Onsite age discrimination of an endangered medicinal and aromatic plant species Valeriana jatamansi using field hyperspectral remote sensing and machine learning techniques

References

• Adam, E., and O. Mutanga. 2009. “Spectral Discrimination of Papyrus Vegetation (Cyperus Papyrus L.) In Swamp Wetlands Using Field Spectrometry.” ISPRS Journal of Photogrammetry and Remote Sensing 64 (6): 612–620. doi:10.1016/j.isprsjprs.2009.04.004.
   Web of Science ®Google Scholar
• Akbarzadeh, S., A. Paap, S. Ahderom, B. Apopei, and K. Alameh. 2018. ““Plant Discrimination by Support Vector Machine Classifier Based on Spectral Reflectance”.” Computers and Electronics in Agriculture 148: 250–258. doi:10.1016/j.compag.2018.03.026.
   Web of Science ®Google Scholar
• Audebert, N., B. Le Saux, and S. Lefèvre. 2019. “Deep Learning for Classification of Hyperspectral Data: A Comparative Review.” IEEE Geoscience and Remote Sensing Magazine 7 (2): 159–173. doi:10.1109/MGRS.2019.2912563.
   Web of Science ®Google Scholar
• Bajwa, S. G., P. Bajcsy, P. Groves, and L. F. Tian. 2004. “Hyperspectral Image Data Mining for Band Selection in Agricultural Applications.” Transactions of the ASAE 47 (3): 895. doi:10.13031/2013.16087.
   Google Scholar
• Ballanti, L., L. Blesius, E. Hines, and B. Kruse. 2016. “Tree Species Classification Using Hyperspectral Imagery: A Comparison of Two Classifiers.” Remote Sensing 8 (6): 445. doi:10.3390/rs8060445.
   Web of Science ®Google Scholar
• Berhane, T., C. Lane, Q. Wu, B. Autrey, O. Anenkhonov, V. Chepinoga, and H. Liu. 2018. “Decision-tree, Rule-based, and Random Forest Classification of High-resolution Multispectral Imagery for Wetland Mapping and Inventory.” Remote Sensing 10 (4): 580. doi:10.3390/rs10040580.
   PubMed Web of Science ®Google Scholar
• Bhadra, S., V. Sagan, M. Maimaitijiang, M. Maimaitiyiming, M. Newcomb, N. Shakoor, and T. C. Mockler. 2020. “Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning.” Remote Sensing 12 (13): 2082. doi:10.3390/rs12132082.
   Web of Science ®Google Scholar
• Bhattacharjee, S. K. 2000. Handbook of Aromatic Plants. Jaipur: Pointer Publishers.
   Google Scholar
• Bhojaraja, B. E., G. Hegde, U. Pruthviraj, A. Shetty, and M. K. Nagaraj. 2015. “Mapping Agewise Discrimination of Arecanut Crop Water Requirement Using Hyperspectral Remote Sensing.” Aquatic Procedia 4: 1437–1444. doi:10.1016/j.aqpro.2015.02.186.
   Google Scholar
• Blackburn, G. A. 2007. ““Hyperspectral Remote Sensing of Plant Pigments.”.” Journal of Experimental Botany 58 (4): 855–867. doi:10.1093/jxb/erl123.
   PubMed Web of Science ®Google Scholar
• Bos, R., H. J. Woerdenbag, H. Hendriks, H. F. Smit, H. V. Wikström, and J. J. Scheffer. 1997. “Composition of the Essential Oil from Roots and Rhizomes of Valeriana wallichiiDC.” Flavour and Fragrance Journal 12 (2): 123–131. doi:10.1002/(SICI)1099-1026(199703)12:2<123::AID-FFJ613>3.0.CO;2-4.
   Google Scholar
• Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Burks, T. F., S. A. Shearer, J. R. Heath, and K. D. Donohue. 2005. “Evaluation of Neural-network Classifiers for Weed Species Discrimination.” Biosystems Engineering 91 (3): 293–304. doi:10.1016/j.biosystemseng.2004.12.012.
   Web of Science ®Google Scholar
• Cechim Júnior, C., R. C. Shemmer, J. A. Johann, G. H. de Almeida Pereira, F. Deppe, M. A. U. Opazo, and C. A. D. Silva Junior. 2019. “Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach.” Canadian Journal of Remote Sensing 45 (1): 16–25. doi:10.1080/07038992.2019.1594734.
   Web of Science ®Google Scholar
• Chang-Hua, J. U., T. I. A. N. Yong-Chao, Y. A. O. Xia, C. A. O. Wei-Xing, Z. H. U. Yan, and D. Hannaway. 2010. “Estimating Leaf Chlorophyll Content Using Red Edge Parameters.” Pedosphere 20 (5): 633–644. doi:10.1016/S1002-0160(10)60053-7.
