An Approach to Deconvoluting Grain-Flow within a Conventional Combine Harvester using a Parametric Transfer Function

Grain flow measured at the flow sensor in a commercial yield monitoring system is considered to be the convoluted result of grain entering the harvester at one time being mixed by the internal threshing and transport processes with grain entering at other times. A transfer function describing the flow of grain within a conventional combine harvester was used to deconvolve the recorded signal using Fast Fourier Transformation (FFT). This process of time series analysis was shown to reposition the yield data in a more accurate manner than the simple time-delay process currently employed. The deconvolution increases the variability in the yield data from 10% to 17% which more closely resembles the variation in crop yield expected from small area samples. The spatial structure of yield variation obtained from small area samples was also shown to be more closely estimated by the deconvoluted data set, however the process does display sensitivity to noise introduced by mechanical/electrical sources and procedural errors.     

An Evaluation of the Response of Yield Monitors and Combines to Varying Yields

Comparisons were made between yield measurements with an impact based yield sensor and an electronic scale in adjacent harvest strips and on the same grain stream within a combine. Yield measurements in adjacent strip comparisons were more prone to errors as the segment lengths decreased. Grain yield difference between the yield sensor and electronic scale ranged from 5 to 14%, 4 to 13%, 3 to 12%, and 2 to 11% for 15, 30, 60, and 300 m long segments. The yield differences between neighboring segments might have been caused by yield variability to a degree; however, a consistent decrease in yield differences with increasing segment lengths implied that better accuracies could be obtained in longer management areas. The combine responses to grain yield changes and the effect of varying ground speed on accuracy were also evaluated by creating artificial yield patterns in harvest strips. Grain diffusion within the combine was more obvious when abrupt yield changes were introduced at known locations. Grain mixing and redistribution inside the combine may dictate the selection of segment sizes in the site-specific decision making process. Constant ground speed provided more stable grain flow values than varying ground speed. The average error in yield estimate was 3.4% and 5.2% at constant ground speed and varying speed, respectively. Careful calibration and constant combine speed were important to achieve better accuracy with the grain yield monitor.     

物联网技术在我国粮食作物生产过程中的应用进展

物联网技术在粮食作物生产过程中的应用,是粮食高产、优质、高效、生态、安全生产的要求。在作物生长监测与诊断中,利用无线传感器网络、物联网技术和视频监控系统,可以远程实时监测作物生长过程中的关键环境因子、作物长势、作物营养和病虫害等信息,从而实现信息的实时采集。为此,主要阐述了物联网技术在我国粮食生产产前、产中和产后的应用现状,分析了物联网技术在粮食作物生产中存在的主要问题,并提出了物联网技术的应用前景及发展方向。     

用流量传感器实现控制粘度加热输送工艺

由于来油组分的不断改变,原油的日输量与温度、粘度之间不可能建立准确的数学模 型,使得加热泵站不论用控制温度加热输送工艺,还是用控制粘度加热输送工艺,都不可能根据生 产任务准确地确定加热原油的温度,或加热后出站原油的粘度,这就给调度工作带来不便。由于出 站油粘度稳定和流量稳定是等效的,由此提出用流量传感器代替粘度传感器组成自动控制系统来 控制加热炉温度,以实现控制粘度加热输送工艺。根据原油的日输量能够准确地计算出原油的流 量,能极大地方便生产调度工作。分析了使用流量传感器的主要优点:可省去采样管道的设计和施 工;不必购置专用的流量传感器;如果输送途中环境温度下降,使管道中原油粘度增大、流量减小 时,泵站的流量传感器会反应出流量减小,并通过自动控制系统提高加热炉温度,使输油更加安全。 可靠。     

核子式谷物产量自动测量方法

介绍了国内外谷物产量自动测量技术的发展状况,用国产核子秤设备,进行了γ射线对谷物的辐射衰减性能试验和谷物动态流量测量试验,验证了核子技术用于谷物流量自动测量的可行性,并提出了应用于谷物联合收获机的产量实时动态测量技术方案。     

粮库检测系统的网络设计

针对大型粮库粮食存储环境监测点分散的现状，设计一种树状拓扑结构的无线传感器网络中央监测系统。该系统以ZigBee无线传输技术为核心，结合温湿度传感器模块，构成无线传感器网络检测子节点。系统能够对现场环境实时检测，同时通过路由节点将检测到的数据上传给上位机，其中路由节点采用无线传输方式与终端节点进行通讯，使得现场检测到的数据能够实时传送给中央监控计算机，最终实现粮库内部的多点检测及和实时监控。     

