河南农业大学学报2024，Vol.58Issue(2)：269-278,10.DOI:10.16445/j.cnki.1000-2340.20240022.002
基于机器学习算法的农田挥发氨多传感器阵列检测技术研究
Rapid detection of volatile ammonia in farmland using mulitsensors with machine learning algorithms
摘要
Abstract
[Objective]Design a device for the rapid,low-cost,and convenient detection of ammonia in farmland.[Method]An electronic nose device based on a tin dioxide(SnO2)semiconductor gas sensor array was constructed.Under conditions of fresh air(ammonia mass concentration of 0 mg· m-3)and ammonia mass concentrations of 75.9,151.8 and 303.6 mg·m-3 respectively,as well as air mixed with ethanol,pure ethanol gas(mass concentration of 151.8 mg·m-3),air mixed with am-monia,and pure ammonia gas(mass concentration of 151.8 mg·m-3),the data responded in the steady-state stage and transient stage of multisensory array were classified by using principal component analysis(PCA),K-nearest neighbors(KNN),and support vector machine(SVM)algorithms.Fur-thermore,the system's ability to differentiate between different mass concentrations of ammonia and mixed gas environments was evaluated.[Results]The device can clearly differentiate between different mass concentrations of ammonia.In the steady-state phase,the PC1 proportion exceeds 90％,and both KNN and SVM algorithms achieve accuracy rates exceeding 97％.While in the transient phase,the average accuracy rate is 68％,and the average classification accuracy for KNN and SVM is 68％.[Conclusion]The multisensor array detection system can read the data without waiting a steady-state phase,which can facilitate the rapid and continuous detection of volatile ammonia in farmland.关键词
多传感器阵列/挥发氨/机器学习/农田/稳态相Key words
multisensor array/volatile ammonia/machine learning/farmland/stead-state phase分类
农业科技引用本文复制引用
耿宽,ATA Jahangir Moshayedi,张浩,张伟,胡建东..基于机器学习算法的农田挥发氨多传感器阵列检测技术研究[J].河南农业大学学报,2024,58(2):269-278,10.基金项目
国家自然科学基金项目(32071890) （32071890）
国家重点研发计划项目(2021YFD1700904) （2021YFD1700904）
农业生物资源工程技术外籍科学家工作室项目(GZS2021007) （GZS2021007）
