摘要: 为了便捷地采集和实时诊断农业病虫害图像,设计了一个分布式移动农业病虫害图像采集与诊断系统。该系统由多个便携式图像采集终端和一个图像处理服务器组成;其中,图像采集终端包括嵌入式相机、可伸缩的手持杆和装载控制App的手机;图像处理服务器包括农业病虫害诊断、信息记录和反馈模块等。手持杆可将安装在其前端的嵌入式相机送到人手或视觉难以企及的病虫害区域,手机可实时预览前端相机的拍摄画面和实现控制相机完成农业病虫害图像采集等功能;系统通过HTTP协议实现多个采集终端与图像处理服务器的数据交互,协同进行分布式计算,可以减少网络移动资费和服务器的负载。利用该系统对水稻纹枯病图像采集与诊断测试结果表明,该系统的图像采集终端可以便捷地采集到水稻纹枯病图像,手机端视频预览画面延时低,对相机控制命令无误,图像采集终端与服务器通信稳定,服务器端对水稻纹枯病图像处理和诊断实时,基于图像的水稻纹枯病为害等级诊断准确率为83.5%。如果服务器端加载不同的农业病虫害图像处理和诊断算法,该系统可广泛应用于各种农业病虫害图像的采集与诊断。
Abstract: Abstract: In order to easily collect images of agricultural diseases and pests and make real-time diagnose, a distributed mobile system was designed with a number of portable image collection devices and one image processing server. Each image collection device consisted of an embedded camera, a stretchable handheld pole and an Android phone equipped with an APP of control capability. The embedded camera was fixed on the end of the handheld pole via universal joints. The handheld pole could extend to about 2 m in length. The embedded camera was built upon a development board with iTOP 4412 and a set of modules, including WIFI control, camera control, image collection, H.264/JPEG coding, RTSP/RTP video transmission, GPS information collection and writing, file transfer, and image preprocessing, which were developed in Linux platform. The mobile application was developed in Android platform with a set of modules, including video streaming preview, network, image browsing and camera control. The image processing sever could receive the images from the image collection devices, record GPS information, diagnose agricultural diseases and pests, and return the diagnosis and control information of agricultural diseases and pests to the mobile phone. Among the components of this system, the handheld pole was used to deliver the embedded camera to some unreachable agricultural disease and pest area, and the mobile phone was used for browsing images and controlling camera to collect the disease and pest images. TCP/UDP protocols and SoftAp technique were used for data exchange among the embedded camera and the mobile phone, which could be independent from cable networks and wireless local area networks. HTTP protocols were used for data exchange and distributed computing among the image collection devices and the image processing server, which can reduce the mobile phone charges and the server overhead. To test the distributed mobile agricultural system, a diagnosis algorithm of damage levels of rice sheath blight was deployed to the image processing server. This algorithm mainly included image feature extraction, disease identification, disease area computation and damage level judgment. The images of rice sheath blight were collected using the image collection device in paddy fields located in China National Rice Research Institute in 2016. After the segmentation of disease area was finished in the embedded camera, the segmented images were uploaded to the image processing server. The diagnosis algorithm in the server was implemented to process these images and the diagnosis results and control information were returned to the mobile phone. The technicians or farmers could control the rice sheath blight based on the diagnosis suggestions. Our experiment indicated that the image collection device could easily collect the images of agricultural diseases and pests, especially on some places where hands and sight were hard to reach. The system could work effectively with low image browse latency, accurate camera control, reliable device-to-server communication and real-time image processing and diagnosis. The accurate rate of 83.5% was achieved to diagnose the damage levels of rice sheath blight based on our algorithm. Therefore, the system is expected to be widely applicable to agricultural disease and pest image collection and diagnosis.
