荧光高光谱成像技术对甜瓜叶片过氧化氢酶活性的无损检测  

Nondestructive Detection of Catalase Activity in Melon Leaves By Fluorescence Hyperspectral Imagery

作　　者：王静[1] 马玲[1] 马思艳 马燕[1] 张祎洋 吴龙国[1,2] WANG Jing;MA Ling;MA Si-yan;MA Yan;ZHANG Yi-yang;WU Long-guo(College of Enology and Horticulture,Ningxia University,Yinchuan 750021,China;Ningxia Modern Facility Horticulture Engineering Technology Research Center,Yinchuan 750021,China)

机构地区：[1]宁夏大学葡萄酒与园艺学院,宁夏银川750021 [2]宁夏现代设施园艺工程技术研究中心,宁夏银川750021

出　　处：《光谱学与光谱分析》2024年第12期3455-3462,共8页Spectroscopy and Spectral Analysis

基　　金：宁夏回族自治区重点研发计划项目(2021BBF02024,2021BEB04077);国家重点研发计划子课题专项(2021YFD1600302-3);横向课题(2022WZYQ0001)资助。

摘　　要：为达到及时监测植株生长状况,快速检测不同光强下甜瓜叶片过氧化氢酶活性分布的差异是至关重要的。采用不同光照强度对甜瓜叶片进行处理,进而采用荧光高光谱成像技术对叶片扫描,提取出300个叶片样本的平均光谱反射率,通过4种预处理方法对原始光谱进行了预处理和优化。运用区间变量迭代空间收缩法(iVISSA)、竞争性自适应加权算法(CARS)、遗传偏最小二乘算法(GAPLS)、迭代保留有效信息变量法(IRIV)和变量组合集群分析法(VCPA)五种方法提取了特征波长,采用偏最小二乘回归(PLSR)模型筛选出最优特征波长。基于优选的特征波长建立了主成分回归(PCR)模型、多元线性回归(MLR)模型、卷积神经网络(CNN)模型、最小二乘支持向量机(LSSVM)模型,结果表明Baseline-IRIV-MLR模型识别准确率最高,训练集和预测集的准确率均为0.852。本研究结果为荧光高光谱成像技术应用于瓜类作物质量评价提供理论依据,为精准农业的发展提供技术支持。To achieve timely monitoring of plant growth,rapid detection of differences in the distribution of catalase activity in melon leaves under different light intensities is essential.In this study,melon leaves were treated with different light intensities.Then the leaves were scanned using fluorescence hyperspectral imaging to extract the average spectral reflectance of 300 leaf samples,and the raw spectra were pre-processed and optimised by four pre-processing methods.Using interval Variable Iterative Space Shrinkage Approach(iVISSA),Competitive adaptive reweighted sampling(CARS),Genetic algorithm partial least squares algorithm(GAPLS),Iterative retained Information Variable(IRIV),and Variables Combination Population Analysis(VCPA)were used to extract the feature wavelengths.The partial-least-squares regression(PLSR)model screened the optimal feature wavelengths.Based on the preferred feature wavelengths,Principal component regression(PCR)model,Multiple linear regression(MLR)model,Convolutional Neural Network(CNN)model,Least Squares Support Vector Machine(LSSVM)model,and the results show that Baseline-IRIV-MLR model has the highest recognition accuracy,with an accuracy of 0.852 in both training and prediction sets.The results of this study provide a theoretical basis for applying fluorescence hyperspectral imaging technology in the quality evaluation of melon crops and technical support for the development of precision agriculture.

关 键 词：荧光高光谱 甜瓜叶片 过氧化氢酶 

分 类 号：S652[农业科学—果树学]