Developing Pedotransfer Functions for Predicting FC and PWP

Using pedotransfer functions (PTF) is a useful way for field capacity (FC) and permanent wilting point (PWP) prediction. The aim of this study was to model PTF to estimate FC and PWP using regression tree (RT) and stepwise multiple linear regressions (SMLR). For this purpose, 165 and 45 soil samples from UNSODA and HYPRES datasets were used for development and validation of new PTFs, respectively. %Clay, geometric mean diameter (dg), and bulk density (BD) were selected as predictor variables due to the highest correlation and lowest multicollinearity. The results showed that clay percentage with W* = 0.89 and dg with W* = ?0.57 were the most effective variables to predict PWP and FC, respectively. The RT method had a better performance (R² = 0.80, ME = ?0.002 cm³cm^?3, RMSE = 0.05 cm³cm^?3 for FC and R² = 0.85, ME = 0.003 cm³cm^?3, RMSE = 0.03 cm³ cm^?3 for PWP) than SMLR in estimation of FC and PWP.     

应用自适应神经模糊推理系统(ANFIS)的ET0预测

参照作物腾发量是计算作物需水量和进行灌溉预报的基础要素。该文利用自适应神经模糊推理系统(ANFIS)所具有的直接通过模糊推理实现输入层与输出层之间非线性映射能力，和神经网络的信息存储和学习能力，将其应用于参照作物腾发量预测中。根据相关分析，输入变量选择日照时数和日最高气温；用5年共1827个数据组对系统进行训练，建立了参照作物腾发量预测系统。利用该系统对近年213个数据组进行了实际预测，与Penman-Monteith方法计算结果进行比较，结果相关性良好。     

基于GBRT模型的湖南县域农村居民点整治潜力预测

针对现有农村居民点整治潜力预测研究存在的评价方法主观性强、预测结果可靠性缺乏事实证据支持等问题,该研究利用土地利用时空数据和梯度提升回归树(gradient boosted regression trees, GBRT)方法构建农村居民点整治潜力预测模型,自动识别区域自然和社会经济多因素综合作用下的农村居民点整治潜力释放规律,并以湖南省为案例区开展了实证研究。精度验证结果表明,模型的回归预测R2为0.976 5,平均绝对百分比误差为11.64%,预测精度总体能满足规划决策支持的需要。根据模型预测：1）2020－2035年,湖南省农村居民点复垦整治潜力总规模约为36 050.26 hm2,占2020年现状农村建设用地规模的4.58%,且预测结果与湖南省各县域单元历史整治潜力释放特征基本相符,表明预测结果具有较好的可行性。2）湖南省潜力规模较大的区域主要分布在地形平缓、交通便利、城镇化水平较高的\     

Comparative Analysis of MLR,ANN, and ANFIS Models for Prediction of Field Capacity and Permanent Wilting Point for Bafra Plain Soils

ABSTRACT

Soil hydraulic parameters like moisture content at field capacity and permanent wilting point constitute significant input parameters of various biophysical models and agricultural practices (irrigation timing and amount of irrigation to be applied). In this study, the performance of three different methods (Multiple linear regression – MLR, Artificial Neural Network – ANN and Adaptive Neuro-Fuzzy Inference System – ANFIS) with different input parameters in prediction of field capacity and permanent wilting point from easily obtained soil characteristics were compared. Correlation analysis indicated that clay content, sand content, cation exchange capacity, CaCO₃, and organic matter had significant correlations with FC and PWP (p < .01). Validation results revealed that the ANN model with the greatest R² and the lowest MAE and RMSE value exhibited better performance for prediction of FC and PWP than the MLR and ANFIS models. ANN model had R² = 0.83, MAE = 2.36% and RMSE = 3.30% for FC and R² = 0.81, MAE = 2.15%, RMSE = 2.89% for PWP in training dataset; R² = 0.80, MAE = 2.27%, RMSE = 3.12% for FC and R² = 0.83, MAE = 1.84%, RMSE = 2.40% for PWP in testing dataset. Also, Bayesian Regularization (BR) algorithm exhibited better performance for both FC and PWP than the other training algorithms.     

