Estimation of chlorophyll content of Cinnamomum camphora leaves based on hyperspectral and fractional order differentiation

References

• Aasen, H., M. L. Gnyp, Y. Miao, and G. Bareth. 2014. “Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with Hypercor.” Photogrammetric Engineering 80 (8): 785–795. https://doi.org/10.14358/PERS.80.8.785.
   Web of Science ®Google Scholar
• An, D., G. Zhao, C. Chang, Z. Wang, P. Li, T. Zhang, and J. Jia. 2016. “Hyperspectral Field Estimation and Remote-Sensing Inversion of Salt Content in Coastal Saline Soils of the Yellow River Delta.” International Journal of Remote Sensing 37 (2): 455–470. https://doi.org/10.1080/01431161.2015.1129562.
   Web of Science ®Google Scholar
• Bartold, M., M. Kluczek. 2023. “A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands.” Remote Sensing 15 (9): 2392. https://doi.org/10.3390/rs15092392.
   Web of Science ®Google Scholar
• Cao, Y., K. Jiang, J. Wu, F. Yu, W. Du, T. Xu, and W. Li. 2020. “Inversion Modeling of Japonica Rice Canopy Chlorophyll Content with UAV Hyperspectral Remote Sensing.” Public Library of Science One 15 (9): e0238530. https://doi.org/10.1371/journal.pone.0238530.
   Google Scholar
• Chen, D., F. Zhang, M. L. Tan, N. W. Chan, J. Shi, C. Liu, and W. Wang. 2022. “Improved Na+ Estimation from Hyperspectral Data of Saline Vegetation by Machine Learning.” Computers and Electronics in Agriculture 196:106862. https://doi.org/10.1016/j.compag.2022.106862.
   Web of Science ®Google Scholar
• Chlingaryan, A., S. Sukkarieh, and B. Whelan. 2018. “Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation in Precision Agriculture: A Review.” Computers and Electronics in Agriculture 151:61–69. https://doi.org/10.1016/j.compag.2018.05.012.
   Web of Science ®Google Scholar
• Cui, S., K. Zhou, R. Ding, Y. Cheng, and G. Jiang. 2022. “Estimation of Soil Copper Content Based on Fractional-Order Derivative Spectroscopy and Spectral Characteristic Band Selection. Spectrochim Acta a Mol Biomol Spectrosc 275.” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 275:121190. https://doi.org/10.1016/j.saa.2022.121190.
   PubMed Web of Science ®Google Scholar
• Fan, H., S. Liu, J. Li, L. Li, L. Dang, T. Ren, and J. Lu. 2021. “Early Prediction of the Seed Yield in Winter Oilseed Rape Based on the Near-Infrared Reflectance of Vegetation (NIRv).” Computers and Electronics in Agriculture 186:106166. https://doi.org/10.1016/j.compag.2021.106166.
   Web of Science ®Google Scholar
• Fazmiya, M. J. A., A. Sultana, K. Rahman, M. B. Heyat, S. Bin, F. Akhtar, S. Khan, S. C. Y. Appiah, and T. Hussain. 2022. “Current Insights on Bioactive Molecules, Antioxidant, Anti-Inflammatory, and Other Pharmacological Activities of Cinnamomum Camphora Linn.” Oxidative Medicine and Cellular Longevity 2022:1–23. https://doi.org/10.1155/2022/9354555.
   Web of Science ®Google Scholar
• Hou, L., X. Li, and F. Li. 2019. “Hyperspectral-Based Inversion of Heavy Metal Content in the Soil of Coal Mining Areas.” Journal of Environmental Quality 48 (1): 57–63. https://doi.org/10.2134/jeq2018.04.0130.
   PubMed Web of Science ®Google Scholar
• Jiang, B., H. Liu, Q. Xing, J. Cai, X. Zheng, L. Li, S. Liu, Z. Zheng, H. Xu, and L. Meng. 2021. “Evaluating Traditional Empirical Models and BPNN Models in Monitoring the Concentrations of Chlorophyll-A and Total Suspended Particulate of Eutrophic and Turbid Waters.” Water (Switzerland) 13 (5): 650. https://doi.org/10.3390/w13050650.
