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首页> 外文会议>Sensing for Agriculture and Food Quality and Safety IX >Detection of pesticide (Cyantraniliprole) residue on grapes using hyperspectral sensing
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Detection of pesticide (Cyantraniliprole) residue on grapes using hyperspectral sensing

机译：使用高光谱传感技术检测葡萄中的农药残留（氰基腈）

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Pesticide residues in the fruits, vegetables and agricultural commodities are harmful to humans and are becoming a health concern nowadays. Detection of pesticide residues on various commodities in an open environment is a challenging task. Hyperspectral sensing is one of the recent technologies used to detect the pesticide residues. This paper addresses the problem of detection of pesticide residues of Cyantraniliprole on grapes in open fields using multi temporal hyperspectral remote sensing data. The reflectance data of 686 samples of grapes with no, single and double dose application of Cyantraniliprole has been collected by handheld spectroradiometer (MS-720) with a wavelength ranging from 350 nm to 1052 nm. The data collection was carried out over a large feature set of 213 spectral bands during the period of March to May 2015. This large feature set may cause model over-fitting problem as well as increase the computational time, so in order to get the most relevant features, various feature selection techniques viz Principle Component Analysis (PCA), LASSO and Elastic Net regularization have been used. Using this selected features, we evaluate the performance of various classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to classify the grape sample with no, single or double application of Cyantraniliprole. The key finding of this paper is; most of the features selected by the LASSO varies between 350-373nm and 940-990nm consistently for all days. Experimental results also shows that, by using the relevant features selected by LASSO, SVM performs better with average prediction accuracy of 91.98 % among all classifiers, for all days.
机译：水果，蔬菜和农产品中的农药残留对人体有害，如今已成为健康问题。在开放环境中检测各种商品上的农药残留是一项艰巨的任务。高光谱传感是用于检测农药残留的最新技术之一。本文探讨了使用多时态高光谱遥感数据在空旷田地中检测葡萄中氰基腈的农药残留的问题。通过手持式分光光度计（MS-720）收集了波长为350 nm至1052 nm的686个葡萄样品的无，单次和双剂量氰基腈的反射率数据。数据收集是在2015年3月至2015年5月期间在213个光谱带的大型特征集上进行的。此大型特征集可能会导致模型过度拟合问题并增加计算时间，因此为了最大程度地获取数据，相关特征，使用了各种特征选择技术，即主成分分析（PCA），LASSO和Elastic Net正则化。使用此选定功能，我们评估了各种分类器的性能，例如人工神经网络（ANN），支持向量机（SVM），随机森林（RF）和极度梯度增强（XGBoost），可对没有，单个或单个Cyantraniliprole的双重应用。本文的主要发现是： LASSO选择的大多数功能在整天中始终在350-373nm和940-990nm之间变化。实验结果还表明，通过使用LASSO选择的相关功能，支持向量机在所有分类器中的所有天均表现更好，平均预测精度为91.98％。

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