共查询到19条相似文献，搜索用时 546 毫秒
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采棉机产量监测系统采集数据的误差分析 总被引：1，自引：0，他引：1
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。 相似文献
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使用Visual Basic6．0编程语言开发了YMapper产量分布图生成软件系统。研究了一种具有粗大误差数据过滤功能的局部平均插值方法，分析了产量分布图生成过程中涉及到的坐标系定义、产量数据分类与统计分析、图形配色与绘制等问题。对谷物和棉花等作物产量数据的处理结果表明，该系统能够对联合收割机测产系统记录的产量数据文件进行处理，通过插值运算将离散分布的产量数据点生成连续的产量分布图；其误差数据过滤功能能够防止产量过低和过高的粗大误差数据点参与插值运算，使产量分布图的精度得到了保证。系统能够按照用户设置的分类和着色方式，将作物产量的空间分布情况以产量数据点图或产量分布图的形式显示。并且能够对作物的产量水平进行统计分析。 相似文献
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谷物产量空间分布数据的分析与处理 总被引：2，自引：0，他引：2
从精细农业实践过程中遇到的实际问题出发,对谷物产量空间分布数据进行了分析,利用区间估计法消除误差数据,通过划分栅格利用移动加权平均法对失真数据进行了修正.对同一地块中同一产量数据在划分成不同栅格情况下对应的产量分布图的比较,得到了一种精度较高的谷物产量分布图. 相似文献
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基于Surfer软件的田间信息制图与分析 总被引：4，自引：0，他引：4
为了直观清晰地表达田间信息的空间分布状况,为农田的定位施肥等农田精细管理提供依据,利用Surfer8.0软件中提供的克立格插值法处理农田信息数据,分别绘制在不同采样方案下土壤速效磷的等值线分布图,并对其进行了定性和定量分析,确定了合理的采样方案。又分别绘制了碱解氮、速效磷、速效钾与小麦产量的三维线框图,该图直观、清晰地反映了碱解氮、速效磷、速效钾与小麦产量的空间变异性分布情况以及它们之间存在的正相关关系。 相似文献
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变量作业是精细农业的核心,信息采集和分析是实现变量作业的关键.为了实时分析采样数据,本文研制了基于掌上电脑的空间分析模块,该模块主要应用于基于掌上电脑的农田信息采集系统中,从而使基于掌上电脑的农田信息采集系统不但能实时地采集数据,而且能及时地分析数据,以此得到整个地块的产量或养分的空间分布情况,并用可视化技术,直观地显示分析结果. 相似文献
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<正> 若价格稳定,气候产量年型即成为植棉业兴衰的主要因素,如1990年我省中部棉花大丰收,1991年棉田面积猛增30%以上。棉花产量也因年际间气候年型不同而差异很大,丰产年型亩产100kg 皮棉地块屡见不鲜,减产年型保住亩产50kg 皮棉就算不错,植棉技术与投入则起着调节与稳定产量的作用。称之谓白色革命的地膜覆盖植棉技术,改变了山西中部特早熟棉区产量低而不稳的植棉状况,其最大功效是太阳能的高效利用和棉花苗期的积温效应,促进了棉苗的生长发育。由此设想在棉花生育后期,是否也可以高效利用太阳能,增进其积温效应,以缩小棉花气候产量年际变化,达到高产稳产的 相似文献
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一、前期准备1.地块选择选安装有加压滴灌系统、土质为沙壤土、前茬为棉花、土壤肥力中等地块。2.产量指标根据乐土驿镇多年来加工番茄生产情况,番茄产量呈逐步提高趋势,亨氏番茄品种及种植模式的引进使产量水平进一步提高,滴灌加工番茄667米2产量可达8吨。 相似文献
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通过在采棉机上安装AFS产量监测系统及建立广域差分GPS基准站，绘制出棉田产量空问分布图，为精准农业的实施提供田问基础数据。 相似文献
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新疆棉花生产区域评估系统研究 总被引：10，自引：0，他引：10
棉花生产区域模拟评估系统由数据库、模拟模型、空间分析与表达等部分组成 ,以棉花生长模拟模型为核心。模拟模型为光能利用模型 ,考虑光合生产、干物质分配、叶面积动态、产量形成等 ,并考虑天气、土壤、品种、管理措施的影响。模型模拟的干物质生长动态、叶面积动态与实际较接近 ,模拟莎车田间试验皮棉产量的平均拟合指数为 0 .93。模拟 1991～ 1996年区域代表地点的皮棉单产和区域平均单产的偏斜率分别为 - 0 .3%和- 2 .4 % ,说明模型可在气候变异对新疆棉花生产影响的评估中应用 相似文献
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The ability to map profit across a cotton field would enable producers to determine where money is being made or lost on their farms and to implement precise field management practices to facilitate the highest return possible on each portion of a field. Mapping profit requires knowledge of site-specific costs and revenues, including yield and price. Price varies site-specifically because fiber quality varies, so mapping fiber quality is an important component of profit mapping. To map fiber quality, the harvest location of individual cotton bales must be known, and thus a system to track the harvest location of cotton modules must be available. To this end, a wireless module-tracking system was recently developed, but automation of the system is required before it will find practical use on the farm. In Part 1 of this report, research to develop automatic triggering of wireless messages is described. In Part 2, research to enable the system to function with multiple harvesting machines of the same type in the same field - a common situation in commercial cotton farming - is described along with testing of the entire automated wireless module-tracking system (WMTS). An RFID system was incorporated, and it enabled the WMTS to correctly and consistently differentiate among various harvesting vehicles. The improved WMTS subsequently sent wireless messages to the correct machines when cotton transfers were made in the presence of multiple harvest machines. Overall testing proved that the automated WMTS worked largely as designed. When both complete and partial cotton basket dumps were simulated, the correct wireless-messaging decision was made 100% of the time. 相似文献
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The ability to map profit across a cotton field would enable producers to determine where money is being made or lost on their farms and to implement precise field management practices to facilitate the highest return possible on each portion of a field. Mapping profit requires knowledge of site-specific costs and revenues, including yield and price. Price varies site-specifically because fiber quality varies, so mapping fiber quality is an important component of profit mapping. To map fiber quality, the harvest location of individual cotton bales must be known, and thus a system to track the harvest location of cotton modules must be available. To this end, a wireless module-tracking system was recently developed, but automation of the system is required before it will find practical use on the farm. In Part 1 of this report, research to develop automatic triggering of wireless