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.

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.

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.

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.