植物信息感知与自组织农业物联网系统研究
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摘要
农业物联网技术作为现代农业最前沿的发展领域之一,是当今世界发展农业信息化,实现农业可持续发展的关键和核心技术。农业物联网信息技术主要包括农业信息感知、传输与信息应用三个层面。而传统农田信息获取面临几大技术瓶颈：一是传感器技术落后,作物养分信息传感器比较鲜见,二是传统农田信息监测只是单点、静态的定时测定,无法实现实时动态检测,难于实现无人值守的农业自动化作业要求。因此,研究植物养分感知技术及关键传感器技术,研究针对大规模农田信息采集无线传输协议与深度路由机制；开发农业物联网软硬件平台已经成为现代农业亟待解决的关键问题。本研究以农业物联网的三个核心层面为研究对象,研究基于可见.近红外光谱植物养分的快速无损感知技术,研究基于FFT算法及小波变换的光谱微弱信号处理方法,并开发了植物叶片养分测定和植物冠层养分、生理信息测定的传感仪器。提出了主动诱导式大规模农业物联网的自组织网络协议和农业物联网深度路由技术,研究了农业物联网故障情况下智能路由维护方法,开发了农业物联网信息采集设备及控制系统,并成功应用到农业生产实践中。主要研究内容与创新性成果有：
(1)提出了以可见/近红外光谱技术为基础的植物养分测定方法,通过光谱数据预处理-特征波长提取-线性和非线性建模预测的光谱分析技术路径,研究并提取了13个作物养分检测特征波段和3个作物生理信息检测特征波段,开发了适用于农业物联网实时动态植物养分与生理信息检测传感器,通过实验证明,传感器氮素含量检测R2=0.8237,叶绿素检测R2=0.9361,NDVI检测R2=0.9672,LAI检测R2=0.7698。另外,研究了单点作物叶片叶绿素含量、氮含量、水分含量同时检测的方法,开发了多参数叶片养分信息检测仪器,并得到叶绿素检测R2=0.9148,氮素含量检测R2=0.9207,水分含量检测R2=0.8656。
(2)应用高灵敏度微信号输出的光电感应器作为植物养分检测的光谱信息探测器,设计了信号处理电路,研究了传感的微弱信号处理方法,应用FFT算法及小波分析方法分别对光谱信号进行滤波与微信号提取。经比较发现,小波变换处理后的信号更接近原始有用信号；而FFT算法在高频段处理与小波分析基本相同,低频段信号噪声去除效果略差于小波分析。实验结果表明,处理后信号中噪声振幅被降至0.5uv以下,信噪比提升为8db。
(3)研究适应于大规模农田信息采集的主动诱导式农业物联网自组织网络组网协议,提出了基于F-MSG,B-MSG (?)肖息事件驱动体制的组网实现方法,并在该组网协议基础上提出了自组织网络协议的最短路径路由组网(SLR)方法,资源竞争模式优化机制(X-SLR)方法,并首次提出深度路由预防(S-SLR)等优化管理方法。实验表明：SLR模式的QoS=1.13,X-SLR优化的QoS=0.14,S-SLR优化的QoS=0.23。经优化后的网络性能大幅提高。在此基础上,提出了网络深度路由的信息调度与组网管理机制,使农业物联网路由深度可达到12级,除了网络延时有所增大外,QoS=0.83,其它参数基本不变。说明网络性能良好,完全达到农业生产的实际需求。
(4)研究了自组织网络故障发生后的智能化路由维护方法,提出了基于局部网络重组与越级路由两种智能路由维护方式,通过实验表明局部网络重组路由维护对网络平均延时为5秒以内,网络丢包率低于1.5%,QoS=0.15。越级路由维护的网络丢包率控制在3%以内,平均延时为8秒,QoS=0.26。说明两种路由维护完全满足农业物联网的网络性能要求。
(5)在农业物联网信息实时获取基础上,研究了农业物联网系统与农业自动化控制装备相结的农业智能化信息管理系统,对农业园区水泵恒压控制、自动肥水管理等方法进行了研究,开发物联网信息与控制系统并在农业园区进行了应用示范。
上述研究成果为大规模田间多维信息实时动态获取、智能化低功耗远程传输及自动控制奠定了理论基础,具有广阔的应用前景。
As one of the forefront areas in the modern agriculture, the agricultural internet of things (IOT) is a key and kernel technology in the development of agricultural informatization and sustainable realization of the world. The Agricultural IOT information technology mainly includes three levels which are agricultural information perception, transmission and information application. While the traditional farmland information acquisition faces several major technical bottleneck:one is backward technology of sensors, as a result, much information of crops lacks corresponding sensors. In addition, the traditional farmland information monitoring is just the single point and static timing measurement which is unable to realize real-time dynamic detection, let alone realize the request of unattended agricultural automation. Therefore, there are some crucial problems that await to be solved urgently in modern agriculture. One is to study the technology of the key sensors and instruments, the wireless transmission agreement and depth routing mechanism based on the characteristics of agricultural IOT netting; the other is to develop the hardware and software platform of the agricultural IOT. Three core levels of agricultural IOT were the objects of this study, nondestructive determining technology of plant nutrients was discussed based on visible/near infrared spectra, and the weak signal detection and development of the corresponding sensing instruments were presented based on the FFT and wavelet transformation. In the research, it was proposed that the active induced self-organized network protocol on large-scale agricultural IOT and the depth routing technology of agricultural IOT. Also,it was investigated that how the intelligent routing maintained under fault conditions, and instruments was developed on the information collection and control systems which had already been successfully used in the agricultural production. The main creative contributions of this paper include:
(1) A determining method of plant nutrients was proposed based on visible/near infrared spectra. Through analysis methods of spectral data preprocessing-characteristic wavelengths selection-linear and nonlinear calibration modeling,13 effective wavelengths were extracted for the detection of plants nutrients, while 3 effective wavelengths were extracted for plant canopy nutrient information detection, and some sensors were developed for real-time and dynamic detection of this information. The tests of these sensors showed that R2=0.8237 for Nitrogen content detection, R2=0.9361 for chlorophyll detection, R2=0.9672 for NDVI detection, while R2=0.7698 for LAI detection. Some analytical mothods were established for the simultaneous determination of single point chlorophyll content, nitrogen content and moisture content in leaves. Some instruments were developed, and the results of tests were as follows:R2=0.9148 for chlorophyll content detection, R2=0.9207 for nitrogen content detection, and R2=0.8656 for moisture content detection.
(2) Photoelectric sensors with high-sensitivity photoelectric micro-signals outputting were applied as the spectral information detector of plant nutrients, signal circuit was designed for processing, and processing methods were investigated for output signal. Filter and weak signal extraction were done by FFT algorithm and wavelet transform analysis, respectively. Results showed that signals processed by Wavelet transform were more close to the original useful signal, it was approximately the same of FFT algorithm and wavelet transform analysis in high frequency band, and de-noising effect by wavelet transform analysis was a little better in low frequency band. As a result, the amplitudes of noise dropped below 0.5uv, while SNR (signal-to-noise) was improved to 8db.
(3) Self-organized network protocol of active induced agricultural IOT was discussed adapted to large-scale agricultural information collection, the realization method was proposed based on F-MSG, B-MSG message event-driven system networking. In this foundation, some optimal management methods were presented as the shortest path routing optimization of self-organized network protocol (SLR), optimization of routing depth defense(S-SLR), optimization mechanism of competition mode(X-SLR). And experiments showed that QoS was 1.13 for SLR, QoS was 0.14 for X-SLR, QoS was 0.23 for S-SLR. Network performances were improved. In this foundation, information scheduling of routing depth and networking management mechanism were presented, routing depth of Agricultural IOT leveled up to 12, other parameters were the same except that network delay was slightly increased as QoS=0.83. The fine network performances meet the practical demands of agricultural industry.
(4) Intelligent routing maintaining method as responses to network nodes faults was presented by self-organized network protocol technology. Two methods were proposed based on local network reorganization and leapfrog routing, and experiments showed that average network delay was below 5 seconds by routing maintenance of local network reorganization, packet loss rate of network was well controlled below 1.5%,Qos was 0.15, and packet loss rate of network by leapfrog routing maintance was controlled below 3%, average network delay was 8 seconds,QoS was 0.26. It had been proved that these two routing maintenance methods completely satisfied with the desired network performances of the agricultural IOT.