[1] Rastogi A, Arora R, Sharma S. Leaf disease detection and grading using computer vision technology & fuzzy logic[C]// International Conference on Signal Processing and Integrated Networks. IEEE, 2015: 500-505. [2] Wang H, Li G, Ma Z, et al. Application of neural networks to image recognition of plant diseases[C]// International Conference on Systems and Informatics. 2012: 2159-2164. [3] Revathi P, Hemalatha M. Cotton leaf spot diseases detection utilizing feature selection with skew divergence method [J]. International Journal of Scientific Engineering and Technology. 2014, 3(1): 22-30. [4] 温芝元,曹乐平. 椪柑果实病虫害的傅里叶频谱重分形图像识别[J]. 农业工程学报,2013,29(23):159-165.Wen Zhiyuan, Cao Leping. Citrus fruits diseases and insect pest recognition based on multifractal analysis of Fourier transform spectra[J]. Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE), 2013, 29(23): 159-165. (in Chinese with English abstract) [5] 张建华,孔繁涛,李哲敏,等. 基于最优二叉树支持向量机的蜜柚叶部病害识别[J]. 农业工程学报,2014,30(19):222-231.Zhang Jianhua, Kong Fantao, Li Zhemin, et al. Recognition of honey pomelo leaf diseases based on optimal binary tree support vector machine[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(19): 222-231. (in Chinese with English abstract) [6] 许良凤,徐小兵,胡敏,等. 基于多分类器融合的玉米叶部病害识别[J]. 农业工程学报,2015,31(14):194-201.Xu Liangfeng, Xu Xiaobing, Hu Min, et al. Corn leaf disease identification based on multiple classifiers fusion[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(14): 194-201. (in Chinese with English abstract) [7] 郭青,王骊雯,董方敏,等. 基于方向一致性特征的小麦条锈病与白粉病识别方法[J]. 农业机械学报,2015,46(1):26-34.Guo Qing, Wang Liwen, Dong Fangmin, et al. Identification of wheat stripe rust and powdery mildew using orientation coherence feature[J]. Transactions of the Chinese Society of Agricultural Machinery, 2015, 46(1): 26-34. (in Chinese with English abstract) [8] 邹修国,丁为民,陈彩蓉,等. 基于改进灰度共生矩阵和粒子群算法的稻飞虱分类[J]. 农业工程学报,2014,30(10):138-144.Zou Xiuguo, Ding Weimin, Chen Cairong, et al. Classification of rice planthopper based on improved gray level co-occurrence matrix and particle swarm algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(10): 138-144. (in Chinese with English abstract) [9] 田凯,张连宽,熊美东,等. 基于叶片病斑特征的茄子褐纹病识别方法[J]. 农业工程学报,2016,32(增刊1):184-189.Tian Kai, Zhang Liankuan, Xiong Meidong, et al. Recognition of phomopsis vexans in solanum melongena based on leaf disease spot features[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016, 32(Supp.1): 184-189. (in Chinese with English abstract) [10] Hiary H A, Ahmad S B, Reyalat M, et al. Fast and accurate detection and classification of plant diseases[J]. International Journal of Computer Applications, 2011, 17(1): 31-38. [11] 肖德琴,黄顺彬,殷建军,等. 基于嵌入式应用的高分辨率农业图像采集节点设计[J]. 农业机械学报,2014,45(2):276-281.Xiao Deqin, Huang Shunbin, Yin jianjun, et al. Development of high-resolution agricultural image capture node based on embedded system[J]. Transactions of the Chinese Society of Agricultural Machinery, 2014, 45(2): 276-281. (in Chinese with English abstract) [12] Fu J, Xiao D, Deng X. Agricultural field environment high-quality image remote acquisition [J]. Ifip Advances in Information & Communication Technology, 2014, 420: 50-60. [13] Garcia-Sanchez A J, Garcia-Sanchez F, Garcia-Haro J. Wireless sensor network deployment for integrating video- surveillance and data-monitoring in precision agriculture over distributed crops [J]. Computers & Electronics in Agriculture, 2011, 75(2): 288-303. [14] Wang P, Sun P, Niu L, et al. Research of real-time image acquisition system based on ARM 7 for agricultural environmental monitoring[C]. International Conference on Remote Sensing, Environment & Transportation Engineering. 