst.Vid.,13(7):645-656.
Wenger S,Hannuksela MM,Stockhammer T,Westerlund M,Singer D,2005.RTP payload format for H.264 video.No.RFC 3984.
Yao Q,Chen GT,Wang Z,Zhang C,Yang BJ,Tang J,2017.Automated detection and identification of white-backed planthoppers in paddy fields using image processing.J.Integr.Agric.,16(7):1547-1557.
Yao Q,Xian DX,Liu QJ,Yang BJ,Diao GQ,Tang J,2014.Automated counting of rice planthoppers in paddy fields based on image processing.J.Integr.Agric.,13(8):1736-1745.
Yu HB,2014.A Scheme on Aero-based Audio and Video Real Time Transmission by Android.MSc Thesis,Harbin Institute of Technology,Harbin.[于瀚博,2014.基于 Android 的机载音视频实时传输方案设计与实现.哈尔滨:哈尔滨工业大学硕士学位论文]
Zhao SQ,Ding WM,Liu DY,2009.Rice hopper shape recognition based on Fourier descriptors.Trans.Chin.Soc.Agric.Mach.,40(8):181-184.[赵三琴,丁为民,刘德营,2009.基于傅立叶描述子的稻飞虱形状识别.农业机械学报,40(8):181-184]
Zou XG,Ding WM,Chen CR,Liu DY,2014.Classification of rice planthopper based on improved gray level co-occurrence matrix and particle swarm algorithm.Trans.Chin.Soc.Agric.Eng.,30(10):138-144.[邹修国,丁为民,陈彩蓉,刘德营,2014.基于改进灰度共生矩阵和粒子群算法的稻飞虱分类.农业工程学报,30(10):138-144]
Zou XG,Ding WM,Liu DY,Zhao SQ,2013a.Recognition system of rice planthopper based on improved Hu moment and genetic algorithm optimized BP neural network.Trans.Chin.Soc.Agric.Mach.,44(6):222-226.[邹修国,丁为民,刘德营,赵三琴,2013a.基于改进Hu矩和遗传神经网络的稻飞虱识别系统.农业机械学报,44(6):222-226]
Zou XG,Ding WM,Liu DY,Zhao SQ,2013b.Classification of rice planthopper based on invariant moments and BP neural network.Trans.Chin.Soc.Agric.Eng.,29(18):171-178.[邹修国,丁为民,刘德营,赵三琴,2013b.基于4种不变矩和BP神经网络的稻飞虱分类.农业工程学报,29(18):171-178]
Cheng JA,Zhu JL,Zhu ZR,Zhang LG,2008.Rice planthopper outbreak and environment regulation.J.Environ.Entomol.,30(2):176-182.[程家安,朱金良,祝增荣,章连观,2008.稻田飞虱灾变与环境调控.环境昆虫学报,30(2):176-182]
Li ZC,2014.Research and Implementation of Multi-Channel Video Monitoring User Platform Based on Android.MSc Thesis,Nanjing University of Posts and Telecommunications,Nanjing.[李宗辰,2014.基于Android的多路视频监控用户平台的研究与实现.南京:南京邮电大学硕士学位论文]
Liu DY,Ding WM,Chen KJ,2011.Automatic acquisition system for insects images in field environment.Trans.Chin.Soc.Agric.Mach.,42(6):184-187.[刘德营,丁为民,陈坤杰,2011.野外环境昆虫图像自动采集装置.农业机械学报,42(6):184-187]
Liu DY,Ding WM,Zhao SQ,2009.Extracting shape and color features of rice hopper images.Acta Agric.Jiangxi.,21(5):97-98.[刘德营,丁为民,赵三琴,2009.稻飞虱图像形状和颜色特征提取的研究.江西农业学报,21(5):97-98]
Liu DY,Zhao SQ,Ding WM,Chen KJ,2012.Identification method for rice plant hoppers based on image spectral characteristics.Trans.Chin.Soc.Agric.Eng.,28(7):184-188.[刘德营,赵三琴,丁为民,陈坤杰,2012.基于图像频谱特征的稻飞虱识别方法.农业工程学报,28(7):184-188]
Ni HJ,2014.Research and implementation of message push based on Android.Res.Explor.Lab.,33(5):96-100.[倪红军,2014.基于 Android平台的消息推送研究与实现.实验室研究与探索,33(5):96-100]
Park YS,Han MW,Kim HY,Uhm KB,Park CG,Lee J,Chon TS,2003.Density estimation of rice planthoppers using digital image processing algorithm.Korean J.Appl.Entomol.,(1):57-63.
