Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

Tong, Long Chang; Bin, Li; Geng, Yanhui; Chen, Lijie; Li, Yanjie; Cao, Ruishu

doi:10.1007/s12161-020-01885-2

Cited by 4 publications

(5 citation statements)

References 42 publications

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“…Since then, through over half a century of development, it has matured and is now widely employed in food, medicine, petrochemical, and other research fields (Mohamed et al, 2018 ; Guo et al, 2020 ). In recent years, NIR spectroscopy technology has become increasingly and broadly used in forestry, for example, to estimate photosynthetic characteristics (Dechant et al, 2017 ), to predict leaf-level nitrogen content (Kokaly, 2001 ), to distinguish bamboo shoots of different qualities (Tong et al, 2020 ), and to name a few applications. Partial least squares (PLS) regression, a quick, efficient, and optimal regression method based on covariance, is a widely used chemometric method, one that combines the advantages of multiple linear regression, canonical correlation analysis, and principal component regression (Tenenhaus et al, 2005 ; Sarker and Nichol, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

et al. 2021

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Drought is a major abiotic stress that adversely affects the growth and productivity of plants. Malondialdehyde (MDA), a substance produced by membrane lipids in response to reactive oxygen species (ROS), can be used as a drought indicator to evaluate the degree of plasma membrane damage and the ability of plants to drought stress tolerance. Still measuring MDA is usually a labor- and time-consuming task. In this study, near-infrared (NIR) spectroscopy combined with partial least squares (PLS) was used to obtain rapid and high-throughput measurements of MDA, and the application of this technique to plant drought stress experiments was also investigated. Two exotic conifer tree species, namely, slash pine (Pinus elliottii) and loblolly pine (Pinus taeda), were used as plant material exposed to drought stress; different types of spectral preprocessing methods and important feature-selection algorithms were applied to the PLS model to calibrate it and obtain the best MDA-predicting model. The results show that the best PLS model is established via the combined treatment of detrended variable–significant multivariate correlation algorithm (DET-sMC), where latent variables (LVs) were 6. This model has a respectable predictive capability, with a correlation coefficient (R2) of 0.66, a root mean square error (RMSE) of 2.28%, and a residual prediction deviation (RPD) of 1.51, and it was successfully implemented in drought stress experiments as a reliable and non-destructive method to detect the MDA content in real time.

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Section: Introductionmentioning

confidence: 99%

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

et al. 2021

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“…In another work, Aggarwal et al [37] presented an image and morphological-based optimized random forest classification method to recognize Indian oxygen plants using SVM, RF, and multilayer perceptron (MLP) classifiers. Tong et al [53] also performed bamboo shoot species classification using the capability of near-infrared reflectance (NIR) spectroscopy and three ML algorithms, which was less expensive and more time-efficient. Three classification models, i.e., SVM, RF, and partial least squares discriminant analysis (PLS-DA), were used.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Three classification models, i.e., SVM, RF, and partial least squares discriminant analysis (PLS-DA), were used. The SVM classifier demonstrated the most significant classification accuracy, reaching 95% in some cases [53].…”

Section: Background and Related Workmentioning

confidence: 99%

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Sawarkar,

Shrimankar,

Ali

et al. 2024

Applied Sciences

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Bamboos, also known as non-timber forest products (NTFPs) and belonging to the family Poaceae and subfamily Bambusoideae, have a wide range of flowering cycles from 3 to 120 years; hence, it is difficult to identify species. Here, the focus is on supervised machine learning (ML) and deep learning (DL) as a potential automated approach for the identification and classification of commercial bamboo species, with the help of the majority multiclass voting (MajMulVot) algorithm. We created an image dataset of 2000 bamboo instances, followed by a texture dataset prepared using local binary patterns (LBP) and gray-level cooccurrence matrix (GLCM)-based methods. First, we deployed five ML models for the texture datasets, where support vector machine (SVM) shows an accuracy rate of 82.27%. We next deployed five DL-based convolutional neural network (CNN) models for bamboo classification, namely AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, using the transfer learning (TL) approach, where VGG16 prevails, with an accuracy rate of 88.75%. Further, a MajMulVot-based ensemble approach was introduced to improve the classification accuracy of all ML- and DL-based models. The ML-MajMulVot enhanced the accuracy for the texture dataset to 86.96%. In the same way, DL-MajMulVot increased the accuracy to 92.8%. We performed a comparative analysis of all classification models with and without K-fold cross-validation and MajMulVot methods. The proposed findings indicate that even difficult-to-identify species may be identified accurately with adequate image datasets. The suggested technology can also be incorporated into a mobile app to offer farmers effective agricultural methods.

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“…It was found that the SVM model with second derivative treatment performed the best. The combination of NIR spectra and the SVM method provides a fast and non-destructive method for the classification of bamboo shoot species [28]. A portable NIR sensor was used to predict the freshness of eggs.…”

Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

Storage Time Detection of Torreya grandis Kernels Using Near Infrared Spectroscopy

2023

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To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times.

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Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods

Cited by 4 publications

References 42 publications

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy

Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm

Storage Time Detection of Torreya grandis Kernels Using Near Infrared Spectroscopy

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