基于ATR-FTIR技术原位无损感知棉花叶片表面农药沉积量  

In situ non-destructive sensing of pesticide deposition on cotton leaf surfaces using ATR-FTIR technology

作　　者：吕高强 温凯渊 伊丽姆努尔·吾斯曼 张启明 刘飞 孙卫红[1] LYU Gaoqiang;WEN Kaiyuan;YILIMUNUER·WUSIMAN;ZHANG Qiming;LIU Fei;SUN Weihong(College of Agricultural Engineering,Jiangsu University,Zhenjiang 212013,China;The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province,Kunming 650201,China;The Key Laboratory of Spectroscopy Sensing of Ministry of Agriculture and Rural Affairs,Hangzhou 310058,China)

机构地区：[1]江苏大学农业工程学院,镇江202013 [2]云南省作物生产与智慧农业重点实验室,昆明650201 [3]农业农村部光谱检测重点实验室,杭州310058

出　　处：《农业工程学报》2024年第24期137-145,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：科技部科技创新2030—“新一代人工智能”重大项目(2022ZD0115801);教育部现代农业装备与技术重点实验室开放基金(MAET202320);云南省作物生产与智慧农业重点实验室开放基金课题(2024ZHNY03);农业农村部光谱检测重点实验室开放基金课题(2024ZJUGP002)。

摘　　要：为了探究基于衰减全反射傅里叶变换红外光谱(attenuated total reflection Fourier transform infrared spectroscopy,ATR-FTIR)技术实施农药沉积量原位感知的可能性,该研究以含有不同量广谱性杀菌剂啶酰菌胺沉积的棉花叶片为试验材料,结合化学计量学分析方法开展相关探索。首先使用棉花叶片制成140例已知农药沉积量的标准样品,并采用ATR-FTIR技术获取其光谱数据;然后借助区间偏最小二乘法、相关性分析等方法筛选到272个相关性强的波长变量;最后以优化后的变量及偏最小二乘回归算法建立定量预测模型。结果表明模型的预测性能优异,预测的均方根误差为1.18μg/cm^(2),最低检测限(limit of detection,LOD)低至3.54μg/cm^(2);利用概率神经网络判别样品中农药沉积量是否大于LOD的整体准确率高达95%。该研究结果证明ATR-FTIR技术可实现农药沉积量的高精度原位检测,为其在生产中的应用提供理论依据和数据支撑。Pesticides can be often regulated to detect the deposition constitutes on the crops in fields.Nevertheless,the current research cannot focus on the specific difficulties in practical application.Fortunately,attenuated total reflection Fourier transform infrared spectroscopy(ATR-FTIR)technology can be expected to probe the presence of pesticides on the leaf surface without damage to the samples.This study aims to evaluate the in situ non-destructive sensing of pesticide deposition using infrared ATR-FTIR together with chemometric analysis.The examples were also taken as the broad-spectrum fungicide boscalid and the economic crop cotton.140 standard samples of cotton leaves were then fabricated with known amounts of pesticide deposition.The spectral data was acquired after experiment.Subsequently,the preliminary analysis was conducted to determine the differences among the samples using principal component analysis(PCA).The great variations in spectral characteristics were attributed to the differences in pesticide deposition.Distinct patterns and trends were also identified from the data.After that the interval partial least squares(iPLS)and correlation analysis were employed to screen out the wavelength variables with strong correlations.This procedure aimed to identify the specific wavelengths the most relevant to pesticide deposition.Finally,a prediction model was established using the optimal variables and partial least squares regression(PLSR).The deposition amount of pesticide was predicted using the spectral data.The outstanding performance of prediction model was achieved with an R^(2) value of 0.83 and an RMSE of 1.21μg/cm^(2) in calibration,while an R^(2) value of 0.86,an RMSE of 1.18μg/cm^(2),an RPD of 2.3,and an RPIQ of 2.0 in validation.These results demonstrated the high accuracy and reliability of the prediction model on pesticide deposition.In addition,a classification model was also built with the probabilistic neural network(PNN),in order to distinguish whether the samples were with a higher de

关 键 词：农药沉积量 原位 无损 定量检测 ATR-FTIR 

分 类 号：S126[农业科学—农业基础科学]