基于GA-BP神经网络的谷物水分预测  

Grain Moisture Prediction Based on GA-BP Neural Network

作　　者：陈洪飞 赵珂[1] 王忠 CHEN Hong-fei;ZHAO Ke;WANG Zhong(School of Information and Engineering,Nanchang Hangkong University,Nanchang 330063,China)

机构地区：[1]南昌航空大学信息工程学院,南昌330063

出　　处：《南昌航空大学学报（自然科学版）》2022年第1期79-85,共7页Journal of Nanchang Hangkong University(Natural Sciences)

基　　金：江西省自然科学基金(20192BAB207001,20202BABL204042);航空科学基金(201920056001,20200020056001)。

摘　　要：粮食安全作为治国理政的头等要事,实时掌握谷物含水率的变化对粮食安全存储具有重要的意义。本文以“中浙优8号”晚籼稻为研究对象,通过近红外光谱实验获取在不同温度、湿度下谷物含水率的测量数据,采用遗传算法优化的BP神经网络建立预测谷物水分计算模型,再将其应用于近红外光谱谷物含水率检测实验中。研究结果表明,该预测模型相对于BP神经网络的决定系数提升了1.356%,仪器的测量精度为94.67%,具有应用价值。As food security is the most important thing in governing the country,it is of great significance to grasp the change of grain moisture content in real time for food security storage.This article choose late indica rice“Zhongzhe You No.8”as the research object,under the condition of different temperature and different humidity,get grain moisture content measurement by NIR experiments,and the BP neural network optimized by genetic algorithm was adopted to establish prediction model of grain moisture,then the prediction model is applied to the detection experiment of grain moisture content by NIR.Experimental results show that the prediction model used in this paper has more application value,because compared with BP neural network,the determination coefficient is improved by 1.356%,and the measurement accuracy of the instrument is 94.67%.

关 键 词：GA-BP神经网络 近红外光谱 谷物 含水率 

分 类 号：S512.3[农业科学—作物学]