基于神经网络优化融合算法的农作物生长土壤微量元素含量测定  

Research on Fusion Algorithm Based on Neural Network Optimization in the Determination of Trace Element Content in Crop Growth Soil

作　　者：齐仁龙[1,2] 翟璐璐 李大海 Qi Renong;Zhai Luu;Li Dahai(School of Electronics and Electrical Engineering,Zhengzhou University of Science and Technology,Zhengzhou City,Henan Province 450064;Henan Intelligent Information Processing and Control Engineering Technology Research Center,Zhengzhou City,Henan Province 450064)

机构地区：[1]郑州科技学院电子与电气工程学院,河南郑州450064 [2]河南省智能信息处理与控制工程技术研究中心,河南郑州450064

出　　处：《黄河科技学院学报》2023年第8期42-45,共4页Journal of Huanghe S&T College

基　　金：河南省高等学校青年骨干教师培养计划:基于大数据的农作物生长过程微量元素含量监测系统(2019GGJS276)。

摘　　要：我国土壤肥力不佳,农作物的增产丰收主要依赖施用化肥。但施肥元素主要以氮磷钾为主,而对微肥的施用不重视,致使施肥不均衡问题突出。以河南省耕层土壤为研究对象,采用物联网大数据分析技术,对植物生长过程中微量元素含量采集结果实行数据处理、模型搭建、测试优化,实现植物微量元素含量的智能识别与诊断分析、预测预报等,以达到科学合理地制定高效施肥方案,实现作物优质高产以及化肥投入零增长的目标。China's soil fertility is weak,and the increase in crop yield and harvest mainly depends on the application of chemical fertilizers.However,the fertilization elements are mainly nitrogen,phosphorus and potassium,and the application of micro-fertilizer is not paid attention to,resulting in the problem of unbalanced fertilization.This time,taking the cultivated soil of Henan Province as the research object,the Internet of Things big data analysis technology was used to implement data processing,model construction,test optimization of the trace element content collection results in the plant growth process,and realize the intelligent identification,diagnosis and analysis of plant trace element content,prediction and forecasting,etc.,so as to achieve the scientific and reasonable formulation of efficient fertilization scheme,and achieve the goal of high quality and high yield of crops and zero growth of chemical fertilizer input.

关 键 词：大数据 神经网络 DS证据理论 微量元素 监测研判 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]