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1.
Abhishek Pandey S. K. Jha J. K. Srivastava R. Prasad 《Russian Agricultural Sciences》2010,36(6):428-432
Estimation of the soil moisture and soil roughness by using microwave data with less complex and fast method is a significant
area of research today. For this purpose an Artificial Neural Network (ANN) based algorithm is used and tested in present
study. The ANN model is calibrated and tested with the experimentally obtained data by using X-band scatterometer for different
field roughness 3.78, 1.83 and 1.63 cm and at fixed value of soil moisture 22.8%. The measurement of scattering coefficient
was carried out over a range of incidence angle from 20° to 70° by 5° steps for both the HH (horizontal transmitter and horizontal
receiver) and VV (vertical transmitter and vertical receiver) polarization. Two training algorithm of Feed Forward Backpropagation
neural network namely Levenberg-Marquardt (TRAINLM) and Gradient-Descent (TRAINGD) were used for analysis. The performance
of the ANN models with different algorithm is evaluated by comparing the direct measured value of soil roughness and soil
moisture with the soil roughness and soil moisture estimated by the ANN. Our work suggests that ANN model with training algorithm
(TRAINLM) is more suitable for the soil moisture and surface roughness prediction in comparison to (TRAINGD) and ANN modeling
may be the promising alternative for the soil moisture and surface roughness estimation. The main advantage of the ANN approach
for the surface roughness and soil moisture estimation is its potential for world wide reporting. 相似文献
2.
Abhishek Pandey R. Prasad J. K. Srivastava S. N. Pandey 《Russian Agricultural Sciences》2012,38(3):230-233
Monitoring of soil moisture is very important to environmental studies, including hydrology, meteorology and their interactive fields. Today back propagation artificial neural networking is a well known and widely applied mathematical model for the remote sensing applications. For the soil moisture estimation an artificial neutral network (ANN) based algorithm is implemented and tested. The ANN model is calibrated (trained) and tested with the experimentally obtained data. The experimentally data is obtained by using X-band (9.5 GHz) scatterometer for different soil moistures viz. 10, 12, 18 and 22%. The measurement of the scattering coefficient was carried out over a range of incidence angle from 20° to 70° at the step of 5° for both the HH and VV polarization. Surface roughness (i.e. root mean square height) is taken constant as 0.5 cm for the whole experimentation. The performance of the ANN model is evaluated by the direct measured soil moisture and by the soil moisture estimated by the ANN model. Our work suggests that ANN modeling for such experimentation is a promising alternative for soil moisture estimation. The advantage of the ANN approach for soil moisture estimation is that it has potential for worldwide coverage. 相似文献
3.
In many ‘real-world’ applications, a classification of large data sets, which are often also imbalanced, is difficult due to the small, but usually more interesting classes. In this study, a large data set, forest cover type classes, which is actually multi-class classification defined with seven imbalanced classes and used as a resource inventory information was analyzed and evaluated. The data set was transformed into seven new data sets and a support vector machine (SVM) was employed to solve a binary classification problem of balanced and imbalanced data sets with various sizes. In the two approaches considered, the use of distributed SVM architectures, which basically reduces the complexity of the quadratic optimization problem of very large data sets, and the use of two sampling approaches for classification of imbalanced data sets were combined and results presented. The experimental results of distributed SVM architectures show the improvement of the accuracy for larger data sets in comparison to a single SVM classifier and their ability to improve the correct classification of the minority class. 相似文献
4.