用毛细吸渗原理快速测量土壤田间持水量的研究

田间持水量是衡量田间土壤保持水分能力的重要指标,视为对作物有效的土壤水分的上限,对农田灌溉和作物水分管理具有十分重要意义。该研究提出了能快速测量土壤田间持水量的新型毛细吸渗法。该方法以田间持水量为无地下水影响下土壤基质所能吸持的最大含水量为物理基础,设计了一套满足田间持水量要求的实验方法、装置和程序,采用5种不同土壤测量了其田间持水量,并与用威尔科克斯法测量得到的田间持水量进行了对比。结果表明：所提出的新型测量方法,无论是基本原理还是室内试验均可行。与威尔科克斯法相比,其测量值略微偏小;两种测量值相关性很好,相差约为9％,产生这种现象的原因主要是土壤的吸湿过程与脱湿过程中土壤含水率的滞后所致。与传统的测量方法相比,该方法可大大缩短测量时间,具有利用前景。     

棉花虫害诊断系统的设计与Web实现

为了推广棉花虫害诊断知识,满足更多棉农对领域专家诊断的需求,该文利用Java和Web技术,并以SQL Server2000为数据库开发工具,设计了一套网络化的棉花虫害诊断系统,实现了借助互联网对棉花虫害进行远程诊断的目标。该系统引入了模糊推理与案例相结合的方法,对棉花虫害的诊断及原有知识获取方法进行改进,提高了棉花虫害诊断的准确度和效率,弥补了单纯运用案例检索方法诊断的不足;系统不仅提供了知识浏览、案例查询、专家咨询等功能,可为棉农提供快速、方便、准确的智能查询服务,而且可通过专家咨询功能,向棉农提供在线远程会诊服务。     

Estimation of Soil Cation Exchange Capacity using Multiple Regression,Artificial Neural Networks,and Adaptive Neuro-fuzzy Inference System Models in Golestan Province,Iran

Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) provide an alternative by estimating soil parameters from more readily available data. In this article, multilayer perceptron (MLP) and radial basis function (RBF) of ANN and ANFIS models were described to estimate soil cation exchange capacity and compared to traditional multiple regression (MR). Moreover, to test the accuracy of previous functions that estimate cation exchange capacity (CEC), five pedotransfer functions (PTFs) were surveyed. The results showed that the accuracies of ANN and ANFIS models were similar in relation to their statistical parameters. It was also found that ANFIS model exhibited greater performance than RBF, MLP, MR, and PTFs to estimate soil CEC, respectively. Finally, sensitivity analysis was conducted to determine the most and the least influential variables affecting soil CEC. The performance comparisons of used models showed that the soft computing system is a good tool to predict soil characteristics.     

Comparison of marker- and pedigree-based methods for estimating heritability in an agroforestry population of Vitellaria paradoxa C.F. Gaertn. (shea tree)

We implemented a regression-based method between pairwise relatedness estimated from markers and phenotypic similarity to estimate heritability of traits related to leaf size and morphology in a wild tree population (Vitellaria paradoxa C.F. Gaertn.: shea tree). We then compared the results with heritability estimated with a classical pedigree-based method. We tested both approaches in an agroforestry population of this tree species, a very important one and abundant in the Sudano-Sahelian zone of Africa. Twelve microsatellite loci were used to estimate pairwise relatedness after selection of estimator coefficients based on Monte Carlo simulation. The regression-based method applied to 200 individuals did not display a significant trend with physical distance between trees for relatedness as well as for actual variance of relatedness. In consequence, estimates of narrow-sense heritability of traits related to leaf size were not significant. The pedigree-based method using a progeny test with 39 families and 15 individuals per family from the same population showed high and significant estimates of narrow-sense heritability for the same traits (h² = 0.36–0.95), demonstrating a marked genetic variation within the population. This discrepancy between methods stresses the poor performance of the molecular marker-based method. This can be explained by the absence of fine-scale structure within the agroforestry population of shea trees, other parameters being consistent with recommended values. The regression-based method does not seem well adapted to the agroforestry tree population. New experiments in tree populations and theoretical approaches are needed to evaluate the real potential of the marker-based methods.     

故障树分析在水产养殖监控系统可靠性设计中的应用

简单介绍了水产工厂化养殖计算机监控系统的组成及工作原理，该系统是一个由工业控制计算机作为上位计算机、单片机控制系统作为下位计算机的典型计算机集散控制系统。为保证系统工作的安全可靠性，增强系统的实用性，以鱼类因水体溶解含氧量严重超限而死亡作为顶事件为例，进行了故障树分析，得到了提高系统可靠性的基本思路：即利用单片机和上位机的软硬件冗余资源来构成容错系统，通过改变故障树的拓扑结构来减少低阶最小割集数，以降低顶事件发生的概率。通过对故障树的评价，提出了提高系统可靠性的基本方法，主要有：减少人为操作失误；单片机系统硬件进行可靠性设计；单片机和上位机的控制系统软件中增加和完善纠错程序和故障诊断程序。理论分析和试验结果表明，采用文中所述方法能有效消除系统中由于部分硬件故障、软件缺陷、人为失误、以及受外界电磁干扰等不良因素所带来的不利影响，保证水产养殖的正常进行。     

