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Pesticide residue detection in pakchoi using a 1D-CNN enhanced by data augmentation and spectral attention

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Outgoing claims: the papers this work cites, builds on, or argues with.

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Principles and Applications of Miniaturized Near‐Infrared (NIR) Spectrometers
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Determination of pesticide residual levels in strawberry (Fragaria) by near‐infrared spectroscopy
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Mango varietal discrimination using hyperspectral imaging and machine learning
inferredcites →https://doi.org/10.1007/s11694-021-01012-7openalex-snapshot
Non-destructive detection and recognition of pesticide residues on garlic chive (Allium tuberosum) leaves based on short wave infrared hyperspectral imaging and one-dimensional convolutional neural network
inferredcites →https://doi.org/10.1007/s11947-022-02880-7openalex-snapshot
Convolutional Neural Networks for Estimating the Ripening State of Fuji Apples Using Visible and Near-Infrared Spectroscopy
inferredcites →https://doi.org/10.1016/j.atech.2025.100954openalex-snapshot
Enhancing rice disease and insect-pest detection through augmented deep learning with transfer learning techniques
inferredcites →https://doi.org/10.1016/j.foodres.2024.115010openalex-snapshot
Fusion features of microfluorescence hyperspectral imaging for qualitative detection of pesticide residues in Hami melon
inferredcites →https://doi.org/10.1016/j.jfca.2021.104346openalex-snapshot
Hyperspectral imaging combined with generative adversarial network (GAN)-based data augmentation to identify haploid maize kernels
inferredcites →https://doi.org/10.1016/j.jfca.2025.107199openalex-snapshot
Rapid non-destructive detection of pork freshness using visible-near infrared spectroscopy based on convolutional neural network hybrid models
inferredcites →https://doi.org/10.1016/j.jfca.2025.107264openalex-snapshot
A CNN-BiGRU-selfattention model combined with GAN reconstruction and Reverse Feature Fusion for apple pesticide residues detecting
inferredcites →https://doi.org/10.1016/j.jfca.2025.108682openalex-snapshot
Detection of imidacloprid in spinach using spectral technology and machine learning
inferredcites →https://doi.org/10.1016/j.jfca.2025.108781openalex-snapshot
WGAN-GP augmented hyperspectral framework for pest infestation grading in stored Astragalus membranaceus
inferredcites →https://doi.org/10.1016/j.jia.2024.03.075openalex-snapshot
E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis
inferredcites →https://doi.org/10.1016/s0925-5214(01)00180-6openalex-snapshot
Vis/NIR estimation at harvest of pre- and post-storage quality indices for ‘Royal Gala’ apple
inferredcites →https://doi.org/10.1021/acsami.2c17954openalex-snapshot
Novel Microneedle Patch-Based Surface-Enhanced Raman Spectroscopy Sensor for the Detection of Pesticide Residues
inferredcites →https://doi.org/10.1021/acsami.4c17777openalex-snapshot
Leveraging SERS and Transformer Models for Simultaneous Detection of Multiple Pesticides in Fresh Produce
inferredcites →https://doi.org/10.1049/cit2.12154openalex-snapshot
Frequency‐to‐spectrum mapping GAN for semisupervised hyperspectral anomaly detection
inferredcites →https://doi.org/10.1080/19440049.2015.1031192openalex-snapshot
Non-destructive detection of pesticide residues in cucumber using visible/near-infrared spectroscopy
inferredcites →https://doi.org/10.1089/ast.2024.0127openalex-snapshot
Remote Detection of Red Edge Spectral Characteristics in Floating Aquatic Vegetation
inferredcites →https://doi.org/10.1111/1750-3841.16728openalex-snapshot
Non‐destructive detection and recognition of pesticide residue levels on cauliflowers using visible/near‐infrared spectroscopy combined with chemometrics
inferredcites →https://doi.org/10.1145/3422622openalex-snapshot
Generative adversarial networks
inferredcites →https://doi.org/10.3390/foods12020300openalex-snapshot
Non-Destructive Detection of Meat Quality Based on Multiple Spectral Dimension Reduction Methods by Near-Infrared Spectroscopy
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