在当前"MOOC"的大环境下,高等农业院校使用现代信息技术进行教学与学习非常重要.传统的教学模式没有针对性,教师重课程理论基础、轻案例实践,教学方式单一陈旧,已不适应当前教学的发展.本文结合作者实践,以基于"QQ课堂+超星学习通"混合式教学为例,谈论了利用现代信息技术进行教学的重要性.

高等农业院校是培养农村应用型人才的摇篮,在其教育教学过程中,只有改变传统的教学模式,加快信息化时代教育变革,才能推动高等农业院校教学与学习的变革创新.该文通过分析现代信息技术融入高校课堂教学的发展现状,分析了我国高等农业院校引入现代信息技术的现实意义,以促使高等农业院校要尽快加强与现代信息技术的融合.

通过梳理国内外高等农业教育的重要性,指出新时期中国农村经济发展需要建设高素质的人才队伍.本文通过对高等农业教育服务吉林省"互联网+"农村经济发展路径的研究,实证构建计量模型,实验数据处理结果显示吉林省农村"互联网+"发展指数产出弹性为0.335,利用估算出来的数据进行测算改变得出吉林省农村"互联网+"农村经济和配套资源占据贡献率的67.56％,最后提出"互联网+"农村经济发展路径对策.

Currently focused on developing their own industry development in Chang-Ji-Tu better township, make these areas first entered the threshold of the urbanization. Itself Chang-Ji-Tu the forefront of the region's economic development is in Jilin province, Chang-Ji-Tu Demonstration Town figure region accounted for nearly 50% of provincial, have the priority to the development of potential and resources. Demonstration Town industry development at present, there is still a layout is not standard, the problem such as unbalanced development, Jilin province constantly adjust, the industry development of Demonstration Town provide convenient conditions and resources. This paper analyzes the Chang-Ji-Tu region urbanization development way, hope to provide some reference for the development of other towns. To Chang-Ji-Tu area at the same time some shallow Suggestions are put forward for further development of the urbanization, hope to Chang-Ji-Tu even in Jilin province urbanization development in the region to provide some reference value.

提出一种基于ZigBee技术栽参田间温度信号识别方案,结合信号采集识别终端设计实现一套低成本的栽参田间温度信号采集识别系统.利用ZigBee技术和GPRS无线通信技术的农业物联网,融合无线传感器温度采集感知节点,实现栽参田间温度信号实时采集与识别.栽参田间温度在16℃～20℃区间范围内,极易发生人参猝倒病,可以通过实时采集栽参田间温度信号,判断识别栽参田间人参病虫害发生机理.实验结果表明,系统实现了栽参田间温度实时采集与识别,有效地控制了人参猝倒病的发生.

现代设施园艺包含设施园艺信息系统、设施园艺智控系统、设施作物生长模拟系统、专家系统等.利用虚拟化技术整合设施园艺中各类系统依附的服务器,可提高设施园艺系统服务器性能、作物生长参数的准确性及作物生长环境调控的精准性.通过建设虚拟化服务器、试验分析及MATLAB结果仿真,对比物理服务器性能指标,达到设施园艺作物高产、优产、稳产的目的.

针对室外农田绿色作物图像分割中存在的问题,提出一种基于绿色作物G-R颜色特征,结合最大类间方差O tsu法和面积阈值分割的农田绿色作物图像分割方法,解决了室外光照不均及复杂土壤背景环境下农田图像中绿色作物与背景不易分割的难题.实验结果表明,该方法不仅对作物、土壤和光照变化不敏感,且可以消除图像阴影部分的影响,与颜色索引方法EXG-Otsu和RGB算法(G>R,G>B)相比,该方法分割效果更理想.

In view of the relationship between diseases and insect pests in the growth process of ginseng, the safety of ginseng planting production and the key scientific problems of product quality and yield are solved, and the signal identification model of ginseng disease and insect pest is adopted to ensure the quality and safety of ginseng products and the increase of yield. Combined with the key technology of Agricultural Internet of things, a signal recognition model for diseases and pests of ginseng was constructed to realize the identification of pests and diseases in the process of ginseng planting. A signal recognition model for ginseng pests and diseases in Agricultural Internet of things is proposed, and the fuzzy clustering probability of signal characteristics is calculated to get the critical value of catastrophic anomalies. The simulation results show that the proposed model algorithm can obtain accurate data of ginseng disease and insect pests signal, the error after test is 0.00213, the correct rate of normal ginseng signal is 91.03%, and the correct rate of ginseng signal is 99.93%, which greatly improves the accuracy of the signal recognition model of ginseng disease and insect pests.

针对高校的智慧校园数字化平台建设进行结构化研究,结合学校信息化的需求,制定唯一的信息编码标准规范,通过软、硬件支撑平台建设为系统提供基础设施,实现数据可视化的搭建.通过基于LDAP和CAS的统一身份认证登录校园平台,实现综合业务上的整合和漫游访问等多服务支撑,并通过数据安全部署建设,为智慧校园的安稳运行和随时恢复备份提供保障.

本文主要针对目前高校数字化校园应用建设需求,对基于SOA的数字化校园架构设计进行深入研究,通过结合SOA架构、云计算和软件管理服务支撑平台等信息技术,实现了高校应用业务系统的统一整合,异构数据交换和信息资源的灵活互通,有效解决数据冗余和信息孤岛问题.

