摘要
[目的]为解决传统人工水稻品种鉴定程序复杂的问题,建立一种快速鉴定水稻品种模型,利用多光谱成像技术对水稻叶色进行分析。[方法]分别利用线性拟合、多层感知器、径向基函数和随机森林4种机器学习算法模型,采用大疆精灵4多光谱版无人机航拍获得多光谱数据,对数据特征提取后得到的植被指数进行建模分析。通过输入不同组合的植被指数,筛选出准确率最高的变量组合。[结果]发现多层感知器模型对水稻品种的识别准确率可达94.87%,比线性拟合模型、径向基函数模型、随机森林模型的准确率分别高41.3%、8.55%、16.24%。将精度最高、拟合程度最好的多层感知器模型确定为最优模型。[结论]多层感知器分类模型对水稻的平均分类准确率最高,能够较好实现水稻品种鉴定的功能。
[Objectives]In order to solve the complex problem of traditional artificial rice variety identification procedure,a visual rice variety identification model was established by using multispectral imaging technology to analyze rice leaf color.[Methods]Four machine learning algorithms,linear fitting,multi-layer perceptron,radial basis function model and random forest model,were used to model and analyze the vegetation index obtained from the feature extraction of multispectral data,which were obtained from the aerial photography of Dajiang spirit 4 multispectral unmanned aerial vehicle(UAV).The variable combinations with the best accuracy would be applied to identify rice variety with different combinations of vegetation index.[Results]The study found that the accuracy of multi-layer perceptron model in identifying rice varieties reached to 94.87%.The accuracy was 41.3%,8.55%,and 16.24%better than that of linear fitting model,radial basis function model and random forest model,respectively.Therefore,the multilayer perceptron model with best accuracy and fitting degree was selected as the optimal model.[Conclusions]The rice variety identification model constructed in this study can accurately predict rice varieties by judging the leaf color of rice leaves,and provide reference value for the identification of rice varieties.
作者
陈裕锋
冯佩雯
凌金生
张书琪
余振鹏
熊琳琳
刘洪
谢家兴
CHEN Yufeng;FENG Peiwen;LING Jinsheng;ZHANG Shuqi;YU Zhenpeng;XIONG Linlin;LIU Hong;XIE Jiaxing(College of Electronic Engineering(College of Artificial Intelligence),South China Agricultural University,Guangzhou 510642,China;College of Agriculture,South China Agricultural University,Guangzhou 510642,China;Guangdong Laboratory for Lingnan Modern Agriculture,Guangzhou 510642,China;Engineering Research Center for Monitoring Agricultural information of Guangdong Province,Guangzhou 510642,China)
出处
《南京农业大学学报》
CAS
CSCD
北大核心
2023年第5期995-1003,共9页
Journal of Nanjing Agricultural University
基金
华南农业大学新农村发展研究院农业科技合作共建项目(2021XNYNYKJHZGJ032)
岭南现代农业实验室科研项目(NT2021009)
广东省科技专项资金“大专项+任务清单”项目(2020020103)
广东省科技创新战略专项资金“攀登计划”专项资金项目(pdjh2021b0077)
大学生创新创业训练计划项目(X202110564137)。
关键词
水稻
品种
鉴定
多光谱图像
植被指数
机器学习
rice
variety
identification
multi-spectral image
vegetation index
machine learning