Research on Intelligent Decision Support System of Soybean

Based on agriculture production knowledge and computer technology, this paper applied database, artificial intelligent, management information system, decision support system, network technology and information integration technology to the field of soybean production, and offered decision support to users in imitating the curse during which experts solved the problems, therefore, guided production practice, introduced structure and function of decision support system of soybean, and then we analyzed the design of the knowledge base and the realization of inference engine in details as well.

改进的QGA-ELM算法水稻叶面积指数反演模型

为了通过植被指数(VI)准确、可靠的获取不同施肥梯度、不同品种的水稻叶面积指数(LAI),提出了一种基于改进的QGA-ELM算法应用于水稻LAI反演。首先通过8折交叉验证确定极限学习机(ELM)最佳的隐含层神经元个数与隐含层激活函数类型,再通过引入组合动态旋转角策略、单点混沌交叉操作、混沌变异操作、确定性选择策略、量子灾变操作对量子遗传算法(QGA)进行改进,最后使用改进后的QGA算法优化ELM神经网络输入层到隐含层的连接权值和隐含层的阈值。为了验证该模型普适性和有效性,依次建立多元线性回归、BP、ELM、QGA-ELM、改进的QGA-ELM算法5种模型,并在不同数据集上进行反演效果比较,结果表明:(1)对比QGA-ELM算法和改进的QGA-ELM算法进化过程,改进的算法能有效提升模型寻优能力,避免算法早熟,且能寻得更优结果。(2)对比五种算法在不同数据集上的反演效果,验证了NDVI,RVI与LAI之间主要为非线性关系,且ELM神经网络模型反演效果要优于BP神经网络模型和多元线性回归模型。(3)对比五种算法在不同数据集上的反演效果,改进的QGA-ELM算法绝大部分情况下拥有最高的反演精度和最低的误差,改进后的算法反演精度得到了明显提升,泛化性能也得到了增强。(4)改进的QGA-ELM算法在各种施肥梯度上均具有最高反演精度和最低误差,且精度较高,能为不同生长状况水稻LAI反演提供依据。(5)五种模型对庆和香LAI反演精度均要高于龙稻18,而改进的QGA-ELM算法在不同水稻品种上依然具有较高的反演精度,且在不同水稻品种上反演精度相差极小,远低于其他四种模型,能很好适应不同水稻品种LAI反演要求,极大提升模型的稳定性性,为不同水稻品种反演提供参考意义。

Chlorophyll Content Retrieval of Rice Canopy with Multi-spectral Inversion Based on LS-SVR Algorithm

To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.

Research and Simulation of Navigation Control System for Agricultural Unmanned Rotation Rotorcraft

In order to improve the efficiency of field operations, such as pesticide spraying and pest control, the development of new agricultural machinery has become the main goal of agricultural modernization. At present, small unmanned aerial vehicles(UAVs) have achieved good results in small area pesticide spraying, but they still do not meet the requirements of the big field operations. The rotation rotorcraft has the characteristics of low speed, good safety, super stol, strong endurance, strong wind resistance and simple mechanism, so it is very suitable for farmland operation. But at present, there are relatively few researches on the unmanned rotation rotorcraft at domestic and abroad, and there is no mature control theory system of unmanned rotation rotorcraft. Therefore, in order to solve the problem of navigation control system of unmanned rotation rotorcraft, the navigation control strategy of the rotation rotorcraft was studied in this paper, and the flight control strategy of the rotation rotorcraft was divided into two parts, the linear control and the turn control. The lateral correction strategy was used to control the rotorcraft, so as to complete the track guidance of the rotation rotorcraft. Using the matrix laboratory(MATLAB) modeling to simulate the circular trajectory and the serpentine trajectory, the maximum deviation of circular path offset was 1.1 m. The variance between the path angle and the given path angle was 6°. The maximum deviation of serpentine path offset was 0.6 m. The variance between the path angle and the given path angle was 8°. The simulation results showed that the rotorcraft could accomplish the scheduled trajectory task based on this control strategy.

Study on Rice Yield Estimation Model Based on Quantile Regression

An airborne multi-spectral camera was used in this study to estimate rice yields.The experimental data were achieved by obtaining a multi-spectral image of the rice canopy in an experimental field throughout the jointing stage(July,2017)and extracting five vegetation indices.Vegetation indices and rice growth parameter data were compared and analyzed.Effective predictors were screened by using significance analysis and quantile and ordinary least square(OLS)regression models estimating rice yields were constructed.The results showed that a quantile regression model based on normalized difference vegetation indices(NDVI)and rice yields performed was best forτ=0.7 quantile.Thus,NDVI was determined as an effective variable for the rice yield estimation during the jointing stage.The accuracy of the quantile regression estimation model was then assessed using RMES and MAPE test indicators.The yields by this approach had better results than those of an OLS regression estimation model and showed that quantile regression had practical applications and research significance in rice yields estimation.

Identification of Typical Rice Diseases Based on Interleaved Attention Neural Network

Taking Jiuhong Modern Agriculture Demonstration Park of Heilongjiang Province as the base for rice disease image acquisition,a total of 841 images of the four different diseases,including rice blast,stripe leaf blight,red blight and bacterial brown spot,were obtained.In this study,an interleaved attention neural network(IANN)was proposed to realize the recognition of rice disease images and an interleaved group convolutions(IGC)network was introduced to reduce the number of convolutional parameters,which realized the information interaction between channels.Based on the convolutional block attention module(CBAM),attention was paid to the features of results of the primary group convolution in the cross-group convolution to improve the classification performance of the deep learning model.The results showed that the classification accuracy of IANN was 96.14%,which was 4.72%higher than that of the classical convolutional neural network(CNN).This study showed a new idea for the efficient training of neural networks in the case of small samples and provided a reference for the image recognition and diagnosis of rice and other crop diseases.

Rural power grid resources WebGIS technology research based on ArcIMS

Web GIS is a tendency of current geographical information system.Arcl MS is an excellent platform to construct Web GIS.This paper introduces the architecture,features and advantages of ArcI MS,which also builds a rural power resources information system based on ArcI MS.This system is a security assurance of reactive power optimization of distribution network,is an effective method of economic operation and is one of important measures to improve the voltage quality of distribution network.Applying ArcS DE to establish spatial database and transform the format of spatial data are key to realizing the system.