   Web of Science ®Google Scholar
• Chen, Y., K. Ren, X. He, J. Gong, X. Hu, J. Su, C. Zou, Z. Zhao, Y. Zhu, and C. Zou. 2019. “Dynamic Changes in Physiological and Biochemical Properties of Flue-cured Tobacco of Different Leaf Ages during Flue-curing and Their Effects on Yield and Quality.” BMC Plant Biology 19 (1): 555. doi:10.1186/s12870-019-2143-x.
   PubMed Web of Science ®Google Scholar
• Clevers, J. G., and L. Kooistra. 2011. “Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (2): 574–583. doi:10.1109/JSTARS.2011.2176468.
   Web of Science ®Google Scholar
• Curran, P. J. 1989. “Remote Sensing of Foliar Chemistry.” Remote Sensing of Environment 30 (3): 271–278. doi:10.1016/0034-4257(89)90069-2.
   Web of Science ®Google Scholar
• Dalponte, M., Ene, L. T., Marconcini, M., Gobakken, T., and Næsset, E. 2015. “Semi-supervised SVM for individual tree crown species classification.” ISPRS Journal of Photogrammetry and Remote Sensing 110: 77–87
   Web of Science ®Google Scholar
• Dalponte, M., H. O. Ørka, T. Gobakken, D. Gianelle, and E. Næsset. 2012. “Tree Species Classification in Boreal Forests with Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 51 (5): 2632–2645. doi:10.1109/TGRS.2012.2216272.
   Web of Science ®Google Scholar
• Datt, B. 1998. “Remote Sensing of Chlorophyll A, Chlorophyll B, Chlorophyll A+ B, and Total Carotenoid Content in Eucalyptus Leaves.” Remote Sensing of Environment 66 (2): 111–121. doi:10.1016/S0034-4257(98)00046-7.
   Web of Science ®Google Scholar
• Din, M., J. Ming, S. Hussain, S. T. Ata-Ul-Karim, M. Rashid, M. N. Tahir, S. Wang, and S. Wang. 2019. “Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices under Varying N Rates at Diverse Phenological Stages of Rice.” Frontiers in Plant Science 9: 1883. doi:10.3389/fpls.2018.01883.
   PubMed Web of Science ®Google Scholar
• Din, M., W. Zheng, M. Rashid, S. Wang, and Z. Shi. 2017. “Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza Sativa L. At Diverse Phenological Stages.” Frontiers in Plant Science 8: 820. doi:10.3389/fpls.2017.00820.
   PubMed Web of Science ®Google Scholar
• Ehleringer, J., BJORKMAN, O., and Mooney, H. A. 1976. Leaf Pubescence: Effects on Absorptance and Photosynthesis in a Desert Shrub.” Science 192(4237): 376–377 doi:10.1126/science.192.4237.376
   PubMed Web of Science ®Google Scholar
• Ehteram, M., V. P. Singh, A. Ferdowsi, S. F. Mousavi, S. Farzin, H. Karami, and M. A. Malek. 2019. “An Improved Model Based on the Support Vector Machine and Cuckoo Algorithm for Simulating Reference Evapotranspiration.” PloS One 14 (5): e0217499. doi:10.1371/journal.pone.0217499.
   PubMed Web of Science ®Google Scholar
• Feng, H., G. Chen, L. Xiong, Q. Liu, and W. Yang. 2017. “Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline.” Frontiers in Plant Science 8: 1238. doi:10.3389/fpls.2017.01238.
   PubMed Web of Science ®Google Scholar
• Friedl, M. A., and C. E. Brodley. 1997. “Decision Tree Classification of Land Cover from Remotely Sensed Data.” Remote Sensing of Environment 61 (3): 399–409. doi:10.1016/S0034-4257(97)00049-7.
   Web of Science ®Google Scholar
• Friedman, J., T. Hastie, and R. Tibshirani. 2001. The Elements of Statistical Learning. New York: Springer series in statistics.
   Google Scholar
• Fu, P., K. Meacham-Hensold, K. Guan, and C. J. Bernacchi. 2019. “Hyperspectral Leaf Reflectance as Proxy for Photosynthetic Capacities: An Ensemble Approach Based on Multiple Machine Learning Algorithms.” Frontiers in Plant Science 10: 730. doi:10.3389/fpls.2019.00730.
   PubMed Web of Science ®Google Scholar
• García, V., R. A. Mollineda, and J. S. Sánchez. 2009. “Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions.” Paper Presented at Iberian Conference on Pattern Recognition and Image Analysis, Springer, June 441-448.