使用竹片图像实现竹片缺陷自动识别，目前深度学习可以有效地解决该类问题，但是必须使用大量样本数据做训练才能获得较高的识别准确率。当图像数量有限时，利用基于迁移学习的方法，把经过预训练的卷积神经网络模型进行迁移，即共享卷积层和池化层的权重参数，调整新网络模型的超参数，并建立一个包含4种共计6 360张竹片缺陷图像的数据库，把图片分成4种训练集测试集形式，即80%训练、20%测试；60%训练、40%测试；40%训练、60%测试；20%训练、80%测试，分别利用支持向量机SVM分类方法、深度学习方法和迁移学习方法进行训练和测试，并将这3种方法作对比。最后，通过构建竹片缺陷识别的混淆矩阵对迁移学习进行具体分析与说明。结果表明，按照80%训练、20%测试的识别准确率最高，通过迁移学习得到的竹片缺陷最高识别精度分别达到98.97%，比普通深度学习提高了11.55% ，比SVM分类方法提高了13.04%。说明迁移学习比普通深度学习和传统支持向量机SVM分类方法更适合用于小样本数据集的分类识别，并且效果优于普通深度学习和 SVM 分类方法。 相似文献
5.
基于支持向量机的水稻稻瘟病图像分割研究 总被引：2，自引：0，他引：2
水稻稻瘟病图像的分割是水稻稻瘟病自动分析与识别的关键环节,其分割效果直接影响后续处理。提出一种基于支持向量机的水稻稻瘟病病害彩色图像分割方法。首先选取叶子正常部分的像素点以及颜色相对复杂的病斑像素点作为负训练样本和正训练样本,提取像素R、G、B彩色分量作为特征向量,对支持向量机进行训练,然后在RGB空间利用训练好的支持向量机对待分割图像的所有像素点进行分类,实现水稻稻瘟病彩色图像的分割。为了获得最佳的分割效果,采用网格搜索法对径向基核函数下的不同核参数分割效果和性能进行比较与分析,确定最佳模型参数。利用此模型进行水稻稻瘟病图像分割实验,获得较好的分割精度,结果优于最大类间方差分割算法。 相似文献
6.
<正> 前言福建长汀河田公社位于汀江上游的两岸,距县城约20公里。粗晶花岗岩构成的低丘、浅丘遍布境内,高丘、低山环绕于周围,形成宽阔的盆地。全社土地面积只有354.38平方公里,而土壤侵蚀面积竟达118.10平方公里,占土地总面积的33.21％。土壤侵蚀的重点都是分布在面积广大的低丘浅丘的坡面上。这些坡面的严重侵蚀,也只不过是近百年的历史。据长汀旧县志(清道光时修撰)的记载,河田原名柳村,境内有“五通松涛”、“铁山拥翠”、 相似文献
7.
Environmental covariates are the basis of predictive soil mapping. Their selection determines the performance of soil mapping to a great extent, especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high. In this study, we proposed an integrated method to select environmental covariates for predictive soil depth mapping. First, candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge. Second, three conventional methods (Pearson correlation analysis (PsCA), generalized additive models (GAMs), and Random Forest (RF)) were used to generate optimal combinations of environmental covariates. Finally, three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate. We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China. A total of 129 soil sampling sites were collected using a representative sampling strategy, and RF and support vector machine (SVM) models were used to map soil depth. The results showed that compared to the set of environmental covariates selected by the three conventional selection methods, the set of environmental covariates selected by the proposed method achieved higher mapping accuracy. The combination from the proposed method obtained a root mean square error (RMSE) of 11.88 cm, which was 2.25–7.64 cm lower than the other methods, and an R2 value of 0.76, which was 0.08–0.26 higher than the other methods. The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties. 相似文献
8.
陆面模型为区域农田土壤墒情监测提供了很好的途径,优化选择模型的网格尺度可以最有效地的利用空间输入信息,提高计算效率。本研究以海河平原内的1°×1°(115.5～116.5°(E),38～39°(N))为研究区,运用陆面模型CLM3.0分别在(1/120)～1°的14种不同网格尺度上对2003年3—5月的土壤墒情进行了独立模拟,分析在一定精度的空间输入数据条件下,陆面模型的网格尺度在该区域春季土壤墒情模拟中的优化取值。研究表明,结合模型输入数据的空间分辨率选择合适的网格尺度,可有效地减少计算机浮点计算取舍引起的误差;网格的无限精细并不能提高模拟效果,需要依据土壤砂粒百分含量数据的精度、变程及模拟目的优化选择陆面模型的网格尺度。当仅需要获得区域的土壤墒情平均值时,网格尺度的优化取值在土壤砂粒百分含量数据变程的1.4倍附近;当需要获得区域的土壤墒情空间变异特征时,网格尺度的优化取值在土壤砂粒百分含量数据变程的28%附近;当需要获得区域的土壤墒情空间变异特征及极大值时,网格尺度的优化取值在土壤砂粒百分含量数据变程的19%附近;当需要获得区域的土壤墒情的所有空间统计特征时,网格尺度的优化取值在土壤砂粒百分含量数据的空间最小尺度附近。 相似文献
9.
