TM影像中基于光谱特征的棉花识别模型

为快速、准确地在遥感图像上从各种农作物中识别提取棉花作物的信息,满足大尺度、运行化棉花遥感监测系统的要求。作者在试验区的棉花主要生长期里,同期进行了棉花与其它主要农作物的地面光谱测量并采集了同期的LandsatTM图像。通过对各时期棉花及主要农作物的地面测量光谱与TM图像光谱特征的差异性及规律性分析,确定了试验区棉花遥感识别的最佳时相期为9月中下旬,研究开发了基于光谱特征的棉花识别模型。经数学分析与实际应用验证,该模型简单、操作方便并且识别的准确度较高,适用于大尺度的"新疆棉花遥感监测运行系统"。

棉花动态模拟模型研究进展(英文)

该文对棉花生理生态模型、棉花形态结构模型以及棉花结构功能模型的研究动态进行了综述,提出将形态结构和生理生态过程紧密结合的棉花结构功能模型的研究应是今后棉花模拟研究的重点,指出了目前生理生态模型和结构模型结合遇到的关键理论和技术问题:未能实现冠层微环境的精确模拟;物质分配和植株结构的结合研究还处于初步阶段;地上部分和根系耦合模拟还未实现。最后对该领域今后的研究趋势和内容进行了探讨:应基于形态结构模型进行冠层微环境的精确模拟;地上部分的物质分配可基于源-汇模型;地上部分和根系进行耦合模拟时,可将地上部分看作是“源”,根据根系各部分的“汇强”模拟物质分配和根的生长。

物联网与作物模型在智慧棉花系统中的应用与展望

在已有科技与研究成果基础上,结合数据库技术,构建基于物联网与棉花功能结构模型Cotton XL的智慧棉花生产数字化系统平台。系统利用智能传感器等物联网设备实时采集田间气象、土壤和作物长势等数据以及现场视频图像信息,通过网络传输监测范围内棉株的信息至数据库,结合模型模拟分析,实现远程操控辅助管理、预测判别、智能处理和可视化模拟等功能,从而优化作物栽培管理措施,提升资源配置效率,提高棉花产量与品质,为棉农适应现代农业信息化、智能化、高效化提供有效途径。

棉花气象研究综述之一 棉花生育模拟模型及生产管理决策系统研究进展

简要叙述了国内外棉花生长发育模拟模型、棉花专家系统和棉花生产管理决策系统的发展 ,重点介绍了美国的 GOSSYM模拟模型和我国的 COTGROW模拟模型等最成功的研究成果。

GOSSYM模拟模型在黄淮海棉区的验证

利用１９９０年（正常年型）中国衣科院棉花研究所（安阳）的试验资料，对ＧＯＳＳＹＭ进行了较全面的验证，结果表明，通过适当调整模型品种参数，ＧＯＳＳＹＭ基本上可以模拟中熟品种中棉所１２、中早熟品种中棉所１７、早熟品种中棉所１６的生长发育和产量的形成过程，并针对存在问题提出了对ＧＯＳＳＹＭ模型进行修改的建议。

基于无人机RGB图像的棉花产量估算

目前,无人机系统已应用于作物产量估算,利用无人机搭载的RGB相机在花铃期和吐絮期从3个高度(10、20和30 m)分别采集棉花冠层图像,提取图像的颜色指数和纹理特征,进而对提取的特征分别进行逐步回归分析和因子分析,筛选出重要特征并构建棉花产量估算模型。通过对比分析2个生育时期和3个高度的产量估算模型,最终确定利用RGB图像对棉花进行产量估算的最佳生育时期和最佳高度。结果表明,20和30 m高度下花铃期图像建立的产量模型拟合度以及模型精度均比吐絮期好,而40 m高度下2个生育时期的模型拟合度接近,但花铃期的验证结果不显著;对比20和30 m高度下花铃期以及40 m高度下吐絮期的产量估算模型发现,30 m高度下花铃期通过SWR方法建立的模型拟合效果最佳,由此表明,棉花产量估算的最佳生育时期为花铃期,图像采集的最佳高度为30 m。综上,利用无人机RGB图像能准确快速估算棉花产量,为基于可见光图像的棉花产量估算提供了理论和技术参考,并为其他农作物估产模型的建立提供借鉴。

