| Detection of adulteration in food based on nondestructive analysis techniques: A review Y He, X Bai, Q Xiao, F Liu, L Zhou, C Zhang Critical Reviews in Food Science and Nutrition 61 (14), 2351-2371, 2021 | 96 | 2021 |
| Advanced high-throughput plant phenotyping techniques for genome-wide association studies: A review Q Xiao, X Bai, C Zhang, Y He Journal of advanced research 35, 215-230, 2022 | 78 | 2022 |
| Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning C Zhang, Y Zhao, T Yan, X Bai, Q Xiao, P Gao, M Li, W Huang, Y Bao, ... Infrared Physics & Technology 111, 103550, 2020 | 60 | 2020 |
| Recent progress of nondestructive techniques for fruits damage inspection: a review Y He, Q Xiao, X Bai, L Zhou, F Liu, C Zhang Critical Reviews in Food Science and Nutrition 62 (20), 5476-5494, 2022 | 49 | 2022 |
| Rapid screen of the color and water content of fresh-cut potato tuber slices using hyperspectral imaging coupled with multivariate analysis Q Xiao, X Bai, Y He Foods 9 (1), 94, 2020 | 38 | 2020 |
| Application of convolutional neural network-based feature extraction and data fusion for geographical origin identification of radix astragali by visible/short-wave near … Q Xiao, X Bai, P Gao, Y He Sensors 20 (17), 4940, 2020 | 30 | 2020 |
| Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds X Bai, C Zhang, Q Xiao, Y He, Y Bao RSC advances 10 (20), 11707-11715, 2020 | 27 | 2020 |
| End-to-end fusion of hyperspectral and chlorophyll fluorescence imaging to identify rice stresses C Zhang, L Zhou, Q Xiao, X Bai, B Wu, N Wu, Y Zhao, J Wang, L Feng Plant Phenomics, 2022 | 23 | 2022 |
| Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves Q Xiao, W Tang, C Zhang, L Zhou, L Feng, J Shen, T Yan, P Gao, Y He, ... Plant Phenomics, 2022 | 22 | 2022 |
| Detection of sulfite dioxide residue on the surface of fresh-cut potato slices using near-infrared hyperspectral imaging system and portable near-infrared spectrometer X Bai, Q Xiao, L Zhou, Y Tang, Y He Molecules 25 (7), 1651, 2020 | 21 | 2020 |
| Deep convolution neural network with weighted loss to detect rice seeds vigor based on hyperspectral imaging under the sample-imbalanced condition N Wu, S Weng, J Chen, Q Xiao, C Zhang, Y He Computers and Electronics in Agriculture 196, 106850, 2022 | 17 | 2022 |
| Nondestructive Determination and Visualization of Quality Attributes in Fresh and Dry Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging J He, S Zhu, B Chu, X Bai, Q Xiao, C Zhang, J Gong Applied Sciences 9 (9), 1959, 2019 | 12 | 2019 |
| Phenotypic analysis of diseased plant leaves using supervised and weakly supervised deep learning L Zhou, Q Xiao, MF Taha, C Xu, C Zhang Plant Phenomics 5, 0022, 2023 | 11 | 2023 |
| Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves Q Xiao, N Wu, W Tang, C Zhang, L Feng, L Zhou, J Shen, Z Zhang, P Gao, ... Frontiers in Plant Science 13, 1080745, 2022 | 3 | 2022 |
| Early detection of cotton verticillium wilt based on root magnetic resonance images W Tang, N Wu, Q Xiao, S Chen, P Gao, Y He, L Feng Frontiers in Plant Science 14, 1135718, 2023 | 2 | 2023 |
| Rapid and accurate identification of bakanae pathogens carried by rice seeds based on hyperspectral imaging and deep transfer learning N Wu, S Weng, Q Xiao, H Jiang, Y Zhao, Y He Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 123889, 2024 | 1 | 2024 |
| Erratum to “Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning” L Zhou, Q Xiao, MF Taha, C Xu, C Zhang Plant Phenomics 5, 0033, 2023 | | 2023 |
