基于高光谱成像技术的烟叶田间吡虫啉用量快速检测技术研究

Research on Rapid Detection of Imidacloprid Dosage in Tobacco Field Production Based on Hyperspectral Imaging Technology

为了降低烟叶田间农药过量使用所带来的残留风险,应用高光谱成像技术(515~900 nm),以1∶5000、1∶2500、1∶850这3组浓度吡虫啉药液喷施24 h后的鲜烟叶样本为研究对象,采用4种常用光谱预处理方法进行数据预处理后,分别输入卷积神经网络(CNN)、随机森林(RF)和最小二乘支持向量机(LSSVM)模式对比准确率,选出全波段最优模型。采用连续投影法(SPA)、竞争性自适应重加权采样法(CARS)对数据进行了降维,将降维后的高光谱数据分别输入选出的模型,建立了吡虫啉田间用量检测模型,比较模型的准确率和特征选择数量,确定了特征波段下的最优模型。结果表明:(1)原始数据和预处理后的数据输入卷积神经网络测试集准确率均达到100.00%,经一阶导数和二阶导数预处理后的数据,输入随机森林测试集准确率也达到100.00%;(2)连续投影法的数据降维效果优于竞争性自适应重加权采样法;(3)综合考虑模型的准确率和检测时间,最终选择经过二阶导数预处理和连续投影法降维后建立的随机森林模型作为最优判别模型,选择特征数量为12个,测试集准确率为98.86%,单个样本检测时间为0.79 ms。综上,应用高光谱技术结合D2-SPA-RF模型可实现烟叶田间吡虫啉用量的快速检测。

In order to reduce the risk of pesticide residues caused by overuse,the hyperspectral imaging technology(515-900nm)was used,and fresh tobacco leaf samples treated with three different concentrations of imidacloprid solution(1∶5000,1∶2500,1∶850)for 24 hours were selected as the research objects.Four commonly used spectral preprocessing methods were applied,followed by inputting the preprocessed data into convolutional neural networks(CNN),random forests(RF),and least squares support vector machines(LSSVM)separately to compare their accuracies and select the optimal model across the entire spectrum.Subsequently,dimensionality reduction techniques including successive projections algorithm(SPA)and competitive adaptive reweighted sampling(CARS)were applied to the data,and the reduced hyperspectral data were inputted into the selected optimal model to establish a model for detecting imidacloprid usage.The model accuracies and the number of selected features were compared,aiming to identify the optimal feature bands.The results show that:(1)Raw data or preprocessing data input to Convolutional Neural Network,and the accuracy of test set samples result reaches 100.00%.The data preprocessed by first and second derivatives input into a random forest,and the accuracy of the testing set result also reaches 100.00%.(2)The dimensionality reduction effect of successive projections algorithm method is better than the competitive adaptive reweighted sampling method.(3)the random forest established after second derivatives preprocessing and successive projections algorithm method dimensionality reduction was ultimately chosen as the optimal discrimination model,with 12 feature wavelengths,the 98.86%of the test set result accuracy,and 0.79 ms of the single sample detection.In conclusion,the application of hyperspectral technology combined with the D2-SPA-RF model can achieve rapid detection of imidacloprid dosage in tobacco field production.