光电感应式油菜种子流监测装置设计与试验

针对油菜气力槽齿盘式精量排种器高速排种过程中种子流不易准确监测的问题，设计出一种光电感应式油菜种子流监测装置，对排种器的落种数量进行实时监测。该监测装置调用ARM嵌入式系统的中断，定时器资源，在时间窗口中采集回归反射型光纤传感器的输出脉冲，计算得到排种器双路各自落种数量以及落种总数，并在液晶屏上显示。选用华油杂62号作为试验对象，以排种盘转速和吸室真空度作为试验变量，以落种数量的监测值和实际值的相对误差作为试验指标，在油菜气力槽齿盘式精量排种器上进行该监测装置的实时监测试验，得到落种数量的监测值；再利用数粒仪计算落种数量的实际值，求出监测的相对误差。试验数据表明，监测值的相对误差不大于5.21%。该装置能够有效实现油菜种子的实时监测，对提高油菜气力槽齿盘式精量排种器排种性能的监测水平，推进油菜排种监测装置的研究具有重要意义。     

Grain Yield Mapping: Yield Sensing,Yield Reconstruction,and Errors

Research findings are reviewed focusing on yield sensing methods, yield reconstruction, mapping, and errors. Yield sensing methods were explained and yield mapping process was briefly introduced. Grain flow through different combines was explained and the effects of combine dynamics on yield measurement accuracy were discussed. Other errors caused by various sensors that are utilized by a yield monitor were included. It was concluded that with proper installation, calibration, and operation of yield monitors, sufficient accuracy can be achieved in yield measurements to make site-specific decisions. Nevertheless, attention must be paid when interpreting yield maps since yield measurement accuracy can vary depending upon the measurement principle, combine grain flow model, size of management area chosen, and the operator's capabilities and carefulness in following instructions to obtain the best accuracy possible under varying field operating conditions. Some of the errors can be filtered out by careful analysis of the raw yield (or flow rate) data provided by yield monitors. Researchers have focused on crop flow models to improve yield reconstruction process. A yield reconstruction algorithm that effectively handles non-linear combine dynamics has not been developed by researchers yet. More efforts towards yield reconstruction should be encouraged.     

基于GWR模型的渭北黄土旱塬粮食单产空间分异 及其影响因子分析——以陕西彬县为例

【目的】 通过探究渭北黄土旱塬区粮食单产在县域尺度上的空间分异特征及其影响因子,为小尺度粮食单产及其影响因子的空间分异研究、区域粮食单产提高提供科学依据。【方法】 应用空间自相关、最小二乘法和地理加权回归模型（GWR）,研究渭北黄土旱塬区典型粮食主产县陕西彬县粮食单产的空间分布特征及其影响因子的空间分异。【结果】 彬县粮食单产的Moran’s I指数为0.328,显著性检验的Z值为5.51,呈北高南低的局部空间集聚特征。坡度、耕层厚度、土壤有机质、道路密度和施肥成本对彬县粮食单产具有正向影响,土壤类型、侵蚀程度和地下水埋深对彬县粮食单产具有负向影响,各解释变量回归系数的相对极差范围为0.55—14.11。空间上,耕层厚度、土壤类型、侵蚀程度、土壤有机质和道路密度对彬县南部、东南部梁峁丘陵沟壑区粮食单产的影响强于北部黄土旱塬区,而坡度、地下水埋深和施肥成本则表现出相反的空间非平稳性特征。OLS模型回归系数的显著性与GWR模型回归系数的相对极差呈负相关关系。GWR模型的R ²比OLS模型提高了0.04,AIC值减少了11.04。 【结论】 彬县粮食单产之间存在显著的空间正相关关系;土壤有机质、施肥成本和地下水埋深是渭北黄土旱塬区县域粮食单产的最主要影响因子;同一影响因子在县域内的不同空间位置对粮食单产的影响程度存在较大差异,且各影响因子对粮食单产影响程度的空间非平稳性是导致OLS模型回归系数显著性水平较低的主要原因。GWR模型在空间非平稳性数据建模方面的解释能力与估计精度都优于OLS模型,且能够实现模型估计参数的空间可视化。     

Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat

Several methods were developed for the redistribution of nitrogen (N) fertilizer within fields with winter wheat (Triticum aestivum L.) based on plant and soil sensors, and topographical information. The methods were based on data from nine field experiments in nine different fields for a 3-year period. Each field was divided into 80 or more subplots fertilized with 60, 120, 180 or 240 kg N ha⁻¹. The relationships between plot yield, N application rate, sensor measurements and the interaction between N application and sensor measurements were investigated. Based on the established relations, several sensor-based methods for within-field redistribution of N were developed. It was shown that plant sensors predicted yield at harvest better than soil sensors and topographical indices. The methods based on plant sensors showed that N fertilizer should be moved from areas with low and high sensor measurements to areas with medium values. The theoretical increase in yield and N uptake, and the reduced variation in grain protein content resulting from the application of the above methods were estimated. However, the estimated increases in crop yield, N-uptake and reduced variation in grain protein content were small.     

