Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

Liu, Weihua; Zeng, Shan; Guiju, Wu; Li, Hao; Chen, Feifei

doi:10.3390/s21134384

Cited by 30 publications

(16 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. In this study, classic machine learning and deep learning methods, CNN, ResNet, LR, SVM, and RF, were used to achieve a multivariate analysis of the detection of pesticide residue levels in grapes.…”

Section: Discussionmentioning

confidence: 99%

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

Yan

Zhang

et al. 2022

Foods

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Yan

Zhang

et al. 2022

Foods

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to preprocess the hyperspectral data to remove undesired information, such as uneven illumination, background, and bad pixels. The commonly used pretreatment algorithms include Savitzky–Golay smoothing (SG), standard normal variable (SNV), multiplicative scatter correction (MSC), and first derivative (FD) [ 20 , 21 ]. SG smoothing estimates the average spectral value of a particular wavelength point based on several wavelengths before and after the point with the moving average algorithm or least-squares fitting method, which is then used to replace the original spectral value.…”

Section: Methodsmentioning

confidence: 99%

A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology

Kang

Huang

Zeng

et al. 2022

Sensors

View full text Add to dashboard Cite

Hyperspectral imaging can simultaneously acquire spectral and spatial information of the samples and is, therefore, widely applied in the non-destructive detection of grain quality. Supervised learning is the mainstream method of hyperspectral imaging for pixel-level detection of mildew in corn kernels, which requires a large number of training samples to establish the prediction or classification models. This paper presents an unsupervised redundant co-clustering algorithm (FCM-SC) based on multi-center fuzzy c-means (FCM) clustering and spectral clustering (SC), which can effectively detect non-uniformly distributed mildew in corn kernels. This algorithm first carries out fuzzy c-means clustering of sample features, extracts redundant cluster centers, merges the cluster centers by spectral clustering, and finally finds the category of corresponding cluster centers for each sample. It effectively solves the problems of the poor ability of the traditional fuzzy c-means clustering algorithm to classify the data with complex structure distribution and the complex calculation of the traditional spectral clustering algorithm. The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.

show abstract

“…Applications of logistic regression are diverse, including processing of tomography [17], detection of rice seed purity [18], classification of scenes [19], spectral and spatialbased classification [20], prediction of axillary lymph node metastases [21], active smoking and associated behavioral risk factors before and during pregnancy [22]. BLR belongs to the class of logistic regression.…”

Section: State-of-the-art Applications Of Binary Logistic Regressionmentioning

confidence: 99%

Method for Data Quality Assessment of Synthetic Industrial Data

Iantovics

Enăchescu

2022

Sensors

View full text Add to dashboard Cite

Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.

show abstract

Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

Cited by 30 publications

References 61 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

A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology

Method for Data Quality Assessment of Synthetic Industrial Data

Contact Info

Product

Resources

About