摘要
柑橘病害严重影响柑橘产量与品质,为进一步寻找叶片光谱与其理化指标变化规律,故将近红外光谱技术与化学计量学相结合探索柑橘病害光谱判别及病害对理化指标影响的可行性。利用便携式近红外光谱仪,获取柑橘正常、溃疡病和砂皮病3类叶片光谱并测量理化指标值,开展光谱特性与理化指标规律分析,进行SPA(successive projections algorithm)与PCA(principal component analysis)变量筛选,结合RF(random forest)与LWPLS(locally weighted partial least squares)分别建立柑橘病害定性模型及理化指标定量模型。对比分析模型结果,发现基于原始光谱变量的LWPLS定性模型效果最佳,其判别准确率为94.03%。用401个光谱变量为输入,基于LWPLS的正常叶片SPAD值模型分析结果最优;虽正常、溃疡病和砂皮病3类叶片综合的LWPLS定量模型RMSEP较大为4.46%,但模型对叶片SPAD值具有较好的预测精度,R^(2)和RPD分别为0.93、2.19。研究表明,近红外光谱技术结合化学计量学判别柑橘病害及分析病害对叶片理化指标的影响具有一定可行性,可为柑橘病害实时现场检测提供重要科学参考。
Pest and diseases have seriously threatened to the yield and quality of citrus.This study aims to explore the effect of citrus diseases on SPAD value using identification model by near-infrared spectroscopy with chemometrics.Leaf spectra and physicochemical indexes were further determined to detect the citrus diseases.The NIR spectra of citrus leaves with normal,canker and emery derma were obtained to measure the physicochemical indices by portable NIR spectrometer.The spectrum analysis showed that there was a reflection peak in the leaves near 553 nm,indicating the reflection peak after the chlorophyll saturated the high-frequency absorption of light.The maximum reflection peak of normal leaves was about 0.21,whereas,the lowest was about 0.13 in the leaves with the sand skin disease.The reflectance of normal leaves was higher than that of diseased leaves at 750-900 nm.The reason was that the reflectance decreased to enlarge the leaf cell cavities under disease stress.The variance was analyzed for two physical and chemical indexes.There were significant differences in the SPAD value indexes of the three types of citrus leaves,while no significant difference was found in fresh quality indexes.A qualitative and quantitative model of citrus diseases was also established to clarify the relationship with leaf spectrum.According to the ratio of 2:1,212 blades were randomly divided into two dataset with 145 samples in training set and 67 samples in verification set.The SPA and PCA were used to screen the spectral variables.17 and 20 optimal variables were selected to combine the RF and LWPLS.The comparison showed that the best detection was achieved in the qualitative model of LWPLS for three types of disease leaves using 401 variables of the original spectrum,with the accuracy of 94.03%.While the low accuracy of 77.61%was found in the qualitative model of RF for three types of disease leaves using SPA screening 17 variables.The main reason was that a few sensitive variables were eliminated,although the characteristic v
作者
肖怀春
吴茂隆
栗琳琳
尹淑琳
孙旭东
肖慧
黎小芳
鞠喜
XIAO Huaichun;WU Maolong;LI Linlin;YIN Shulin;SUN Xudong;XIAO Hui;LI Xiaofang;JU Xi(Key Laboratory of Mine Environmental Monitoring and Control in the Ring Poyang Lake Region,Ministry of Natural Resources,East China University of Technology,Nanchang 330013,China;Ji'an Institute of Forestry Science,Ji'an 343011,China;School of Mechatronics and Vehicle Engineering,East China Jiaotong University,Nanchang 330013,China;Jiangxi China Coal Water Conservancy and Environmental Protection Group Co LTD,Nanchang 330108,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2024年第13期189-195,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室开放基金项目(No.MEMI-2021-2022-18)
国家自然科学基金项目(No.31960497)
江西省质谱与仪器重点实验室开放基金项目(No.JXMS202011)
东华理工大学博士启动研究基金项目(No.DHBK2019074),东华理工大学研究生工作站建设基金项目(No.研究生字[2022]27号)
江西省核地学数据科学与系统工程技术研究中心开放基金项目(No.JETRCNGDSS202102)。
关键词
病害
模型
近红外光谱
柑橘
SPAD值
实时检测
disease
models
near infrared spectroscopy
citrus
SPAD value
real-time detection