messages is described. In Part 2, research to enable the system to function with multiple harvesting machines of the same type in the same field - a common situation in commercial cotton farming - is described along with testing of the entire automated wireless module-tracking system. To automate wireless-message triggering, a sensing and control system was added to a harvester to indicate when the machine is dumping a basket load of cotton so that wireless messages can be automatically sent from the harvester to subsequent field machines. This automated system was incorporated into the existing wireless module-tracking system, field tested, and it ultimately operated as designed, without human intervention. Linking data collected with this system together with cotton classing data enabled the creation of fiber-quality maps. 相似文献
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Estimation of yield zones using aerial images and yield data from a few tracks of a combine harvester 总被引：1，自引：0，他引：1
Yield maps derived from yield mapping systems are often erroneous not only due to limitations in measuring the yield precisely
but due to insufficient consideration of the requirements of yield mapping systems in practice as well. Aerial images of cultivated
crop fields at an advanced growth stage frequently provide a spatial pattern similar to that of yield maps. Therefore, the
possibility of generating a yield map using aerial images and measured yield data of a few tracks was examined for a period
of 2 years in two fields grown with cereals. Yield zones based on Visible Atmospherically Resistant Index (VARI) values were
compared with yield zones based on measured yield data of the whole field. About half of the grid cells of a field were allocated
to the same yield zones irrespective of the mode of yield determination. Using the Kruskal–Wallis test, the data sub-sets
of measured yield within the yield zones based on the VARI values differed significantly for all tested yield zones. As a
result, the approach was successful in the case of these experimental sites. 相似文献
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It is generally accepted that aerial images of growing crops provide spatial and temporal information about crop growth conditions and may even be indicative of crop yield. The focus of this study was to develop a straightforward technique for creating predictive cotton yield maps from aerial images. A total of ten fields in southern Georgia, USA, were studied during three growing seasons. Conventional (true color) aerial photographs of the fields were acquired during the growing season in two to four week intervals. The aerial photos were then digitized and analyzed using an unsupervised classification function of image analysis software. During harvest, conventional yield maps were created for each of the fields using a cotton picker mounted yield monitor. Classified images and yield maps were compared quantitatively and qualitatively. A pixel by pixel comparison of the classified images and yield maps showed that spatial agreement between the two gradually increased in the weeks after planting, maintained spatial agreement of between 40% and 60% during weeks eight to fourteen, and then gradually declined again. The highest spatial agreement between a classified image and a yield map was 78%. The highest average agreement was 52% and occurred 9.9 weeks after planting. The visual similarity between the classified images and the yield maps were striking. In all cases, the dates with the best visual agreement occurred between eight and ten weeks after planting, and generally, during July for southern Georgia. This method offers great potential for offering cotton farmers early-season maps that predict the spatial distribution of yield. Although these maps can not provide magnitudes, they clearly show the resulting yield patterns. With inherent knowledge of past performance, farmers can use this information to allocate resources, address crop growth problems, and, perhaps, improve the profitability of their farm operation. These maps are well suited to be offered to farmers as a service by a crop consultant or a cooperative. 相似文献
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膜下滴灌对不同土壤水分棉花花铃期光合生产、分配及籽棉产量的调节 总被引：25，自引：1，他引：24
【目的】研究膜下滴灌条件下土壤水分对棉花光合物质生产、分配的调节效应,揭示不同土壤水分对棉花对产量形成的影响机制,为干旱区发展节水高产高效农业提供依据。【方法】在新疆气候生态条件下,选用对水分反应敏感性不同的新陆早10号和新陆早13号为试验材料。控制0～60 cm土壤相对含水量滴水下限分别为田间持水量55%、70%和85%,滴水上限均为田间持水量,采用气体交换和同位素示踪技术,研究花铃期不同土壤水分对叶片光合速率、14C光合产物运转和分配及产量的影响。【结果】滴水下限为55%处理土壤轻度水分亏缺,叶片光合速率低,地上部光合物质累积量少,14C光合产物输出较快、向蕾铃分配比例增加;滴水下限为70%和85%处理叶片光合速率高,地上部光合物质累积量大,但85%处理14C光合产物向营养器官分配的比例过大,最终籽棉产量以70%处理最高,85%处理次之,55%处理最低。籽棉产量水分利用效率为55%＞70%＞85%;不同品种对土壤水分的响应不同,新陆早10号在55%和70%条件下籽棉产量和水分利用效率显著低于新陆早13号,85%条件下显著高于新陆早13号。【结论】土壤水分对棉花光合物质生产、分配具有明显的调节效应,花铃期滴水下限在70%～85%有利于实现棉花高产,在55%～70%范围内,棉株能通过适应性调节,有利于提高水分利用效率。依据不同品种对土壤水分响应的差异,结合滴灌棉田土壤水分可控性强的特点,制定相应的灌溉制度,对实现滴灌棉田节水高产高效具有重要意义。 相似文献