2011: 6216-6220. [15] Xiao Deqin, Huang Shunbin, Yin Jinajun, et al. High resolution vision sensor transmission control scheme based on 3G and Wi-Fi [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2015, 31(9): 167-172. [16] 熊迎军,沈明霞,孙玉文,等. 农田图像采集与无线传输系统设计[J]. 农业机械学报,2011,42(3):184-187.Xiong Yingjun, Shen Mingxia, Sun Yuwen, et al. Design on system of acquisition and wireless transmission for farmland image[J]. Transactions of the Chinese Society for Agricultural Machinery, 2011, 42(3): 184-187. (in Chinese with English abstract) [17] 沈明霞,丛静华,张祥甫,等. 基于ARM和DSP的农田信息实时采集终端设计[J]. 农业机械学报,2010,41(6):147-152.Shen Mingxia, Cong Jinghua, Zhang Xiangfu, et al. Design and implementation of terminal for agricultural data real- time acquisition based on ARM and DSP[J]. Transactions of the Chinese Society for Agricultural Machinery, 2010, 41(6), 147-152. (in Chinese with English abstract) [18] Feng X Y, Huang X Q. The research of the real-time high-resolution image acquisition based on embedded system [J]. Applied Mechanics & Materials, 2014, 631-632: 508-511. [19] Zhang Y J, Shi Z X. The implementation of single frame image acquisition system with USB camera based on embedded Linux [J]. Journal of Agricultural University of Hebei, 2014. [20] 李伟,王库,王冬,等. 基于DM355的便携式农作物图像采集仪的实现[C]// 中国农业工程学会电气信息与自动化专委会、中国电机工程学会农村电气化分会科技与教育专委会2010年学术年会. 2010.Li Wei, Wang Ku, Wang Dong, et al. Implementation of portable crop image acquisition device base on DM355[C]// Chinese Agricultural Engineering Institute of electrical information and Automation Committee, 2010 Academic Annual Meeting of Technology and Education Professional Committee, Rural Electrification Chapter, Chinese Society of Electrical Engineering, 2010. (in Chinese with English abstract) [21] Kaiyi Wang, Shuifa Zhang, Zhibin Wang, et al. Mobile smart device-based vegetable disease and insect pest recognition method[J]. Intelligent Automation & Soft Computing, 2013, 19(3): 263-273. [22] 张宜君. 农作物虫害图像采集与处理手持设备的设计[D]. 石家庄: 河北农业大学,2014.Zhang Yijun. The Design of Crop Pests Image Acquisition and Processing and Processing Portable Device[D]. Shijiazhuang: Agriculture university of Hebei, 2014. (in Chinese with English abstract) [23] 吴健. 基于ARM的嵌入式USB图像采集与处理系统[D]. 合肥: 合肥工业大学,2012.Wu Jian. Image Acquisition and Processing System Based on Embedded ARM by USB Camera[D]. Heifei: Hefei University of Technology, 2012. (in Chinese with English abstract) [24] Bradski G R, Kaehler A. Learning OpenCV - computer vision with the OpenCV library: software that sees [M]. O'Reilly Media, Inc., 2008. [25] Bradski G, Kaehler A. Learning OpenCV: Computer Vision in C++ with the OpenCV Library [M]. O'Reilly Media, Inc. 2013. [26] 管泽鑫. 基于图像的水稻病害识别方法的研究[D]. 杭州:浙江理工大学, 2010.Guan Zexin. Study on recognition method of rice disease based on image [D]. Hangzhou: Zhejiang Sci-Tech University, 2010. (in Chinese with English abstract) [27] 刁广强. 基于图像的水稻病虫害分割算法研究[D]. 杭州:浙江理工大学, 2014.Diao Guangqiang. Study on segmentation of rice disease and insect based on image [D]. Hangzhou: Zhejiang Sci-Tech University, 2014. (in Chinese with English abstract) [28] 刘涛, 仲晓春, 孙成明, 等. 基于计算机视觉的水稻叶部病害识别研究[J]. 中国农业科学, 2014, 47(4): 664-674.Liu Tao, Zhong Xiaochun, Sun Chengming, et al. Recognition of rice leaf diseases based on computer vision [J]. Scientia Agricultura Sinica, 2014, 47(4): 664-674. (in Chinese with English abstract) [29] Rastogi A, Arora R, Sharma S. Leaf disease detection and grading using computer vision technology & fuzzy logic[C]// International Conference on Signal Processing and Integrated Networks. IEEE, 2015, 500-505. [30] Wang H, Li G, Ma Z, et al. Application of neural networks to image recognition of plant diseases[C]// International Conference on Systems and Informatics. 2012, 2159-2164. [31] 景怀宇. 中文版Photoshop CS5实用教程[M]. 北京: 人民邮电出版社, 2012.相关知识
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