本文针对无线传感器以及无线信号传播方向进行详细的理论分析,并在此基础上深入研究RSSI测距定位算法的缺点、不足以及优化改进方案,在基于ZigBee传输协议的无线传感器网络构架上进行优化RSSI定位运行探究,通过实验证明,改进后的RSSI定位算法能够有效的精确定位.

通过对基于物联网的智慧大棚温室技术的框架结构进行系统性研究,对各部分功能模块的实操测试、分析和总结,本文对智慧农业大棚框架的信息平台架构、物联网系统架构、智能环境监控和智能决策管理调控的实现流程进行初探概述.

Plant diseases and insect pests have similar symptoms, but it is difficult to distinguish between professional and technical personnel to identify plant diseases and insect pests. In order to accurate extraction of plant diseases and insect pests, physiological and pathological characteristics of signal, puts forward a based on lifting wavelet transform feature extraction algorithm optimization scheme, for the study of plant diseases and insect pests damage signal showing the effect of the prior farmers identify any disease, choose the correct method of governance, quickly make the right decision, improve farmers plant diseases and insect pests, harm signal feature extraction and recognition level. The simulation results show that this algorithm can be used to optimize the stability and convergence, and can be used as an ideal plant disease and insect pests signal feature extraction optimization algorithm, which can effectively identify the different plant diseases and insect pests.

新型城镇化是中国发展的大方向,吉林省作为全国农业大省,其城镇化发展受到国家的关注.长吉图地区是吉林省重点发展区域,也是国家级战略规划先导区,长吉图地区是吉林省发展较好的区域,其城镇化的进程直接影响到全省的城镇化发展.总结近几年长吉图地区城镇化发展的成果,并指出新型城镇化发展中依然存在的问题,提出进一步发展长吉图地区新型城镇化发展的建议.

According to the self similarity of plant electrical signal (fractal feature), changes of plant electrical signal amplitude a moment with the physical environment and mutation, causing plant electrical signal is not continuous.Electrical signal fractal characteristics of plant changes along with the time development, but at some point, it does not change with time change.This paper adopts the wavelet coefficient and self similarity relationship, through the index of self similarity calculation between plant electrical signal and wavelet to obtain the wavelet decomposition.Self similarity index is large, and plant electrical of the self similar degree are high.The simulation experiment results show that the self similarity index diagram after wavelet decomposition display can be found in many scales, the wavelet coefficients are very similar looking, providing a new idea for the detection of plant electrical signal characteristics of the physical environment.

Aiming at the weak noise signals the existence of plant diseases and insect pest images, pathological activity regulation of identification of the plant, so that between the signal molecules exist in plants can regulate mutually, collaborative work. Therefore, identification of weak signal molecules in plants is significant to the study of plant life activities. Taking corn pest images as the research object, using the identification method of lifting wavelet transform, combined with image identification technology, calculated the original plant diseases and insect pest images by not detect the break point of signal. Simulation results show that, the analysis of lifting wavelet of plant disease image identification technology reliability is about 71.65%; the accuracy of edge detection is about 76.21%. The operation speed of this algorithm is fast, easy for hardware implementation, provides an effective method for plant disease images identification.

Chang-Ji-Tu area has its development characteristics,and the talents needed also have certain local characteristics.How to effectively use of education resources in the province,in-depth exhume Chang-Ji-Tu economic development characteristics,cultivate urgent need of talents have become a major topic of present education research in Jilin province.In this paper,combining with the characteristics of Chang-Ji-Tu economic development,puts forward the countermeasures of present higher education personnel training,to provide necessary talent guarantee for sustainable economic development.

This paper proposed the electron density of time series by using the Siesta software to calculate the weak electrical signals of ginseng molecule, combining with the lifting scheme DWT to remove ginseng molecular spatial redundancy. For the acquisition and identification of weak electrical signals of ginseng molecule in physical environment , based on the analysis of collection and identification’s principles, the noise coefficient is removed to reconstruct the signal and retain the useful signal components through applying the multi-decomposition of DWT transform to divide weak electrical signals of ginseng molecule into wavelet coefficients of different scales. The experimental results show that the multi-resolution analysis of DWT transform is performed for the weak electrical signal of ginseng molecule with different rhythms and different frequency ranges, and the weak electrical signal size of ginseng molecule before and after compression, the percentage of high frequency coefficients set to zero, and the average energy percentage after compression are, respectively, increased to 77.73%, 46.88%, and 99.99%. This algorithm operates fast enough to ease hardware implementation, providing an effective method for lossless compression of the weak electrical signals of ginseng molecule.

The paper puts forward an image de-noising method based on 2D wavelet transform with the application of the method in agricultural data collection system. As the there are influences of various factors in the collection process through wireless image sensor network, the detail signals of each scale are obtained from multi-scale analysis to replace the original signals with smooth low-frequency signals by applying 2D wavelet transform in de-nosing the images collected. The experiment result shows that the application of 2D wavelet transform image de-noising algorithm can achieve good subjective and objective image quality and help to collect high quality data and analyze the images for the data center with optimum effects.

In order to find the solution to the problems in collection and identification of the weak electrical signals in the physical environment, this paper, based on the analysis of the relevant principles, presents a denoising method using multilevel threshold based on the detailed coefficients of Daubechies wavelet transform through a deduction process of the method. This method uses the analysis of the minimum frequency components of signals to determine the maximum decomposition levels with the ability of extracting and processing the plant weak electrical signals. The simulation experiments show the method is effective in denoising, especially for the restoration of the weak electrical signals with high noise background, and it can be used in extracting and processing the weak electrical signals and is an effective method of detecting the signals.