Ren JW,Lin DD,2005.Research of platform independent programming using JNI technology.Appl.Res.Comput.,(7):180-184.[任俊伟,林东岱,2005.JNI技术实现跨平台开发的研究.计算机应用研究,(7):180-184]
Schulzrinne H,Casner S,Frederick R,Jacobson V,2003.RTP:a transport protocol for real-time applications.NO.RFC 3550.
Vun N,Ooi YH,2010.Implementation of an Android phone based video streamer.In:Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber,Physical and Social Computing,18-20 Dec.2010,Hangzhou,China.IEEE Computer Society.912-915.
Wang JQ,Zhang YJ,Liu Y,2009.Rules of Investigation and Forecast for the Rice Planthopper (GB/T15794-2009).National Agro-Technical Extension and Service Center.[王建强,张跃进,刘宇,2009.稻飞虱测报调查规范(GB/T15794-2009).全国农业技术推广服务中心]
Wang YH,2017.Design and Implementation of Mobile Terminal Information Query System Based on Android.MSc Thesis,University of Electronic Science and Technology of China,Chengdu.[王彦浩,2017.基于Android移动终端信息查询系统设计与实现.成都:电子科技大学硕士学位论文]
Watcharabutsarakham S,Methasate I,Watcharapinchai N,Sinthupinyo W,Sriratanasak,W,2016.An approach for density monitoring of brown planthopper population in simulated paddy fields.In:Proceedings of the 13th International Joint Conference on Computer Science and Software Engineering (JCSSE),12-14 July,2016,Khon Kaen,Thailand.1-4.
Wenger S,2003.H.264/avc over ip.IEEE T.Circ.Syst.Vid.,13(7):645-656.
Wenger S,Hannuksela MM,Stockhammer T,Westerlund M,Singer D,2005.RTP payload format for H.264 video.No.RFC 3984.
Yao Q,Chen GT,Wang Z,Zhang C,Yang BJ,Tang J,2017.Automated detection and identification of white-backed planthoppers in paddy fields using image processing.J.Integr.Agric.,16(7):1547-1557.
Yao Q,Xian DX,Liu QJ,Yang BJ,Diao GQ,Tang J,2014.Automated counting of rice planthoppers in paddy fields based on image processing.J.Integr.Agric.,13(8):1736-1745.
Yu HB,2014.A Scheme on Aero-based Audio and Video Real Time Transmission by Android.MSc Thesis,Harbin Institute of Technology,Harbin.[于瀚博,2014.基于 Android 的机载音视频实时传输方案设计与实现.哈尔滨:哈尔滨工业大学硕士学位论文]
Zhao SQ,Ding WM,Liu DY,2009.Rice hopper shape recognition based on Fourier descriptors.Trans.Chin.Soc.Agric.Mach.,40(8):181-184.[赵三琴,丁为民,刘德营,2009.基于傅立叶描述子的稻飞虱形状识别.农业机械学报,40(8):181-184]
Zou XG,Ding WM,Chen CR,Liu DY,2014.Classification of rice planthopper based on improved gray level co-occurrence matrix and particle swarm algorithm.Trans.Chin.Soc.Agric.Eng.,30(10):138-144.[邹修国,丁为民,陈彩蓉,刘德营,2014.基于改进灰度共生矩阵和粒子群算法的稻飞虱分类.农业工程学报,30(10):138-144]
Zou XG,Ding WM,Liu DY,Zhao SQ,2013a.Recognition system of rice planthopper based on improved Hu moment and genetic algorithm optimized BP neural network.Trans.Chin.Soc.Agric.Mach.,44(6):222-226.[邹修国,丁为民,刘德营,赵三琴,2013a.基于改进Hu矩和遗传神经网络的稻飞虱识别系统.农业机械学报,44(6):222-226]
Zou XG,Ding WM,Liu DY,Zhao SQ,2013b.Classification of rice planthopper based on invariant moments and BP neural network.Trans.Chin.Soc.Agric.Eng.,29(18):171-178.[邹修国,丁为民,刘德营,赵三琴,2013b.基于4种不变矩和BP神经网络的稻飞虱分类.农业工程学报,29(18):171-178]