   Google Scholar
• Gitelson, A. A., G. P. Keydan, and M. N. Merzlyak. 2006. “Three‐band Model for Noninvasive Estimation of Chlorophyll, Carotenoids, and Anthocyanin Contents in Higher Plant Leaves.” Geophysical Research Letters 33 (11): L11402. doi:10.1029/2006GL026457.
   Web of Science ®Google Scholar
• Gitelson, A. A., Y. Gritz, and M. N. Merzlyak. 2003. “Relationships between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-destructive Chlorophyll Assessment in Higher Plant Leaves.” Journal of Plant Physiology 160 (3): 271–282. doi:10.1078/0176-1617-00887.
   PubMed Web of Science ®Google Scholar
• Gnyp, M. L., Y. Yao, K. Yu, S. Huang, H. Aasen, V. I. S. Lenz-Wiedemann, G. Bareth, and G. Bareth. 2012. “Hyperspectral Analysis of Rice Phenological Stages in Northeast China.” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 7: 77–82. doi:10.5194/isprsannals-I-7-77-2012.
   Google Scholar
• Han, E. H. S., G. Karypis, and V. Kumar. 2001. “Text Categorization Using Weight Adjusted K-nearest Neighbor Classification.” Paper Presented at Pacific-asia Conference on Knowledge Discovery and Data Mining, Springer, April 53-65.
   Google Scholar
• Hassanzadeh, A., S. P. Murphy, S. J. Pethybridge, and J. van Aardt. 2020. “Growth Stage Classification and Harvest Scheduling of Snap Bean Using Hyperspectral Sensing: A Greenhouse Study.” Remote Sensing 12 (22): 3809. doi:10.3390/rs12223809.
   Web of Science ®Google Scholar
• Jugran, A. K., I. D. Bhatt, R. S. Rawal, S. K. Nandi, and V. Pande. 2013. “Patterns of Morphological and Genetic Diversity of Valeriana Jatamansi Jones in Different Habitats and Altitudinal Range of West Himalaya, India.” Flora-Morphology, Distribution, Functional Ecology of Plants 208 (1): 13–21. doi:10.1016/j.flora.2012.12.003.
   Google Scholar
• Kho, S. J., S. Manickam, S. Malek, M. Mosleh, and S. K. Dhillon. 2017. “Automated Plant Identification Using Artificial Neural Network and Support Vector Machine.” Frontiers in Life Science 10 (1): 98–107. doi:10.1080/21553769.2017.1412361.
   Web of Science ®Google Scholar
• Kim, T. K. 2015. “T Test as a Parametric Statistic.” Korean Journal of Anesthesiology 68 (6): 540. doi:10.4097/kjae.2015.68.6.540.
   PubMed Web of Science ®Google Scholar
• Kimes, D. S., R. F. Nelson, M. T. Manry, and A. K. Fung. 1998. “Attributes of Neural Networks for Extracting Continuous Vegetation Variables from Optical and Radar Measurements.” International Journal of Remote Sensing 19 (14): 2639–2663. doi:10.1080/014311698214433.
   Web of Science ®Google Scholar
• Kishore, B. S. P. C., A. Kumar, P. Saikia, N. Lele, A. C. Pandey, P. Srivastava, M. L. Khan, and M. L. Khan. 2020. “Major Forests and Plant Species Discrimination in Mudumalai Forest Region Using Airborne Hyperspectral Sensing.” Journal of Asia-Pacific Biodiversity 13 (4): 637–651. doi:10.1016/j.japb.2020.07.001.
   Web of Science ®Google Scholar
• Knox, N. M., A. K. Skidmore, H. M. van der Werff, T. A. Groen, W. F. de Boer, H. H. Prins, M. Peel, and M. Peel. 2013. “Differentiation of Plant Age in Grasses Using Remote Sensing.” International Journal of Applied Earth Observation and Geoinformation 24: 54–62. doi:10.1016/j.jag.2013.02.004.
   Web of Science ®Google Scholar
• Kong, B., B. He, H. Yu, and Y. Liu. 2017. “An Integrated Field and Hyperspectral Remote Sensing Method for the Estimation of Pigments Content of Stipa Purpurea in Shenzha, Tibet.” Mathematical Problems in Engineering 2017 (4): 1–10.
