作物长势监测仪数据采集与分析系统设计及应用

doi:10.12133/j.smartag.2019.1.4.201910-SA002

Smart Agriculture ›› 2019, Vol. 1 ›› Issue (4): 91-104.doi: 10.12133/j.smartag.2019.1.4.201910-SA002

• 智能装备与系统 • 上一篇

作物长势监测仪数据采集与分析系统设计及应用

王娇娇^1,^2,³, 徐波^1,^2,⁴, 王聪聪^1,^2,⁴, 杨贵军^1,^2,⁴, 杨忠³, 梅新³, 杨小冬^1,^2,⁴()

1. 农业农村部农业遥感机理与定量遥感重点实验室，北京农业信息技术研究中心，北京 100097
2. 国家农业信息技术工程研究中心，北京 100097
3. 湖北大学资源环境学院，湖北武汉, 430062
4. 北京农业物联网工程技术研究中心，北京 100097

收稿日期:2019-10-30 修回日期:2019-12-11 出版日期:2019-10-30
基金资助:
国家自然科学基金(41771469);国家重点研发计划(2016YFD0300602);北京市农林科学院科技创新能力建设专项(KJCX20170423)
作者简介:王娇娇（1997－），女，硕士，研究方向：农业遥感，Email：wangjj@stu.hubu.edu.cn。
通信作者:

Design and application of data acquisition and analysis system for CropSense

Wang Jiaojiao^1,^2,³, Xu Bo^1,^2,⁴, Wang Congcong^1,^2,⁴, Yang Guijun^1,^2,⁴, Yang Zhong³, Mei Xin³, Yang Xiaodong^1,^2,⁴()

1. Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Faculty of Resources and Environmental Science of Hubei University, Wuhan 430062, China
4. Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, China

Received:2019-10-30 Revised:2019-12-11 Online:2019-10-30

摘要/Abstract

摘要：

针对中小农场对作物长势快速监测与精确诊断的需求，本研究设计了作物长势监测仪（CropSense）数据采集与分析系统，该系统实现了数据采集、处理、分析和管理的一体化集成。系统通过蓝牙技术连接智能手机和作物长势监测仪获取作物采样数据，经服务器中内置光谱模型计算得到地块的作物生长参数分布专题图。依据地块预期产量指标，可提供可视化的专家决策处方。用户只需点击一次按钮，即可实时获取田间作物的监测诊断信息和专业的田间管理指导方案。目前系统已在多个研究机构实验农场试用，其中在小汤山基地的应用示例结果显示：在玉米大喇叭口期使用该系统进行作物诊断和指导施肥，比传统的施肥方案减少约16.67%施肥量。该系统具有采集分析数据高效便利、推荐施肥方案优化合理等特点，在中国家庭农场快速增长的背景下，具有广阔的应用前景。

关键词: 数据采集, 长势分析, 实时诊断, 专家决策

Abstract:

In view of the demand of small and medium-sized farms for rapid monitoring and accurate diagnosis of crop growth, the National Engineering Research Center for Information Technology in Agriculture (NERCITA) designed a crop growth monitoring device which named CropSense. It is a portable crop health analysis instrument based on dual-channel high-throughput spectral signals which derived from the incident and reflected light intensity of the crop canopy at red and near-infrared bands. This paper designed and implemented a data collecting and analyzing system for CropSense. It consisted of a mobile application for collecting data of CropSense and a server-side system for data and model management. The system implemented data collecting, processing, analyzing and management completely. The system calculated normalized differential vegetation index (NDVI) based on the two-channels spectral sampling data from CropSense which connected smart phone by Bluetooth, then generated crop growth parameters about nitrogen content, chlorophyll content and Leaf Area Index with the built-in spectral inversion model in the server. Meanwhile, it calculated vegetation coverage, density and color content by images captured from the camera of smart phone. When we finished the sampling program, it generated growth parameter thematic maps by Kriging interpolation based on all sampling data of the selected fields. Considering the target yield of the plot, it could provide expert advice visually. Users could get diagnostic information and professional guiding scheme of crop plots immediately after collecting data by touch a button. Now the device and system have been applied in some experimental farms of research institutes. This paper detailed application of the system in XiaoTangShan farm of NERCITA. Compared with the traditional corn flare period samples and fertilize schemes, users could avoid errors caused by manual recording. Besides, with the same corn yield, the fertilization amount has reduced 16.67% when using the generation of the variable fertilization scheme by this system. The result showed that the system could get the crop growth status efficiently and produced reasonable fertilization. The system collected and analyzed crop growth efficiently and conveniently. It is suitable for various farmers without expertise to obtain the information of the crop growth timely and can guide them to operate more effectively and economically in the field. The system saved data to web server through the Internet which improved the shortcoming of poor sharing in the traditional data exporting mode. This system is practical and promising, and it will be widely applied in the explosion of family farms in China.

