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publications

Identification of hybrid okra seeds based on near-infrared hyperspectral imaging technology

Published in Applied Sciences-Basel, 2018

Recommended citation: Zhang, Jinnuo, et al. "Identification of hybrid okra seeds based on near-infrared hyperspectral imaging technology." Applied Sciences 8.10 (2018): 1793. https://www.mdpi.com/2076-3417/8/10/1793

Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning

Published in Sensors and Actuators B: Chemical, 2019

Recommended citation: Nie, Pengcheng, et al. "Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning." Sensors and Actuators B: Chemical 296 (2019): 126630. https://www.sciencedirect.com/science/article/pii/S0925400519308251

Identification of bacterial blight resistant rice seeds using terahertz imaging and hyperspectral imaging combined with convolutional neural network

Published in Plant Methods, 2020

Recommended citation: Zhang, Jinnuo, et al. "Identification of bacterial blight resistant rice seeds using terahertz imaging and hyperspectral imaging combined with convolutional neural network." Frontiers in Plant Science 11 (2020): 821. https://www.frontiersin.org/articles/10.3389/fpls.2020.00821/full

Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning

Published in RSC advances, 2020

Recommended citation: Yang, Yong, et al. "Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning." RSC advances 10.72 (2020): 44149-44158. https://pubs.rsc.org/en/content/articlehtml/2020/ra/d0ra06938h

Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Published in Plant Methods, 2022

Recommended citation: Zhang, Jinnuo, et al. "Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning." Plant Methods 18.1 (2022): 1-16. https://plantmethods.biomedcentral.com/articles/10.1186/s13007-022-00882-2

Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing

Published in Remote Sensing, 2023

Recommended citation: Zhang, Jinnuo, et al. "Visible and Near-Infrared Hyperspectral Diurnal Variation Calibration for Corn Phenotyping Using Remote Sensing." Remote Sensing 15.12 (2023): 3057. https://www.mdpi.com/2072-4292/15/12/3057

Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping

Published in Plant Phenomics, 2023

Recommended citation: Zhang, Jinnuo, et al. "Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping." Plant Phenomics 5 (2023): 0071. https://spj.science.org/doi/full/10.34133/plantphenomics.0071

A high-precision spatial and spectral imaging solution for accurate corn nitrogen content level prediction at early vegetative growth stages

Published in Computers and Electronics in Agriculture, 2025

Recommended citation: Zhang, Jinnuo, et al. "A high-precision spatial and spectral imaging solution for accurate corn nitrogen content level prediction at early vegetative growth stages." Computers and Electronics in Agriculture 230 (2025): 109940. https://www.sciencedirect.com/science/article/pii/S0168169925000468