无弧有载自动调容配电变压器切换策略及其控制系统设计

通过对有载调容变压器经济运行方式的分析,阐述了一种计算临界经济容量的方法,设计了一套由数据采集模块、负荷预测模块、切换策略以及晶闸管触发模块等组成的无弧有载调容配电变压器的控制系统,提出了一种基于灰色理论负荷预测模型的切换策略。该切换策略克服了仅凭经验确定有载调容变压器临界经济容量的非科学性与不合理性等缺点,避免了负荷由于短时波动而引起调容开关频繁切换,延长了调容开关的使用寿命。通过对某单班制企业的实际数据的负荷预测与切换过程的仿真分析,验证了该控制策略的有效性。通过计算分析可知,SZ11-M-T-315（100）型调容变压器年综合损耗电量节约1 821 kW·h,节电率达15.79%。     

基于嵌入式组件技术的精准农业农田信息采集系统的设计与实现

基于精确地理位置的农田信息采集是实施精准农业的基础工作。介绍了一种基于掌上电脑和DGPS/背夹式GPS设备的农田信息采集系统的开发过程。该系统在Microsoft eMbedded Visual C++ 3.0集成开发环境下,采用嵌入式GIS开发组件,实现了掌上电脑环境下GPS、GIS功能的集成。系统由GPS实时通讯和数据处理模块、基于WinCE的基本GIS功能模块和农田信息采集功能模块等组成模块,能够实现与DGPS设备或背夹式GPS设备的实时通讯和定位数据的解析,实现了矢量农田地理信息的显示、操作、查询等基本GIS功能,同时,系统能够采集农田地物分布和多种影响作物生长的环境差异性信息。该文还介绍了使用Microsoft数据库访问组件对象ADOCE对Pocket Access数据库的操作方法,实现了对嵌入式农田信息采集系统中农田信息的有效管理。     

Estimation of Soil Infiltration and Cation Exchange Capacity Based on Multiple Regression,ANN (RBF,MLP), and ANFIS Models

Investigation of soil properties such as cation exchange capacity (CEC) and soil infiltration is an important role in environmental research. The measurement of these parameters is time-consuming and costly. In this study, intelligence-based models [artificial neural networks (MLP and RBF), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression (MR) techniques] are employed as alternatives to estimate the CEC and soil infiltration parameters from more readily available soil data. Two hundred soil samples were collected from soil 0–30 cm deep from two sites of the Ghoshe Region in Semnan Province, Iran. The first site was a flood plain and second site was agriculture land. The input data for models were electrical conductivity (EC), soil texture, lime percentage, sodium adsorption ratio (SAR), and bulk density. To evaluate the performance of these models, the statistical parameters such as root mean square error (RMSE), mean absolute error (MAE), mean error (ME), and coefficient of determination (R²) were used. Then the results of the intelligence-based models and MR were compared to each other’s. The results show that the MLP model was better than ANFIS, MR, and RBF models. Finally, sensitivity analysis was conducted to determine the most and the least influential variables affecting the soil infiltration and CEC parameters. It was found that EC and bulk density have respectively the most and the least effect on soil infiltration, and for CEC they were clay percentage and bulk density, respectively.     

Enhancing pedotransfer functions with environmental data for estimating bulk density and effective cation exchange capacity in a data‐sparse situation

Soil bulk density (BD) and effective cation exchange capacity (ECEC) are among the most important soil properties required for crop growth and environmental management. This study aimed to explore the combination of soil and environmental data in developing pedotransfer functions (PTFs) for BD and ECEC. Multiple linear regression (MLR) and random forest model (RFM) were employed in developing PTFs using three different data sets: soil data (PTF‐1), environmental data (PTF‐2) and the combination of soil and environmental data (PTF‐3). In developing the PTFs, three depth increments were also considered: all depth, topsoil (<0.40 m) and subsoil (>0.40 m). Results showed that PTF‐3 (R²; 0.29–0.69) outperformed both PTF‐1 (R²; 0.11–0.18) and PTF‐2 (R²; 0.22–0.59) in BD estimation. However, for ECEC estimation, PTF‐3 (R²; 0.61–0.86) performed comparably as PTF‐1 (R²; 0.58–0.76) with both PTFs out‐performing PTF‐2 (R²; 0.30–0.71). Also, grouping of data into different soil depth increments improves the estimation of BD with PTFs (especially PTF‐2 and PTF‐3) performing better at subsoils than topsoils. Generally, the most important predictors of BD are sand, silt, elevation, rainfall, temperature for estimation at topsoil while EVI, elevation, temperature and clay are the most important BD predictors in the subsoil. Also, clay, sand, pH, rainfall and SOC are the most important predictors of ECEC in the topsoil while pH, sand, clay, temperature and rainfall are the most important predictors of ECEC in the subsoil. Findings are important for overcoming the challenges of building national soil databases for large‐scale modelling in most data‐sparse countries, especially in the sub‐Saharan Africa (SSA).     