Key words: data collection, growth analysis, real-time diagnosis, expert decision

中图分类号:

TP391

王娇娇, 徐波, 王聪聪, 杨贵军, 杨忠, 梅新, 杨小冬. 作物长势监测仪数据采集与分析系统设计及应用[J]. 智慧农业, 2019, 1(4): 91-104.

Wang Jiaojiao, Xu Bo, Wang Congcong, Yang Guijun, Yang Zhong, Mei Xin, Yang Xiaodong. Design and application of data acquisition and analysis system for CropSense[J]. Smart Agriculture, 2019, 1(4): 91-104.

参考文献

1	王利民, 刘佳, 唐鹏钦, 等 . 农作物长势遥感监测需求、系统框架及业务应用[J]. 中国农业信息, 2019, 31(2): 1-10.
1	Wang L , Liu J , Tang P , et al . Demand, system framework and operational application of crop growth remote sensing monitoring[J]. China Agricultural Informatics, 2019, 31(2): 1-10.
2	朱姜蓬，岑海燕，何立文，等 . 农情监测多旋翼无人机系统开发及性能评估[J]. 智慧农业， 2019， 1(1): 43-52.
2	Zhu J , Cen H , He L , et al . Development and performance evaluation of a multi-rotor unmanned aircraft system for agricultural monitoring[J]. Smart Agriculture, 2019, 1(1): 43-52.
3	倪军, 姚霞, 田永超, 等 . 便携式作物生长监测诊断仪的设计与试验[J]. 农业工程学报, 2013, 29(6): 150-156.
3	Ni J , Yao X , Tian Y , et al . Design and experiments of portable apparatus for plant growth monitoring and diagnosis[J]. Transactions of the CSAE, 2013, 29(6): 150-156.
4	卢少林, 倪军, 曹卫星, 等 . 基于主动光源的作物生长信息监测仪的设计与试验[J]. 农业工程学报, 2014, 30(23): 199-206.
4	Lu S , Ni J , Cao W , et al . Design and experiment for crop growth information monitoring instrument based on active light source[J]. Transactions of the CSAE, 2014, 30(23): 199-206.
5	马浚诚, 刘红杰, 郑飞翔, 等 . 基于可见光图像和卷积神经网络的冬小麦苗期长势参数估算[J]. 农业工程学报, 2019, 35(5): 183-189.
5	Ma J , Liu H , Zheng F , et al . Estimating growth related traits of winter wheat at seedling stages based on RGB images and convolutional neural network[J]. Transactions of the CSAE, 2019, 35(5): 183-189.
6	兰玉彬，邓小玲，曾国亮 . 无人机农业遥感在农作物病虫草害诊断应用研究进展[J]. 智慧农业，2019，1(2): 1-19.
6	Lan Y , Deng X , Zeng G . Advances in diagnosis of crop diseases, pests and weeds by UAV remote sensing[J]. Smart Agriculture, 2019, 1(2): 1-19.
7	王利民, 杨玲波, 刘佳, 等 . GF-1和MODIS影像冬小麦长势监测指标NDVI的对比[J]. 作物学报, 2018, 44(7): 1043-1054.
7	Wang L , Yang L , Liu J , et al . Comparison of growth monitoring index NDVI between GF-1 and MODIS images in winter wheat[J]. Acta Agronomica Sinica, 2018, 44(7): 1043-1054.
8	侯学会, 隋学艳, 姚慧敏, 等 . 基于物候信息的山东省冬小麦长势遥感监测[J].国土资源遥感, 2018, 30(2): 171-177.
8	Hou X , Sui X , Yao H , et al . Study of the growth condition of winter wheat in Shandong Province based on phenology[J]. Remote Sensing for Land & Resources, 2018, 30(2): 171-177.
9	杨北方, 韩迎春, 毛树春, 等 . 基于数字图像的棉花长势空间变异分析[J]. 棉花学报, 2015, 27(6): 534-541.
9	Yang B , Han Y , Mao S , et al . Analysis of the spatial variability in cotton growth conditions based on digital Imaging[J]. Cotton Science, 2015, 27(6): 534-541.
10	陈仲新，郝鹏宇，刘佳，等 . 农业遥感卫星发展现状及我国监测需求分析[J]. 智慧农业，2019，1(1): 32-42.
10	Chen Z , Hao P , Liu J , et al . Technical demands for agricultural remote sensing satellites in China[J]. Smart Agriculture, 2019, 1(1): 32-42.
