Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Chen, Yi; Bin, Jun; Zou, Congming; Ding, Mengjiao

doi:10.1155/2021/9912589

Cited by 25 publications

(10 citation statements)

References 51 publications

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“…They extracted the features of flue-cured tobacco leaves by fusing apparent features and low-dimensional deep features, and the experiments showed that applying the fused features improved the accuracy of tobacco-grading compared with just apparent or deep features. Chen et al [30] utilized a discriminant technique for tobacco leaf maturity level based on NIR spectroscopy combined with a deep learning approach of CNNs, in which the experiment results showed that the proposed model outperformed KNN, BPNN, SVM, and extreme learning machine (ELM) models. In [31], a CNN-based method was applied to detect the moisture of tobacco during the drying process.…”

Section: Related Workmentioning

confidence: 99%

Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method

Yang

2021

IEEE Access

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The research of the bulk tobacco flue-curing process is helpful to model the intelligent bulk curing system, which is designed to implement the curing procedure without manual operation. An intelligent bulk curing method based on the convolutional neural network (CNN) named TobaccoNet was proposed, which could set the target dry-bulb temperature (TD) and the target wet-bulb temperature (TW) of the bulk curing barn according to the tobacco leaves image. The performance of the TobaccoNet is compared with the traditional manual image feature extraction method, the stacked-sparse-autoencoder (SSAE)-based deep learning method, and the other two methods applied in related references. The test results show that TobaccoNet outperforms the comparison methods in predicting TD and TW. Specifically, the correlation coefficient reaches 0.9965 and 0.9683, the mean relative error is 1.62% and 1.77%, and the root mean squared error achieves 1.061 °C and 0.8581 °C respectively. The promising results demonstrate that the TobaccoNet is effective and reliable for modeling the intelligent bulk tobacco flue-curing process. The influence of different CNN structures on the prediction accuracy of TD and TW was analyzed. From the perspective of the computational complexity and the prediction performance, the proposed sequential CNN structure is more suitable for analyzing bulk tobacco curing in this study.INDEX TERMS Convolutional neural network, bulk tobacco flue-curing, tobacco leaves, deep learning.

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Section: Related Workmentioning

confidence: 99%

Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method

Yang

2021

IEEE Access

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“…It can better fit nonlinear data and discover more complex relationships between variables, making it suitable for various complex prediction and classification problems. Typical nonlinear classification modeling methods include KNN algorithm [16] , Support Vector Classification (SVC) [17] , Random forest (RF) [18] , Gradient Boosting Tree (GBT) [19] and Extreme Learning Machine [20] .…”

Section: Introductionmentioning

confidence: 99%

Rapid identification of adulterated rice using fusion of near-infrared spectroscopy and machine vision data: the combination of feature optimization and nonlinear modeling

Song,

Liu,

Wang

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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Rice is susceptible to mold and mildew during storage. Metabolites such as aflatoxin produced during mildew will do great harm to consumers. To meet the need for rapid detection of normal rice adulterated with moldy rice, a rapid identification method of adulterated rice was established based on data fusion of near-infrared spectroscopy and machine vision. Using competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), and least angle regression (LARS) for spectral and image feature extraction, combined with support vector classification (SVC), random forest (RF), and gradient boosting tree (GBT) nonlinear discriminant models, and use Bayesian search to optimize modeling parameters. The results show that the GBT fusion data model established by LARS optimization of spectral and image feature variables has the highest discrimination accuracy, with recognition accuracy rates of 100.00% and 98.11% for its training and testing sets, respectively. The discrimination performance is significantly improved compared to single near-infrared spectroscopy and machine vision. The results indicate that rapid identification of adulterated rice based on near-infrared spectroscopy and machine vision data fusion technology is feasible, providing theoretical support for the development of online identification equipment for adulterated rice.

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“…In recent years, machine vision-based deep learning methods have provided advanced and efficient image processing solutions in agriculture. Deep learning methods, combined with machine vision technology, have been widely used in plant disease and pest classification, including the classification of fresh tobacco leaves of various maturity levels ( Chen et al., 2021 ); the classification of tobacco plant diseases ( Lin et al., 2022 ); the classification of wheat spike blast ( Fernández-Campos et al., 2021 ); the classification of rice pests and diseases ( Yang et al., 2021 ); the detection of plant parts such as tobacco leaves and stems ( Li et al., 2021 ); the detection of tomato diseases ( Liu et al., 2022 ); the detection of wheat head diseases ( Gong et al., 2020 ); the detection of brown planthoppers in rice ( He et al., 2020 ); plant image segmentation, such as tobacco planting areas segmentation ( Huang et al., 2021 ); field-grown wheat spikes segmentation ( Tan et al., 2020 ); rice ear segmentation ( Bai-yi et al., 2020 ; Shao et al., 2021 ); rice lodging segmentation ( Su et al., 2022 ); photosynthetic and non-photosynthetic vegetation segmentation ( He et al., 2022 ); weed and crop segmentation ( Hashemi-Beni et al., 2022 ); and wheat spike segmentation ( Wen et al., 2022 ). Deep learning methods combined with machine vision technology have been utilized in research focused on the classification of tobacco shred images.…”

Section: Introductionmentioning

confidence: 99%

Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm

Wang

Jia

et al. 2023

Front. Plant Sci.

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IntroductionThe classification of the four tobacco shred varieties, tobacco silk, cut stem, expanded tobacco silk, and reconstituted tobacco shred, and the subsequent determination of tobacco shred components, are the primary tasks involved in calculating the tobacco shred blending ratio. The identification accuracy and subsequent component area calculation error directly affect the composition determination and quality of the tobacco shred. However, tiny tobacco shreds have complex physical and morphological characteristics; in particular, there is substantial similarity between the expanded tobacco silk and tobacco silk varieties, and this complicates their classification. There must be a certain amount of overlap and stacking in the distribution of tobacco shreds on the actual tobacco quality inspection line. There are 24 types of overlap alone, not to mention the stacking phenomenon. Self-winding does not make it easier to distinguish such varieties from the overlapped types, posing significant difficulties for machine vision-based tobacco shred classification and component area calculation tasks.MethodsThis study focuses on two significant challenges associated with identifying various types of overlapping tobacco shreds and acquiring overlapping regions to calculate overlapping areas. It develops a new segmentation model for tobacco shred images based on an improved Mask region-based convolutional neural network (RCNN). Mask RCNN is used as the segmentation network’s mainframe. Convolutional network and feature pyramid network (FPN) in the backbone are replaced with Densenet121 and U-FPN, respectively. The size and aspect ratios of anchors parameters in region proposal network (RPN) are optimized. An algorithm for the area calculation of the overlapped tobacco shred region (COT) is also proposed, which is applied to overlapped tobacco shred mask images to obtain overlapped regions and calculate the overlapped area.ResultsThe experimental results showed that the final segmentation accuracy and recall rates are 89.1% and 73.2%, respectively. The average area detection rate of 24 overlapped tobacco shred samples increases from 81.2% to 90%, achieving high segmentation accuracy and overlapped area calculation accuracy.DiscussionThis study provides a new implementation method for the type identification and component area calculation of overlapped tobacco shreds and a new approach for other similar overlapped image segmentation tasks.

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Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Cited by 25 publications

References 51 publications

Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method

Modeling of the Bulk Tobacco Flue-Curing Process Using a Deep Learning-Based Method

Rapid identification of adulterated rice using fusion of near-infrared spectroscopy and machine vision data: the combination of feature optimization and nonlinear modeling

Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm

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