Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

Liu, Ziwei; Jiang, Jinbao; Li, Mengquan; Yuan, Deshuai; Nie, Cheng; Sun, Yi; Zheng, Peng

doi:10.3390/foods11081156

Cited by 8 publications

(4 citation statements)

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“…K-nearest neighbor (KNN) is a classification technique used to assign unmarked specimens to categories based on their similarity to labeled specimens [31]. When dealing with unknown samples, the KNN algorithm calculates the distance between the sample and labeled sample of known category.…”

Section: Conventional Machine Learning Methodsmentioning

confidence: 99%

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning

Bai,

Zhou,

Wang

et al. 2024

Foods

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Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model’s classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model’s accuracy. Upon validating the model’s performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.

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Section: Conventional Machine Learning Methodsmentioning

confidence: 99%

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning

Bai,

Zhou,

Wang

et al. 2024

Foods

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“…used MobileViT_v2 as a backbone network to identify wild mushroom species by introducing CA ( Hou et al., 2021 ) blocks and introducing jump connections between the blocks, and the classification accuracy of the obtained Top1 was 97.39%. Liu Z et al ( Liu et al., 2022a ). used KNN ( Cover and Hart, 1967 ), SVM ( Schölkopf and Smola, 2002 ), and MobileViT-xs methods to identify moldy peanuts with improved accuracy of 3.55%, 4.42%, and 5.9%.…”

Section: Introductionmentioning

confidence: 99%

Identification of wheat seedling varieties based on MssiapNet

Feng,

Liu,

Han

et al. 2024

Front. Plant Sci.

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IntroductionIn the actual planting of wheat, there are often shortages of seedlings and broken seedlings on long ridges in the field, thus affecting grain yield and indirectly causing economic losses. Variety identification of wheat seedlings using physical methods timeliness and is unsuitable for universal dissemination. Recognition of wheat seedling varieties using deep learning models has high timeliness and accuracy, but fewer researchers exist. Therefore, in this paper, a lightweight wheat seedling variety recognition model, MssiapNet, is proposed.MethodsThe model is based on the MobileVit-XS and increases the model's sensitivity to subtle differences between different varieties by introducing the scSE attention mechanism in the MV2 module, so the recognition accuracy is improved. In addition, this paper proposes the IAP module to fuse the identified feature information. Subsequently, training was performed on a self-constructed real dataset, which included 29,020 photographs of wheat seedlings of 29 varieties.ResultsThe recognition accuracy of this model is 96.85%, which is higher than the other nine mainstream classification models. Although it is only 0.06 higher than the Resnet34 model, the number of parameters is only 1/3 of that. The number of parameters required for MssiapNet is 29.70MB, and the single image Execution time and the single image Delay time are 0.16s and 0.05s. The MssiapNet was visualized, and the heat map showed that the model was superior for wheat seedling variety identification compared with MobileVit-XS.DiscussionThe proposed model has a good recognition effect on wheat seedling varieties and uses a few parameters with fast inference speed, which makes it easy to be subsequently deployed on mobile terminals for practical performance testing.

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“…used MobileViT_v2 as a backbone network to identify wild mushroom species by introducing CA (Hou et al, 2021) blocks and introducing jump connections between the blocks, and the classification accuracy of the obtained Top1 was 97.39%. Liu Z et al (Liu et al, 2022a). used KNN (Cover and Hart, 1967), SVM (Schölkopf and Smola, 2002), and MobileViT-xs methods to identify moldy peanuts with improved accuracy of 3.55%, 4.42%, and 5.9%.…”

Section: Introductionmentioning

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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Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

Cited by 8 publications

References 50 publications

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning

Identification of wheat seedling varieties based on MssiapNet

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

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