11	李修华, 李民赞, 崔笛, 等 . 基于双波段作物长势分析仪的东北水稻长势监测[J]. 农业工程学报, 2011, 27(8): 206-210.
11	Li X , Li M , Cui D , et al . Monitoring of rice plant growth in Northeast China using dual-wavebands crop growth analyzer[J]. Transactions of the CSAE, 2011, 27(8): 206-210.
12	叶思菁, 朱德海, 姚晓闯, 等 . 基于移动GIS的作物种植环境数据采集技术[J]. 农业机械学报, 2015, 46(9): 325-334.
12	Ye S , Zhu D , Yao X , et al . Mobile GIS based approach for collection of crop planting environment data[J]. Transactions of the CSAM, 2015, 46(9): 325-334.
13	马旭颖, 张智勇, 高德华, 等 . 基于云服务架构的田间信息采集与分析系统设计[J]. 农业机械学报, 2019, 50(S1): 122-127.
13	Ma X , Zhang Z , Gao D , et al . Design of crop information storage analysis system based on cloud service architecture[J]. Transactions of the CSAM, 2019, 50(S1): 122-127.
14	Liu H , Sun H , Mao B , et al . Development of a crop growth detecting system[J]. Ifac Papersonline, 49(16): 138-142.
15	Matsah M I , Kusky T . Fieldlog: GIS software as a mapping aid for structural geologists[J]. Computer Methods in the Geosciences, 1996, 15(96): 471-475.
16	Turner L . PenMap demonstration project , landslide mapping system[R]. California, 2002.
17	Abdullah A F , Muhadi N A . GIS data collection for oil palm (DaCOP) mobile application for smart phone[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2015， II-2/W 2： 165-168.
18	Wagtendonk A , Reus N D , Lammeren R J A V , et al . A mobile GIS application for crop mapping development and evaluation[EB/OL]. [2019-10-02]. .
19	杨忠 . 基于移动GIS的作物生长状况采集系统研究与实现[D]. 武汉：湖北大学, 2018.
19	Yang Z . Research and implementation of crop growth status collection system based on mobile GIS[D]. Wu Han: Hubei University, 2018.
20	孙丽娟 . 基于Android的物联网数据采集监测系统开发[D]. 南京：南京理工大学, 2016.
21	朱云, 史力超, 侯振安, 等基于NDVI的滴灌春小麦氮肥分期施用推荐模型 [J]. 麦类作物学报, 2015, 35(5): 681-686.
21	Zhu Y , Shi L , Hou Z , et al . Nitrogenous fertilizer recommendation model based on NDVI for drip-irrigated spring wheat[J]. Journal of Triticeae Cropsm, 2015, 35(5): 681-686.
22	刁兴良，杨再洁，李奇峰，等 . 基于多源数据的华北平原夏玉米种植区研究[J]. 智慧农业，2019，1(2): 73-84.
22	Diao X , Yang Z , Li Q ,et al . Regionalization research of summer corn planting in North China Plain based on multi-source data[J]. Smart Agriculture, 2019, 1(2): 73-84.
23	梅方权, 吴宪章, 姚长溪, 等 . 中国水稻种植区划[J]. 中国水稻科学, 1988(03): 97-110.
24	赵广才 . 中国小麦种植区划研究(一)[J]. 麦类作物学报, 2010, 30(5): 886-895.
24	Zhao G . Study on Chinese wheat planting regionalization (Ⅰ)[J]. Journal of Triticeae Crops, 2010, 30(5): 886-895.
25	李海涛, 邵泽东 . 空间插值分析算法综述[J]. 计算机系统应用, 2019, 28(7): 1-8.
25	Li H , Shao Z . Review of spatial interpolation analysis algorithm[J]. Computer Systems & Applications, 2019, 28(7): 1-8.

作物长势监测仪数据采集与分析系统设计及应用

Design and application of data acquisition and analysis system for CropSense

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