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基于云计算与RBF神经网络集成的 玉米精准施肥模型研究
Research on Accurate Fertilizing Model of Maize Based on Cloud Computing and RBF Neural Network
投稿时间:2018-12-23  修订日期:2019-03-04
中文关键词:云计算;玉米精准施肥模型;RBF神经网络  土壤空间变异
英文关键词:cloud computing  precision fertilization model for corn  RBF neural network  soil nutrients
基金项目:国家863项目(2006AA10A309),国家星火计划(2015GA660004),吉林省重点科技研发项目(20180201073SF)
作者单位E-mail
贾海峰 吉林农业大学 717749793@qq.com 
陈桂芬 吉林农业大学  
赵姗 吉林农业大学信息技术学院  
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中文摘要:
      玉米精准施肥是实现玉米精准作业的关键技术,玉米精准施肥的核心是精准施肥模型的构建。针对玉米精准施肥模型与土壤多个参数有复杂的关系,且动态性数据量庞大、维度高,具有非线性强的特点,所以本文基于云计算技术的数据存储、分析、共享的功能,运用神经网络较强的处理非线性问题的能力,对玉米土壤养分数据进行处理,首先构建玉米土壤实时监控云平台,然后进行基于优化RBF神经网络的玉米土壤养分施肥模型研究,最后明确模拟施肥量与玉米产量、土壤养分含量的关系,并将该模型应用到国家星火计划示范基地农安县陈家店村,为玉米的精准施肥提供决策依据。实验应用结果表明,基于云计算与RBF神经网络集成的玉米精准施肥模型较传统网络进行比较减少了误差,节约了时间,可对玉米精准施肥提供咨询指导,促进了玉米精准农田管理的实施。
英文摘要:
      Precise fertilization of corn is a key technology for realizing precise maize operations. The core of precision fertilization in maize is the construction of a precise fertilization model.There is a complex relationship between precision corn fertilization model and soil parameters, and the dynamic data is large in size, high in dimension, and has strong nonlinearity.this paper is based on the data storage, analysis and sharing functions of cloud computing technology, and the use The network has strong ability to deal with non-linear problems. To deal with corn soil nutrient data, we first construct a real-time cloud platform for monitoring maize soil,then conduct a study on the soil nutrient fertilization model based on an optimized RBF neural network. Finally, we can clearly simulate the amount of fertilizer and corn. The relationship between yield and soil nutrient content, and the model was applied to the National Spark Program Demonstration Base Chenjiadian Village of Nong'an County to provide decision-making basis for accurate fertilization of maize. The experimental application results show that the precision corn fertilization model based on cloud computing and RBF neural network integration reduces the error compared with the traditional network, saves time, provides advice and guidance on precision corn fertilization, and promotes the implementation of corn precision farm management.
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