新疆棉花生产区域评估系统研究

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 % 。

基于IMS的多面域棉花种植参数监测分析

为了实现棉花生长监控,基于图像处理技术设计了棉花种植监测系统。系统需要摄像头采集棉花冠层图像,叶面积仪检测叶面积指数,光谱仪检测归一化植被指数值NDVI和植被指数RVI,由于数据种类不同,基于IMS建立输出传输系统。采用图像处理方法,计算冠层覆盖度,冠层覆盖度与NDVI和RVI呈线性关系。分别建立地表植株N含量、叶面积指数、地表上植株干物质累计量和冠层覆盖度模型呈指数关系。该模型的线性系数k和指数系数b随着氮肥用量的增加而升高。同时,在不同氮肥用量下拟合决定系数保持较高水平。利用系统实际检测棉田,具有良好精度。

气候变化下新疆棉花调亏灌溉节水效果评估研究

为了验证气候变化下新疆棉花种植调亏灌溉节水效果,选取早熟、早中熟2个典型棉花种植区,采用棉花生长模型结合大气环流模式进行模拟试验。结果显示,调亏灌溉技术可有效节省用水量,与充分灌溉相比,在不影响生产力的同时,可节约农业灌溉用水5%~28%。因此,建议在新疆棉花种植时,控制灌溉用水量,结合实际情况优化调亏灌溉技术,确保棉花种植的价值与收益。

Assessment of different genetic distances in constructing cotton core subset by genotypic values

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances(Euclidean,standardized Euclidean,Mahalanobis,city block,cosine and correlation distances) combining four commonly used hierarchical cluster methods(single distance,complete distance,unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling(LDSS) method for constructing different core subsets. The analyses of variance(ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean,standardized Euclidean,Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean,Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages,which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

Development and Validation of National Cotton Cultivar Registration Index Model in Yangtze River Valley

[Objective] Based on the cotton variety high yielding potential, fiber quality traits, disease resistance, and early maturity characters, a cultivar registration index model was developed to simplify the tedious calculation process in national cotton registration procedure, and thus to enhance the practical application of cultivar regis- tration index in cotton breeding ancl cotton recommending. [Method] By means of correlation analysis, partial correlation analysis and path analysis methods, the cor- relation of cotton main properties and their effects on cultivar registration index were explored using the dataset of national cotton regional trials in Yangtze River Valley during 1996-2013. The cultivar registration index model was constructed with step- wise regression statistical technique to ascertain the quantitative relationship of main characters with cultivar registration index, and the regional cotton trial dataset in 2013 was used to validate the model. [Result] Several characters with larger deter- minants to cultivar registration index were screened out,ie. lint yield increase ratio, pro-frost yield ratio, verticillium wilt index, fiber strength, fusarium wilt index and mi- cronaire value. The cultivar registration index model defined the functional relation- ship of cultivar registration index with the selected main characters, among which lint yield increase ratio, fiber strength and micronaire value contributed most to culti- var registration index. The model validation with regional cotton trials in 2013 indi- cated the root mean square error, RMSE was only 2.77, and the variation coeffi- cient was 6.77%, which confirmed the model prediction effect was quite perfect. [Conclusion] The developed cultivar registration index model was reliable enough to simulate the complicated scoring system in cultivar registration procedure, also sim- plified cotton registration process, and enhanced the practicability of the cultivar reg- istration index.

Modeling of the Dyeing Uptake Rate for Direct Dyestuff on Cotton in Batch Dyeing Process Based on Grey System Theory

The grey system theory, with the characteristics of fewer modeling data and higher accuracy, was employed to model the batch dyeing process for the purpose of accurate online control. The GM（1, 1） and GM （0, N） models of the grey system theory were discussed for their feasibilities of modding for batch dyeing process. The combination of direct dyestuff Fast Red F3B on cotton was chosen as a representative of the common dyeing method for describing the modeling process. Firstly, the GM（ 1, 1 ） model and the GM（1, 1） combined with GM（0, N） model were employed to model the equilibrium percentage of dyeing uptake rate. Secondly, an integrated dyeing uptake rate model with three factors （ temperature, salt concentration, and pH） was established based on the adsorption rate equation. Experimental results show that this model has higher accuracy and beetler generalization ability, which can predict the results of batch dyeing process. Due to the application of grey system theory, the model has a lot of advantages, such as being easy to determine the parameter value and small amount of calculation. So it can also be suitable for the same type of combination of dyestuff-fahric by changing the parameters value only.