(5) In the context of real-time agricultural IOT information acquisiton, intelligent information management system of agriculture was proposed of agricultural IOT system combined with agricultural automatic control devices. The methods for constant pressure control of pump and automatic management of fertilizer and water were discussed, and some corresponding systems were developed and successfully applied in plantation as demonstration.
The studies above have laid a theoretical foundation for real-time dynamic acquisition of multi-dimensional information and intelligent low-power consumption of remote transmission and automatic control in large-scale field, and it has an extensive application prospect.
(1)提出了以可见/近红外光谱技术为基础的植物养分测定方法,通过光谱数据预处理-特征波长提取-线性和非线性建模预测的光谱分析技术路径,研究并提取了13个作物养分检测特征波段和3个作物生理信息检测特征波段,开发了适用于农业物联网实时动态植物养分与生理信息检测传感器,通过实验证明,传感器氮素含量检测R2=0.8237,叶绿素检测R2=0.9361,NDVI检测R2=0.9672,LAI检测R2=0.7698。另外,研究了单点作物叶片叶绿素含量、氮含量、水分含量同时检测的方法,开发了多参数叶片养分信息检测仪器,并得到叶绿素检测R2=0.9148,氮素含量检测R2=0.9207,水分含量检测R2=0.8656。
(2)应用高灵敏度微信号输出的光电感应器作为植物养分检测的光谱信息探测器,设计了信号处理电路,研究了传感的微弱信号处理方法,应用FFT算法及小波分析方法分别对光谱信号进行滤波与微信号提取。经比较发现,小波变换处理后的信号更接近原始有用信号；而FFT算法在高频段处理与小波分析基本相同,低频段信号噪声去除效果略差于小波分析。实验结果表明,处理后信号中噪声振幅被降至0.5uv以下,信噪比提升为8db。
(3)研究适应于大规模农田信息采集的主动诱导式农业物联网自组织网络组网协议,提出了基于F-MSG,B-MSG (?)肖息事件驱动体制的组网实现方法,并在该组网协议基础上提出了自组织网络协议的最短路径路由组网(SLR)方法,资源竞争模式优化机制(X-SLR)方法,并首次提出深度路由预防(S-SLR)等优化管理方法。实验表明：SLR模式的QoS=1.13,X-SLR优化的QoS=0.14,S-SLR优化的QoS=0.23。经优化后的网络性能大幅提高。在此基础上,提出了网络深度路由的信息调度与组网管理机制,使农业物联网路由深度可达到12级,除了网络延时有所增大外,QoS=0.83,其它参数基本不变。说明网络性能良好,完全达到农业生产的实际需求。
(4)研究了自组织网络故障发生后的智能化路由维护方法,提出了基于局部网络重组与越级路由两种智能路由维护方式,通过实验表明局部网络重组路由维护对网络平均延时为5秒以内,网络丢包率低于1.5%,QoS=0.15。越级路由维护的网络丢包率控制在3%以内,平均延时为8秒,QoS=0.26。说明两种路由维护完全满足农业物联网的网络性能要求。
(5)在农业物联网信息实时获取基础上,研究了农业物联网系统与农业自动化控制装备相结的农业智能化信息管理系统,对农业园区水泵恒压控制、自动肥水管理等方法进行了研究,开发物联网信息与控制系统并在农业园区进行了应用示范。
上述研究成果为大规模田间多维信息实时动态获取、智能化低功耗远程传输及自动控制奠定了理论基础,具有广阔的应用前景。
As one of the forefront areas in the modern agriculture, the agricultural internet of things (IOT) is a key and kernel technology in the development of agricultural informatization and sustainable realization of the world. The Agricultural IOT information technology mainly includes three levels which are agricultural information perception, transmission and information application. While the traditional farmland information acquisition faces several major technical bottleneck:one is backward technology of sensors, as a result, much information of crops lacks corresponding sensors. In addition, the traditional farmland information monitoring is just the single point and static timing measurement which is unable to realize real-time dynamic detection, let alone realize the request of unattended agricultural automation. Therefore, there are some crucial problems that await to be solved urgently in modern agriculture. One is to study the technology of the key sensors and instruments, the wireless transmission agreement and depth routing mechanism based on the characteristics of agricultural IOT netting; the other is to develop the hardware and software platform of the agricultural IOT. Three core levels of agricultural IOT were the objects of this study, nondestructive determining technology of plant nutrients was discussed based on visible/near infrared spectra, and the weak signal detection and development of the corresponding sensing instruments were presented based on the FFT and wavelet transformation. In the research, it was proposed that the active induced self-organized network protocol on large-scale agricultural IOT and the depth routing technology of agricultural IOT. Also,it was investigated that how the intelligent routing maintained under fault conditions, and instruments was developed on the information collection and control systems which had already been successfully used in the agricultural production. The main creative contributions of this paper include:
(1) A determining method of plant nutrients was proposed based on visible/near infrared spectra. Through analysis methods of spectral data preprocessing-characteristic wavelengths selection-linear and nonlinear calibration modeling,13 effective wavelengths were extracted for the detection of plants nutrients, while 3 effective wavelengths were extracted for plant canopy nutrient information detection, and some sensors were developed for real-time and dynamic detection of this information. The tests of these sensors showed that R2=0.8237 for Nitrogen content detection, R2=0.9361 for chlorophyll detection, R2=0.9672 for NDVI detection, while R2=0.7698 for LAI detection. Some analytical mothods were established for the simultaneous determination of single point chlorophyll content, nitrogen content and moisture content in leaves. Some instruments were developed, and the results of tests were as follows:R2=0.9148 for chlorophyll content detection, R2=0.9207 for nitrogen content detection, and R2=0.8656 for moisture content detection.
(2) Photoelectric sensors with high-sensitivity photoelectric micro-signals outputting were applied as the spectral information detector of plant nutrients, signal circuit was designed for processing, and processing methods were investigated for output signal. Filter and weak signal extraction were done by FFT algorithm and wavelet transform analysis, respectively. Results showed that signals processed by Wavelet transform were more close to the original useful signal, it was approximately the same of FFT algorithm and wavelet transform analysis in high frequency band, and de-noising effect by wavelet transform analysis was a little better in low frequency band. As a result, the amplitudes of noise dropped below 0.5uv, while SNR (signal-to-noise) was improved to 8db.
(3) Self-organized network protocol of active induced agricultural IOT was discussed adapted to large-scale agricultural information collection, the realization method was proposed based on F-MSG, B-MSG message event-driven system networking. In this foundation, some optimal management methods were presented as the shortest path routing optimization of self-organized network protocol (SLR), optimization of routing depth defense(S-SLR), optimization mechanism of competition mode(X-SLR). And experiments showed that QoS was 1.13 for SLR, QoS was 0.14 for X-SLR, QoS was 0.23 for S-SLR. Network performances were improved. In this foundation, information scheduling of routing depth and networking management mechanism were presented, routing depth of Agricultural IOT leveled up to 12, other parameters were the same except that network delay was slightly increased as QoS=0.83. The fine network performances meet the practical demands of agricultural industry.
(4) Intelligent routing maintaining method as responses to network nodes faults was presented by self-organized network protocol technology. Two methods were proposed based on local network reorganization and leapfrog routing, and experiments showed that average network delay was below 5 seconds by routing maintenance of local network reorganization, packet loss rate of network was well controlled below 1.5%,Qos was 0.15, and packet loss rate of network by leapfrog routing maintance was controlled below 3%, average network delay was 8 seconds,QoS was 0.26. It had been proved that these two routing maintenance methods completely satisfied with the desired network performances of the agricultural IOT.
(5) In the context of real-time agricultural IOT information acquisiton, intelligent information management system of agriculture was proposed of agricultural IOT system combined with agricultural automatic control devices. The methods for constant pressure control of pump and automatic management of fertilizer and water were discussed, and some corresponding systems were developed and successfully applied in plantation as demonstration.
The studies above have laid a theoretical foundation for real-time dynamic acquisition of multi-dimensional information and intelligent low-power consumption of remote transmission and automatic control in large-scale field, and it has an extensive application prospect.
引文
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