在线谷物水分测试精度影响因素的试验研究

设计了分流式取样装置,配备无级变速的叶轮式排粮机构,在实验台上对谷物进行试验研究,分析了谷物流速、含杂率以及温度等因素对传感器输出频率的影响规律,自主研制电容式谷物水分传感器并通过烘箱法进行标定试验,试验结果表明：该传感器的水分测量范围：11%～24%,测量精度：≤0.5%;在常温下,低水分段时测量结果波动很小,基本满足在线谷物水分测量的要求。     

采棉机产量监测系统采集数据的误差分析

为了获取农田作物产量信息，建立产量分布图，在消化、吸收美国AgLeader公司棉花产量监测系统PF3000的基础上，进行了棉花测产收获试验。在收获过程中，对流量传感器、速度传感器等进行了标定，并对产量数据进行了处理。试验结果表明，现场标定可有效提高测产系统流量传感器和速度传感器的测量精度，但田问环境比较复杂，产量数据采集过程中仍会引入一些误差，需对产量数据进行误差处理。误差处理后得到的棉花产量分布图聚类性增强，较为符合实际情况。     

基于LMDI分解模型的中国粮食主产区增产格局及贡献因素研究

深入分析中国13个粮食主产省份的增产空间格局、作物结构、贡献因素,为维系其增长态势、优化粮食生产布局、调整种植结构,实现主产区粮食稳步增产提供决策依据。运用LMDI分解模型和GIS空间分析法,将主产区粮食总产量分解为播种面积和作物单产2种因素,分析其增产格局及增产贡献因素。从主产区粮食增产量的空间分布来看：北方主产区优于南方主产区,粮食增产重心呈现出较为明显的北移趋势;增产结构上看,玉米为主要贡献品种,其次为小麦,稻谷贡献率最低;主产区粮食增产贡献因素来看,单产的贡献率高于播面的贡献率,且空间差异明显。分作物看稻谷和玉米的播种面积效应为正值,玉米大于稻谷,小麦的播种面积效应产生负向影响;3种作物的单产效应均为正值,小麦的单产效应最为显著,小麦>稻谷>玉米。面积增加和单产提高都对主产区粮食产量增加产生了积极的贡献。对于主要依靠播面增加来提高产量的地区,应调整种植结构,合理轮作,挖掘优势作物,增加农业技术推广,提高土地产出;对于单产贡献显著的地区,要发展特色产业优势区,形成集群化发展,在生产、流通、销售环节提供全方位的服务,防止增产不增收的现象发生。     

The most common errors of capacitance grain moisture sensors: effect of volume change during harvest

The objective of this study was to investigate the inaccuracy of a capacitance moisture sensor mounted on a combine harvester based on the datasets of six consecutive years. Variation of sensed volume is a major cause of measurement error for a capacitive sensor. The percentage of the sensed volume occupied by grain changes continuously by filling and emptying of the grain bin, which causes a large fluctuation in sensor output during on-the-go moisture sensing. At the beginning of the bin filling process when the grain bin is empty, under-measures were recorded and when it is approximately 60 % full, large over-measures are observed compared to the actual moisture values. This effect mainly influences the precision of the recorded site-specific moisture values and causes inaccurate yield maps. To assess the effect of varying sensed volume content during harvest operation, a bin level transmitter sensor was mounted on the top of the grain bin to continuously measure the height of the grain. A clear correlation between the actual amount of material (available space) in the grain bin to the bias from the standard moisture was demonstrated. The coefficient of determination was R² = 0.86 for corn (Zea mays L.) and R² = 0.87 for winter wheat (Triticum aestivum L.). By using equations generated from the datasets of consecutive years (2008, 2009 and 2010), an effective post-correction method for the recorded data is proposed.     