基于EnMAP-Box的遥感图像分类研究 总被引：2，自引：0，他引：2
采用2007年6月云南省勐腊县TM遥感数据,利用EnMAP-box进行了支持向量机的图像分类研究,以网格搜索法寻找最优参数,在设定的范围内,求得了最优C和g参数,用此参数进行支持向量机的遥感图像土地覆盖分类。结果表明：SVM方法较最大似然分类方法具有较高的分类精度,特别是阔叶林和橡胶林的精度明显优于最大似然分类方法;对于面积较小的次要类型,2种分类方法的精度基本保持一致;SVM的总体精度相对于最大似然分类提高了11.9％。 相似文献
10.
研究土壤有机碳的尺度效应能够为区域生态环境保护和确定合理的土壤取样间距提供科学依据。采用土壤类型法估算了广东山区表层(0-20 cm)和全剖面(0-100 cm)土壤有机碳密度,选择4条采样带,获取采样间距为250 m的土壤有机碳密度序列,并利用离散小波变换工具对其进行多尺度分解,得到2×250 m、22×250 m、23×250 m、24×250 m、25×250 m和26×250 m 6个分解尺度上的小波信息,计算小波信息方差。结果表明:土壤有机碳密度具有较强的空间异质性,其空间异质性的大小受控于不同尺度下土壤有机碳密度分布格局的主导因子影响程度;整体上在大于等于1 km的尺度,其空间异质性较强;各个样带特征尺度的差异与各样带的土壤和植被类型、地貌特征以及土地利用方式、耕作管理方式等人类活动干扰强度有关。 相似文献
11.
12.
D. F. Roberts V. I. Adamchuk J. F. Shanahan R. B. Ferguson J. S. Schepers 《Precision Agriculture》2011,12(1):82-102
Active canopy sensors are currently being studied as a tool to assess crop N status and direct in-season N applications. The
objective of this study was to use a variety of strategies to evaluate the capability of an active sensor and a wide-band
aerial image to estimate surface soil organic matter (OM). Grid soil samples, active sensor reflectance and bare soil aerial
images were obtained from six fields in central Nebraska before the 2007 and 2008 growing seasons. Six different strategies
to predict OM were developed and tested by dividing samples randomly into calibration and validation datasets. Strategies
included uniform, interpolation, universal, field-specific, intercept-adjusted and multiple-layer prediction models. By adjusting
regression intercept values for each field, OM was predicted using a single sensor or image data layer. Across all fields,
the uniform and universal prediction models resulted in less accurate predictions of OM than any of the other methods tested.
The most accurate predictions of OM were obtained using interpolation, field-specific and intercept-adjusted strategies. Increased
accuracy in mapping soil OM using an active sensor or aerial image may be achieved by acquiring the data when there is minimal
surface residue or where it has been excluded from the sensor’s field-of-view. Alternatively, accuracy could be increased
by accounting for soil moisture content with supplementary sensors at the time of data collection, by focusing on the relationship
between soil reflectance and soil OM content in the 0–1 cm soil depth or through the use of a subsurface active optical sensor. 相似文献
13.