   Google Scholar
• Jin, J., and Q. Wang. 2022. “Hyperspectral Indices Developed from the Low Order Fractional Derivative Spectra Can Capture Leaf Dry Matter Content Across a Variety of Species Better.” Agricultural and Forest Meteorology 322. https://doi.org/10.1016/j.agrformet.2022.109007.
   Web of Science ®Google Scholar
• Kluczek, M., B. Zagajewski, and T. Zwijacz-Kozica. 2023. “Mountain Tree Species Mapping Using Sentinel-2,planetscope, and Airborne HySpexhyperspectral Imagery.” Remote Sensing 15 (3): 15. https://doi.org/10.3390/rs15030844.
   Web of Science ®Google Scholar
• Li, Z., Z. Chen, Q. Cheng, F. Duan, R. Sui, X. Huang, and H. Xu. 2022. “UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat.” Agronomy 12 (1): 202. https://doi.org/10.3390/agronomy12010202.
   Google Scholar
• Liang, T., S. Yang, Q. Feng, B. Liu, R. Zhang, X. Huang, and H. Xie. 2016. “Multi-Factor Modeling of Above-Ground Biomass in Alpine Grassland: A Case Study in the Three-River Headwaters Region, China.” Remote Sensing of Environment 186:164–172. https://doi.org/10.1016/j.rse.2016.08.014.
   Web of Science ®Google Scholar
• Lin, H., L. Liang, L. Zhang, and P. Du. 2013. “Wheat Leaf Area Index Inversion with Hyperspectral Remote Sensing Based on Support Vector Regression Algorithm.” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 29. https://doi.org/10.3969/j.issn.1002-6819.2013.11.018.
   Google Scholar
• Liu, N., Z. Xing, R. Zhao, L. Qiao, M. Li, G. Liu, and H. Sun. 2020. “Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization.” Remote Sensing 12 (17): 12. https://doi.org/10.3390/rs12172826.
   Web of Science ®Google Scholar
• Liu, S., H. Y. Yu, J. H. Zhang, H. G. Zhou, L. J. Kong, L. Zhang, J. M. Dang, and Y. Y. Sui. 2021. “Study on Inversion Model of Chlorophyll Content in Soybean Leaf Based on Optimal Spectral Indices.” Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis 41. https://doi.org/10.3964/j.issn.1000-0593(2021)06-1912-08.
   Google Scholar
• Liu, X., Lin, H., Wan, L., Sun, H., Xiong, Y., Mo, D. 2007 .“Hyper-spectral estimation models of the chlorophyll contents of young camphor trees based on remote sensing date.” Journal of Central South University of Forestry and Technology 27 (4): 49–54. https://doi.org/10.14067/j.cnki.1673-923x.2007.04.022
   Google Scholar
• Liu, Y., H. Feng, J. Yue, Y. Fan, X. Jin, Y. Zhao, X. Song, H. Long, and G. Yang. 2022. “Estimation of Potato Above-Ground Biomass Using UAV-Based Hyperspectral Images and Machine-Learning Regression.” Remote Sensing 14 (21): 14. https://doi.org/10.3390/rs14215449.
   Web of Science ®Google Scholar
• Seelan, S. K., S. Laguette, G. M. Casady, and G. A. Seielstad. 2003. “Remote Sensing Applications for Precision Agriculture: A Learning Community Approach.” Remote Sensing of Environment 88 (1–2): 157–169. https://doi.org/10.1016/j.rse.2003.04.007.
   Web of Science ®Google Scholar
• Sharma, R. C. 2022. “Dominant Species-Physiognomy-Ecological (DSPE) System for the Classification of Plant Ecological Communities from Remote Sensing Images.” Ecologies 3 (3): 323–335. https://doi.org/10.3390/ecologies3030025.
   Google Scholar
• Shen, L., M. Gao, J. Yan, Q. Wang, and H. Shen. 2022. “Winter Wheat SPAD Value Inversion Based on Multiple Pretreatment Methods.” Remote Sensing 14 (18): 14. https://doi.org/10.3390/RS14184660.
