摘要
对于典型的呼吸跃变型水果,降低外源乙烯浓度可以减弱果实对乙烯的敏感性,从而延缓内源乙烯跃变高峰及呼吸跃变高峰的出现,对果实起到一定的延缓衰老作用。首先采用MOX气体传感器构建高精度乙烯浓度传感器;其次,基于Kernel DM+V算法,开展单一和多气源定位实验,形成SLAM-GDM地图对未知环境中的乙烯进行气源定位和浓度分布可视化;最后采用乙烯降解机器人开展降解实验。实验结果表明:所建立的气体分布模型能够预测气源位置,SLAM-GDM地图性能在预测单气源位置上的平均误差为0.78 m,精度高于95%;多气源位置预测实验表明,方差分析具有较高预测精度,气源预测位置平均误差小于0.48 m,相对误差小于5%;气体降解机器人能够根据乙烯气体分布图较为准确地找到气源位置,并以较高的速率进行乙烯降解。本研究通过移动机器人进行乙烯气源定位与降解乙烯,从而延长水果保鲜期和货架期,在水果保鲜领域具有应用前景。
For characterized climacteric fruit,its sensitivity to ethylene can be weakened by reducing the concentration of exogenous ethylene,so as to postpone the emergence of endogenous ethylene climacteric peak and respiratory climacteric peak,which plays a role in postponing the senescence of fruits to some extent.Firstly,the MOX gas sensor was adopted to build an ethylene concentration sensor with high precision;secondly,it carried out single and multiple gas sources localization experiments based on Kernel DM+V algorithm to form a SLAM-GDM map to locate the gas source and visualize the concentration distribution of ethylene in an unknown environment;finally,it performed the degradation experiment by using ethylene degradation robots.The experiment findings indicated that the built gas distribution model can predict the gas source localization.The average error of the SLAM-GDM map performance in predicting the single gas source localization was 0.78 m with more than 95%of accuracy.The multiple gas sources localization prediction experiment showed that the analysis of variance presented high prediction accuracy.The average error of the gas source prediction localization was less than 0.48 m with less than 5%of relative error,and the gas degradation robot can accurately find the gas source position according to the ethylene gas distribution mapping,and conduct ethylene degradation at a high rate.The ethylene gas-source was positioned and degraded ethylene through mobile robots,to extend the storage and shelf life of fruits and it had an application prospect in the field of fruit preservation.
作者
赵文锋
刘小玲
林暖晨
梁升濠
张宇
ZHAO Wenfeng;LIU Xiaoling;LIN Nuanchen;LIANG Shenghao;ZHANG Yu(College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;South China Smart Agriculture Public Research and Development Platform,Ministry of Agriculture and Rural Affairs,Guangzhou 510520,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第3期451-458,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
广东省农业科技创新十大主攻方向“揭榜挂帅”项目(2022SDZG03)
广东省科技创新战略专项资金项目(pdjh2022b0078)。