针对传统玉米种子活力等级分类方法耗时长、环境要求严格、对种子产生损伤等问题,利用红外热成像技术结合SVM算法,建立了快速、无损、高效的玉米种子活力等级分类方法。首先采用人工老化的方法将1 200粒玉米种子分组分别老化0 h,72 h,144 h。利用不同老化时间玉米种子具有不同的生理特性,通过红外热成像仪采集温度胁迫后自然冷却的玉米种子红外热像图,提取温度值作为特征。随后对玉米种子进行标准萌发实验,根据实验结果,将玉米种子分为高活力,中活力和低活力3个活力等级。将温度值作为特征,活力等级作为标签分别建立K最近邻(KNN)和支持向量机(SVM)模型并进行训练,以模型分类准确率和训练时间作为评价指标,确定较佳模型,最终通过网格搜索对选择的模型参数进行优化。结果表明基于红外热成像技术结合支持向量机(SVM)建立的模型,训练集准确率达到了92.4%,测试集准确率为91%,训练用时0.12s。该模型经过优化后训练集准确率达到了97.1%,测试集准确率达到了96.5%。 相似文献
14.
The study aims at spatial analysis of water deficit of fruit trees under semi-humid climate conditions. Differences of soil, root, and their relation with the spatial variability of crop evapotranspiration (ETa) were analyzed. Measurements took place in a six hectare apple orchard (Malus x domestica ‘Gala’) located in fruit production area of Brandenburg (latitude: 52.606°N, longitude: 13.817°E). Data of apparent soil electrical conductivity (ECa) in 25 cm were used for guided sampling of soil texture, bulk density, rooting depth, root water potential, and volumetric water content. Soil ECa showed high correlation with root depth. The readily available soil water content (RAW) was calculated considering three cases utilizing (i) uniform root depth of 1 m, (ii) measured values of root depth, and (iii) root water potential measured during full bloom, fruit cell division stage, at harvest. The RAW set the thresholds for irrigation. The ETa was calculated based on data from a weather station in the field and RAW cases in high, medium and low ECa conditions. ETa values obtained were utilized to quantify how fruit trees cope with spatial soil variability. The RAW-based irrigation thresholds for locations of low and high ECa value differed. The implementation of plant parameters (rooting depth, root water potential) in the water balance provided a more representative figure of water needs of fruit trees Consequently, the precise adjustment of irrigation including plant data can optimize the water use.
相似文献15.
以内蒙古自治区根河市根河生态站为研究区,探讨在大面积复杂林区、具有红边波段卫星数据支持下,高空间分辨率遥感影像林地类型精细分类方法。以2016年7月的RapidEye遥感影像和2017年的GF-1PMS遥感影像为主要数据源,综合利用影像的光谱特征、纹理特征与根河森林资源小班数据等辅助信息,以及2016年林地类型外业调查样本数据,分别对2种数据源采用传统的监督分类方法[最大似然法（MLC）和支持向量机法（SVM）]和基于IDL语言的ImageSVM和ImageRF分类方法进行林地类型精细识别。最后以外业调查数据和根河森林资源小班数据作为检验样本对分类结果进行精度验证,通过建立混淆矩阵对分类结果进行评价。结果表明:①ImageRF和ImageSVM等2种分类方法对林地类型信息提取精度较高。在RapidEye影像中,针叶林、阔叶林、灌木林等8种地物类型总体分类精度分别为90.26%和90.02%,Kappa系数均大于0.88。ImageSVM和ImageRF分类结果中,灌木林、针叶林和阔叶林制图精度和用户精度均高于支持向量机法和最大似然法;相对于支持向量机法和最大似然法,ImageSVM法总体分类精度分别提高了6.18%和7.06%,Kappa系数分别提高了0.07和0.08;ImageRF法总体分类精度分别提高了5.93%和6.82%,Kappa系数分别提高了0.07和0.08,能确保森林资源调查成果的精细化、准确性、高效性。②在林地类型精细识别中,携带红边波段信息的RapidEye影像比无红边波段信息的GF-1影像具有更好的识别精度和可分性。研究证明,ImageSVM和ImageRF分类方法是有效的林地类型信息精细识别方法,具有精度高和可信度高的优势,是进行复杂山区林地类型精细分类的有效手段,可满足森林资源调查、变化监测、数字更新等林业应用需求。 相似文献
16.