   Web of Science ®Google Scholar
• Shi, H., J. Guo, J. An, Z. Tang, X. Wang, W. Li, X. Zhao, et al. 2023. “Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index.” Agronomy 13 (3): 663. https://doi.org/10.3390/agronomy13030663.
   Google Scholar
• Sinhal, A., and B. Verma. 2013. “A Distribution Based Approach of Outlier Removal for Software Effort Data.” International Journal of Computer Application 74 (20): 24–28. https://doi.org/10.5120/13027-0210.
   Google Scholar
• Sonobe, R., Y. Sugimoto, R. Kondo, H. Seki, E. Sugiyama, Y. Kiriiwa, and K. Suzuki. 2021. “Hyperspectral Wavelength Selection for Estimating Chlorophyll Content of Muskmelon Leaves.” European Journal of Remote Sensing 54 (1): 513–524. https://doi.org/10.1080/22797254.2021.1964383.
   Web of Science ®Google Scholar
• Srivastava, R., M. Sethi, R. K. Yadav, D. S. Bundela, M. Singh, S. Chattaraj, S. K. Singh, et al. 2017. “Visible-Near Infrared Reflectance Spectroscopy for Rapid Characterization of Salt-Affected Soil in the Indo-Gangetic Plains of Haryana, India.” Journal of the Indian Society of Remote Sensing 45 (2): 307–315. https://doi.org/10.1007/S12524-016-0587-0.
   Web of Science ®Google Scholar
• Sun, J., W. Yang, M. Zhang, M. Feng, L. Xiao, and G. Ding. 2021. “Estimation of Water Content in Corn Leaves Using Hyperspectral Data Based on Fractional Order Savitzky-Golay Derivation Coupled with Wavelength Selection.” Computers and Electronics in Agriculture 182:105989. https://doi.org/10.1016/j.compag.2021.105989.
   Web of Science ®Google Scholar
• Yang, H., Y. Hu, Z. Zheng, Y. Qiao, B. Hou, and J. Chen. 2022. “A New Approach for Nitrogen Status Monitoring in Potato Plants by Combining RGB Images and SPAD Measurements. Remote Sens (Basel) 14.” Remote Sensing 14 (19): 4814. https://doi.org/10.3390/rs14194814.
   Web of Science ®Google Scholar
• Yuan, Z., Y. Ye, L. Wei, X. Yang, and C. Huang. 2022. “Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf Spad Value.” Sensors 22 (1): 183. https://doi.org/10.3390/s22010183.
   Web of Science ®Google Scholar
• Zhang, F., and G. Zhou. 2019. “Estimation of Vegetation Water Content Using Hyperspectral Vegetation Indices: A Comparison of Crop Water Indicators in Response to Water Stress Treatments for Summer Maize.” BMC Ecology 19 (1). https://doi.org/10.1186/S12898-019-0233-0.
   Google Scholar
• Zhang, J., X. Jing, X. Song, T. Zhang, W. N. Duan, and J. Su. 2023. “Hyperspectral Estimation of Wheat Stripe Rust Using Fractional Order Differential Equations and Gaussian Process Methods.” Computers and Electronics in Agriculture 206:107671. https://doi.org/10.1016/j.compag.2023.107671.
   Web of Science ®Google Scholar
• Zhou, W., H. Yang, L. Xie, H. Li, L. Huang, Y. Zhao, and T. Yue. 2021. “Hyperspectral Inversion of Soil Heavy Metals in Three-River Source Region Based on Random Forest Model.” Catena (Amst) 202:202. https://doi.org/10.1016/J.CATENA.2021.105222.
   Web of Science ®Google Scholar
• Zhu, C., J. Ding, Z. Zhang, J. Wang, Z. Wang, X. Chen, and J. Wang. 2022. “SPAD Monitoring of Saline Vegetation Based on Gaussian Mixture Model and UAV Hyperspectral Image Feature Classification.” Computers and Electronics in Agriculture 200:107236. https://doi.org/10.1016/j.compag.2022.107236.
   Web of Science ®Google Scholar