履带式收割机全田块路径跟踪算法设计与试验

针对中小型传统收割机无人化作业需求,该研究设计了一套后装式全田块自动驾驶系统。以沃得锐龙单边制动转向型履带式收割机为平台,搭建手自兼容自动驾驶系统,并进行系统特性辨识试验,明确其测控性能限制。针对测控性能限制设计一种PD-Fuzzy-BangBang组合路径跟踪算法,并进行样机集成与试验。水泥地面直线行驶试验表明,组合算法相较单一PD算法的上线距离缩短57.3%,稳态标准差缩小81.3%。全田块模拟试验证明,组合算法在理想条件下的路径跟踪最大偏差为6.00 cm,标准差为2.42 cm,样机具备全田块自动驾驶功能。实际水田收割作业试验证明,样机在车速0.7 m/s条件下,上线过程的路径跟踪最大偏差为12.00 cm,标准差为 6.18 cm,全田块不漏割,割幅利用率大于80%,满足田间作业需求。     

基于全视域GA-SVR模型的鱼类行为双目视觉观测系统标定

针对大视场水下环境鱼类行为视觉观测系统较难准确标定的问题,该文以双目立体测量系统为例,提出一种基于全视域GA-SVR(genetic algorithm-support vector regression)模型的鱼类行为三维观测系统标定方法。该方法选用具有圆点靶标的方形标定板为标定工具,通过设计具备前后左右移动能力的简易滑动轨道,实现了标定板的全视域空间定位。然后利用HALCON算子获取标定板靶点二维坐标,联立标定板空间位置,构建训练样本集。选取SVR模型对样本集进行训练,对比不同的寻优算法对支持向量回归模型的参数组合寻优结果,选用最优参数分别建立X,Y,Z轴标定模型。试验结果表明,利用遗传算法进行参数寻优构建的标定模型,其X、Y、Z轴测量均方误差分别为0.959、0.893和4.381 mm,互相关系数分别为0.999 988,0.999 998和0.998 356,优于差分进化算法和粒子群算法参数寻优的标定结果。与传统标定方法比较,该方法单点测量均方误差为1.861 mm,距离测量均方误差为0.706 mm,均低于空气中标定方法(单点均方误差27.75 mm;距离均方误差10.188 mm)和水下测量标定方法(单点均方误差8.215 mm;距离均方误差2.832 mm)的标定结果,有效的提高了鱼类行为视觉观测系统的定位精度。该研究可为鱼类行为量化方法研究和优化提供理论支持和技术参考。     

车辆半主动悬架系统的设计与试验研究

设计了可调阻尼减震器,通过台架试验,分析其动态特性,并建立了可调减振器阻尼与步进电机转角之间的关系,为半主动悬架控制系统设计提供了依据。在对半主动悬架系统进行数值仿真的基础上,开发了以C8051F005单片机为主控件的半主动悬架模糊控制系统,并进行了台架和实车道路试验。结果表明,设计的半主动悬架及其控制系统性能稳定,可靠,汽车行驶平顺性明显优于传统被动悬架,为半主动悬架的实际应用奠定了基础。     

利用径向基函数神经网络通过高光谱数据估算植被生物物理参数

The radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reffectance (350 to 2500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m^-2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reffectance (R) and its three different transformations, the first derivative reffectance (D1), the second derivative reffectance (D2) and the log-transformed reflectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reffectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.     

长期水稻-大麦轮作体系土壤供氮能力与作物需氮量研究

通过18年稻麦轮作,7个施肥处理,研究了水稻-大麦轮作系统中土壤生产力、氮素自然供应能力、作物氮素内部利用率及氮肥表观利用率。结果表明,在水旱轮作下,土壤对大麦产量的地力贡献率平均为69%,水稻为75%～81%; 肥料的增产贡献率分别为31%和19%～25%,可维持每年生产大麦2.3 t/hm2、稻谷6～7 t/hm2。土壤氮素自然供给力在大麦上平均为75.9%,比水稻的低3.3%～7.2%。在一年三熟水旱轮作制中,土壤和环境年供氮118～299 kg/hm2; 在一年二熟轮作制中为86～199 kg/hm2。施氮肥条件下,大麦的氮素内部利用率为31.0～56.3 kg/kg; 水稻在23.6～50.2 kg/kg之间变动; 大麦的氮肥利用率变幅在27.5%～41.2%,水稻为14.6%～41.2%。在稻麦轮作系统中,如果想获得作物产量12 t/hm2（4 t大麦和8 t 单季稻）,需要每年施氮肥 N 226～337 kg/hm2。为获得更高的作物产量,在氮肥推荐时不但要考虑作物的目标产量,作物对氮素的需要量,还要充分考虑土壤和环境氮素供应能力。