Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches

Cai, Zeyi; Huang, Zihong; He, Mengyu; Li, Cheng; Qi, Hengnian; Peng, Jiyu; Zhou, Fei; Zhang, Chu

doi:10.1016/j.foodchem.2023.136169

Cited by 30 publications

(8 citation statements)

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“…However, in the previous study, the LLF strategy had better predictive ability in predicting TVB-N content in chicken than the optimal model for single-spectral data. Our results yielded different conclusions from this, which could be attributed to the large amount of data introduced by LLF, leading to the computational complexity and uncertainty of the PLSR method [21,41].…”

Section: Quantitative Analysis Based On Llf Data and Image Featurescontrasting

confidence: 80%

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Jiang,

Lv,

Zhong

et al. 2023

Foods

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Atractylodis rhizoma (AR) is an herb and food source with great economic, medicinal, and ecological value. Atractylodes chinensis (DC.) Koidz. (AC) and Atractylodes lancea (Thunb.) DC. (AL) are its two botanical sources. The commercial fraud of AR adulterated with Atractylodes japonica Koidz. ex Kitam (AJ) frequently occurs in pursuit of higher profit. To quickly determine the content of adulteration in AC and AL powder, two spectroscopic techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI), were introduced. The partial least squares regression (PLSR) algorithm was selected for predictive modeling of AR adulteration levels. Preprocessing and feature variable extraction were used to optimize the prediction model. Then data and image feature fusions were developed to obtain the best predictive model. The results showed that if only single-spectral techniques were considered, NIRS was more suitable for both tasks than HSI techniques. In addition, by comparing the models built after the data fusion of NIRS and HSI with those built by the single spectrum, we found that the mid-level fusion strategy obtained the best models in both tasks. On this basis, combined with the color-texture features, the prediction ability of the model was further optimized. Among them, for the adulteration level prediction task of AC, the best strategy was combining MLF data (at CARS level) and color-texture features (C-TF), at which time the R2T, RMSET, R2P, and RMSEP were 99.85%, 1.25%, 98.61%, and 5.06%, respectively. For AL, the best approach was combining MLF data (at SPA level) and C-TF, with the highest R2T (99.92%) and R2P (99.00%), as well as the lowest RMSET (1.16%) and RMSEP (2.16%). Therefore, combining data and image features from NIRS and HSI is a potential strategy to predict the adulteration content quickly, non-destructively, and accurately.

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Section: Quantitative Analysis Based On Llf Data and Image Featurescontrasting

confidence: 80%

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Jiang,

Lv,

Zhong

et al. 2023

Foods

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“…CNNs are a "black box" model in the field of deep learning, and in order to make it easier to interpret CNN results, Gradientweighted Class Activation Mapping++ (Grad-CAM++) was used to visualize CNN models in this study. Grad-CAM++ is a generalized approach based on Grad-CAM that better provides visualization results for CNNs (Cai et al, 2023). It uses the gradient of any target concept flowing into the final convolutional layer to generate a coarse localization map that highlights important areas of the image for predicting the concept (Moujahid et al, 2022).…”

Section: Visualization Methodsmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Orlov

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…CNNs are a “black box” model in the field of deep learning, and in order to make it easier to interpret CNN results, Gradient-weighted Class Activation Mapping++ (Grad-CAM++) was used to visualize CNN models in this study. Grad-CAM++ is a generalized approach based on Grad-CAM that better provides visualization results for CNNs ( Cai et al., 2023 ). It uses the gradient of any target concept flowing into the final convolutional layer to generate a coarse localization map that highlights important areas of the image for predicting the concept ( Moujahid et al., 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning

Qi,

Huang,

Sun

et al. 2023

Front. Plant Sci.

Self Cite

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Vigor is one of the important factors that affects rice yield and quality. Rapid and accurate detection of rice seed vigor is of great importance for rice production. In this study, near-infrared hyperspectral imaging technique and transfer learning were combined to detect rice seed vigor. Four varieties of artificial-aged rice seeds (Yongyou12, Yongyou1540, Suxiangjing100, and Longjingyou1212) were studied. Different convolutional neural network (CNN) models were built to detect the vigor of the rice seeds. Two transfer strategies, fine-tuning and MixStyle, were used to transfer knowledge among different rice varieties for vigor detection. The experimental results showed that the convolutional neural network model of Yongyou12 classified the vigor of Yongyou1540, Suxiangjing100, and Longjingyou1212 through MixStyle transfer knowledge, and the accuracy reached 90.00%, 80.33%, and 85.00% in validation sets, respectively, which was better or close to the initial modeling performances of each variety. MixStyle statistics are based on probabilistic mixed instance-level features of cross-source domain training samples. When training instances, new domains can be synthesized, which increases the domain diversity of the source domain, thereby improving the generalization ability of the trained model. This study would help rapid and accurate detection of a large varieties of crop seeds.

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Identification of geographical origins of Radix Paeoniae Alba using hyperspectral imaging with deep learning-based fusion approaches

Cited by 30 publications

References 50 publications

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Rapid Prediction of Adulteration Content in Atractylodis rhizoma Based on Data and Image Features Fusions from Near-Infrared Spectroscopy and Hyperspectral Imaging Techniques

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning

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