基于支持向量机的DNA序列分类系统的设计与实现 总被引：1，自引：0，他引：1
针对传统统计方法进行DNA序列分类时要求DNA序列样本的概率分布函数已知，但多数情况下概率分布函数未知这一问题，采用支持向量机这一新的机器学习方法对DNA序列进行分类；以VB和Matlab为主要工具开发了基于支持向量机的DNA序列分类系统。结果表明：该系统能够动态选择DNA训练样本、待测试样本．以及支持向量机模型中的参数，并根据用户的指定条件动态输出计算结果；对于预测一批已知正确分类答案的DNA序列，系统能够自动统计识别率，以观察参数变化对于算法执行结果的影响。支持向量机能够在概率分布函数未知的条件下对DNA序列进行分类。 相似文献
17.
土壤坡面侵蚀模型是当前水土流失监测预报的重要工具,明确其应用存在的问题有助于指导生产实践并提升水土流失监测与水土保持评价的科学性。采用同一数据源,对比研究三因子模型、USLE模型及CSLE模型的坡面侵蚀监测结果,揭示了3种模型侵蚀强度分级的差异性。三因子模型与USLE模型的侵蚀等级划分结果相近,而CSLE模型划分的侵蚀等级偏高。CSLE模型划分的中度及以下等级侵蚀分布在3.0°以下坡耕地,5.0°以上坡耕地以强烈及以上侵蚀等级为主且极强烈和剧烈侵蚀面积占总侵蚀面积的80%以上,3.0°～5.0°的坡耕地各侵蚀强度等级所占比例相当,5.0°可以确定为东北地区坡耕地侵蚀强度由轻变重的临界坡度。 相似文献
18.
土壤侵蚀量随着坡度增大而升高,但不成比例增长,在一定坡度范围内具有相对的稳定性。当坡度增大到26°时,土壤侵蚀量显著地高于10°,14°,18°和22°小区。坡面覆盖植被后,上述情况明显改变,且能使26°坡面侵蚀量降到允许范围。因此,提高植被覆盖度是控制土壤侵蚀最有效的措施。植被类型不同,其生效也有快慢之差,以草类最快,中耕作物次之,灌丛最慢。 相似文献
19.
【目的】分析Landsat 8 OLI卫星遥感影像数据面向农用地分类的实际应用方法和效果,以新疆奇台县南部为研究对象。【方法】使用随机森林(RF)、支持向量机(SVM)和神经网络(Neural Net)三种分类器进行研究区农用地分类对比。【结果】通过对三种分类器参数设置参数精度检验,利用上述三种算法对农用地地物分类进行精度评价,在整体分类精度中,支持向量机算法(SVM)<随机森林算法(RF)<神经网络算法(Neural Net),分类精度分别为:90.75%,94.30%和94.84%。【结论】神经网络方法(Neural Net)在该地区的农用地物整体分类上,比支持向量机(SVM)和随机森林法(RF)相比具有一定的优势,并获得较好的分类精度。 相似文献
20.
马铃薯高垄膜上覆土自然顶膜出苗栽培技术为近年来推广的一项马铃薯栽培新技术,具有自然出苗率高、产量高、杂草少等优点,其中膜上覆土的最佳土层厚度是影响马铃薯自然顶膜出苗的关键因素之一。本试验设0、1、3、5、7 cm 5个覆土厚度处理,结果表明： 3cm、5cm两个处理为最佳覆土厚度,3cm、5 cm两个处理采用的覆土厚度更有利于顶膜出苗,自然顶膜出苗率分别达到95.2%、98%,出苗整齐度分别为91.7%、91.4%,较对照产量分别增加了28.83%、27.72%,商品率分别提高了47.77%、49.64%,膜下杂草分别减少了44.74%、84.21%。同时,地膜回收也更为彻底。 相似文献