NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

Zhang, Liu; Rao, Zhenhong; Ji, Haiyan

doi:10.3390/s19143147

Cited by 28 publications

(9 citation statements)

References 24 publications

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“…Due to the redundancy and high volumes of hyperspectral data, machine learning and deep learning were used to process the data and extract features. Previous studies have used SVM [ 22 , 59 ], DT [ 59 ], KNN [ 59 ], or RF [ 18 ] to detect pesticide residue, which showed fine results. SVM [ 60 , 61 , 62 ], LR [ 63 ], CNN [ 31 , 56 , 64 ], RF [ 60 , 62 ], and ResNet [ 56 ] have been applied widely in quality detection of hyperspectral imaging.…”

Section: Discussionmentioning

confidence: 99%

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Yan

Zhang

et al. 2022

Foods

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Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.

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Section: Discussionmentioning

confidence: 99%

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Yan

Zhang

et al. 2022

Foods

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“…Metric learning is a type of mechanism to combine features to compare observations effectively. There are many types of metric learning models, such as stochastic neighbor embedding (SNE) [ 44 ], locally linear embeddings (LLE) [ 45 ], mahalanobis metric for clustering (MMC) [ 46 ], and neighborhood component analysis (NCA) [ 47 ]. The first two are unsupervised, and the latter two are supervised.…”

Section: Methodsmentioning

confidence: 99%

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Wang

Chen

et al. 2021

Biosensors

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Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing.

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“…LDA ensures that the intra-class variance of each class is small and the inter-class mean disparity is large. NCA learns the optimal linear transformation matrix for data dimensional reduction by continuously raising the accuracy of the KNN algorithm based on the Mahalanobis distance measurement [33]. They are both supervised learning methods, so they must be combined with ML methods to attain reflectance-based disease severity classification.…”

Section: Feature Extractionmentioning

confidence: 99%

Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy

Hou

Zhang

et al. 2022

Agriculture

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Late blight caused by Phytophthora infestans is a destructive disease in potato production, which can lead to crop failure in severe cases. This study combined visible/near-infrared (Vis/NIR) spectroscopy with machine learning (ML) and chemometric methods for rapid detection of potato late blight. The determination of disease severity was accomplished by two methods directly or indirectly based on differences in reflectance. One approach was to utilize ML algorithms to build a model that directly reflects the relationship between disease level and spectral reflectance. Another method was to first use partial least squares to construct a predictive model of internal physicochemical values, such as relative chlorophyll content (SPAD) and peroxidase (POD) activity, and then use an ML model to classify disease levels based on the predicted values. The classification accuracy based on these two methods could reach up to 99 and 95%, respectively. The changes in physicochemical values during the development of disease were further investigated. Regression models for fitting changes in SPAD value and POD activity were developed based on temperature and incubation time, with determination coefficients of 0.961 and 0.997, respectively. The prediction of epidemic period was realized by combining regression and classification models based on physicochemical values with an accuracy of 88.5%. It is demonstrated that rapid non-destructive determination of physicochemical values based on Vis/NIR spectroscopy for potato late blight detection is feasible. Furthermore, it is possible to guide the control of disease throughout the epidemic period.

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NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

Cited by 28 publications

References 24 publications

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Potato Late Blight Severity and Epidemic Period Prediction Based on Vis/NIR Spectroscopy

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