Improving the accuracy of estimating grain weight by discriminating each grain impact on the yield sensor

This study was aimed at accurately estimating total weight of harvested grain on a combine by simply attaching a small yield sensor in the grain tank and by processing the output of the sensor. The yield sensor was first installed in a grain tank of a 1.2 m-swath Japanese-style (head-feeding or jidatsu) combine, and the weight was estimated from individual impulses received at each rotation of a grain-releasing device i.e. an auger blade. A non-linear relation was assumed between the weight of grain released and the impulse received, and the parameters of the non-linear model were optimized to minimize the sum of squares between the estimated and actual weight of grain accumulated at each run of the combine. A threshold for the output discriminated between actual release and no release of the grain from the auger blade. The appropriate range of the threshold was 4–6 times the root-mean squared output of the sensor without throughput (F _rms ) of grain. The aim was to enhance the accuracy of the estimation of grain weight by disregarding signals that did not relate to the accumulation of grain in the tank. Two methods of calculating the impulses were proposed after the discrimination: “successive addition” and “interval addition”, and two non-linear models of converting impulses into the weight of grain: “odd function model” and “positive function model”. The use of the odd function model with the impulse calculated by the interval addition was the most robust, and root-mean squared relative errors of calibration and validation were both stable and around 2.5 % at a threshold of 5F _rms . In the confirmatory experiment with a larger 1.8 m-swath Japanese-style grain combine equipped with the same sensor, the odd function model with the interval addition achieved root-mean squared relative error of 3.6 % at calibration and 4.4 % at validation at a threshold of 5F _rms .     

基于GPRS技术的粮库温湿度监测系统设计

针对大型粮库粮食储藏环境监测点分散的情况,设计了一种基于GPRS技术的无线粮库温湿度监测系统。该系统以MSP430单片机为控制核心,结合温湿度传感器SHT11和GPRS模块PIML-900/1800构成现场监测终端,完成信息的采集与上传;利用LabVIEW开发了监控中心管理软件。     

Grain separation loss monitoring system in combine harvester

Based on laboratory experimental results obtained with an axial threshing test-rig with tangential feeding, cumulative distribution functions of separated grain in axial and radial directions of threshing rotor were built. Based on the analysis of the relationship between grain separation loss and grain separation flux in an area under the concave, an indirect grain separation loss monitoring method is presented in this paper.Piezo-electric polyvinylidene fluoride (PVDF) film was selected as sensitive material to design a grain flux sensor. While grain and material-other-than-grain (MOG) separated in the monitoring area impact on piezo-electric PVDF films, different electric charges are generated. After signal progressing with a charge amplifier, frequency discrimination and wave shaping, the number of grain can be counted by a microcontroller (MCU) and the grain separation loss of combine harvester can be measured in real-time.Field test results indicated that the measurement errors of grain separation loss recorded by the monitoring system relative to the loss checked manually were less than 12%.     

陕西省粮食生产安全驱动因素分析

以陕西省的年鉴统计资料为依据,对其粮食生产总量、耕地面积、单产进行了分析。结果表明:粮食生产总量和单产的变化规律基本趋于一致,都呈波动增长趋势;耕地面积总体呈下降趋势,但下降趋势有所减缓。粮食生产总量由耕地面积和单产决定。制约粮食单产的主要因素有:自然灾害、农业基础设施建设与农业投入、农业生态环境、土地政策、耕地质量、农业科技水平和劳动力文化水平等。     

Development of a yield monitoring system for citrus mechanical harvesting machines

A load cell based yield monitoring system was developed for the Oxbo citrus mechanical harvesting machines. The yield monitoring system consisted of a GPS receiver, a mass flow sensor and data processing and storage units. The mass flow sensor consisted of four load cells attached to a carbon-fiber plate which sensed the impact force created by the oranges hitting the plate. A mathematical model was developed to relate the impact force to fruit mass. Laboratory tests were conducted on a test rig that replicated the flow of oranges to measure the accuracy of the system under a controlled environment. The system performed very well under laboratory conditions (R ² = 0.99 and an average error of 3.3%). In addition, a field test was conducted in a citrus orchard in Florida to evaluate the performance of the system under field conditions. Of the 72 rows used in the field test, the first 10 rows were used to calibrate the computed weight. A correlation of R ² = 0.97 between the actual weight and the computed weight was found from the field data with an average error of 7.81%.     

山东省县域粮食生产格局演变及其影响因素

利用1995—2015年山东省县域粮食产量等数据资料,运用ESDA、重心迁移模型和多元回归模型等方法,分析山东省粮食生产格局演变及其地理集聚特征,探索影响粮食生产格局变迁的关键因素。结果表明:研究时段内,山东省粮食生产波动较大,粮食产量由1995年的4 245万t减少到2002年的3 293万t,然后通过十三连增,到2015年达到4 713万t;县域尺度上,山东省粮食生产的空间集聚程度不断增强,但西部地区产量增长明显快于东部地区,重心向西移动显著,逐步形成了"西热东冷"的空间格局。研究表明,农业机械总动力、有效灌溉面积和年末总人口等因素对粮食产量有着显著地正向影响,而人均地区生产总值和农村用电量表现为负向影响,共同推动粮食生产及其空间格局的演变。因此,提出加强高标准农田建设、推动土地适度规模化发展和建立粮食主产县利益补偿机制等建议,有效保障粮食产能。