   Google Scholar
• Kuhn, M., J. Wing, S. Weston, A. Williams, C. Keefer, A. Engelhardt, and B. Kenkel 2016. “Caret: Classification and Regression Training. R Package Version 6.0-73.” https://cran.r-project.org/web/packages/caret/index.html
   Google Scholar
• Kuhn, M., S. Weston, N. Coulter, M. Culp, and R. Quinlan. 2015. “C50: C5.0 Decision Trees and Rule Based Models.” R package version 0.8.0. https://cran.r-project.org/web/packages/C50/index.html
   Google Scholar
• Kumar, A., K. R. Manjunath, R. Bala, R. K. Sud, R. D. Singh, S. Panigrahy, and S. Panigrahy. 2013. “Field Hyperspectral Data Analysis for Discriminating Spectral Behavior of Tea Plantations under Various Management Practices.” International Journal of Applied Earth Observation and Geoinformation 23: 352–359. doi:10.1016/j.jag.2012.10.006.
   Web of Science ®Google Scholar
• Kuska, M., Wahabzada, M., Leucker, M., Dehne, H. W., Kersting, K., Oerke, E. C., and Mahlein, A. K. 2015. “Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions.” Plant methods 11(1): 1–15.
   PubMed Web of Science ®Google Scholar
• Kwon, Y. K., M. S. Ahn, J. S. Park, J. R. Liu, D. S. In, B. W. Min, and S. W. Kim. 2014. “Discrimination of Cultivation Ages and Cultivars of Ginseng Leaves Using Fourier Transform Infrared Spectroscopy Combined with Multivariate Analysis.” Journal of Ginseng Research 38 (1): 52–58. doi:10.1016/j.jgr.2013.11.006.
   PubMed Web of Science ®Google Scholar
• Lauwers, M., B. De Cauwer, D. Nuyttens, S. R. Cool, and J. G. Pieters. 2020. “Hyperspectral Classification of Cyperus Esculentus Clones and Morphologically Similar Weeds.” Sensors 20 (9): 2504. doi:10.3390/s20092504.
   PubMed Web of Science ®Google Scholar
• Letchamo, W., W. Ward, B. Heard, and D. Heard. 2004. “Essential Oil of Valeriana Officinalis L. Cultivars and Their Antimicrobial Activity as Influenced by Harvesting Time under Commercial Organic Cultivation.” Journal of Agricultural and Food Chemistry 52 (12): 3915–3919. doi:10.1021/jf0353990.
   PubMed Web of Science ®Google Scholar
• Lichtenthaler, H. K., A. Gitelson, and M. Lang. 1996. “Non-destructive Determination of Chlorophyll Content of Leaves of a Green and an Aurea Mutant of Tobacco by Reflectance Measurements.” Journal of Plant Physiology 148 (3–4): 483–493. doi:10.1016/S0176-1617(96)80283-5.
   Web of Science ®Google Scholar
• Lim, J., Kim, K-M.., and Jin, R. 2019. “Tree species classification using hyperion and sentinel-2 data with machine learning in South Korea and China.” ISPRS International Journal of Geo-Information 8(3): 150 doi:10.3390/ijgi8030150
   Web of Science ®Google Scholar
• Ling, B., D. G. Goodin, E. J. Raynor, and A. Joern. 2019. “Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation.” Frontiers in Plant Science 10: 142. doi:10.3389/fpls.2019.00142.
   PubMed Web of Science ®Google Scholar
• Liu, X., W. Zhang, F. Yang, X. Zhou, Z. Liu, F. Qu, X. Tang, C. Wang, and X. Tang. 2012. “Changes in Vegetation–environment Relationships over Long-term Natural Restoration Process in Middle Taihang Mountain of North China.” Ecological Engineering 49: 193–200. doi:10.1016/j.ecoleng.2012.06.040.
   Web of Science ®Google Scholar
• Mackinney, G. 1941. “Absorption of Light by Chlorophyll Solutions.” The Journal of Biological Chemistry 140 (2): 315–322. doi:10.1016/S0021-9258(18)51320-X.
   Google Scholar
• Maier, P. M., and S. Keller 2018. “Machine Learning Regression on Hyperspectral Data to Estimate Multiple Water Parameters.” Paper presented at the 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), IEEE, September 23–26.
   Google Scholar
• Makrides, S. C., and J. Goldthwaite. 1981. ““Biochemical Changes during Bean Leaf Growth, Maturity, and Senescence: Content of DNA, Polyribosomes, Ribosomal Rna, Protein, and Chlorophyll.”.” Journal of Experimental Botany 32 (4): 725–735. doi:10.1093/jxb/32.4.725.
   Web of Science ®Google Scholar
• Mariotto, I., P. S. Thenkabail, A. Huete, E. T. Slonecker, and A. Platonov. 2013. “Hyperspectral versus Multispectral Crop-productivity Modeling and Type Discrimination for the HyspIRI Mission.” Remote Sensing of Environment 139: 291–305. doi:10.1016/j.rse.2013.08.002.
   Web of Science ®Google Scholar
• Marrs, J., and W. Ni-Meister. 2019. “Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data.” Remote Sensing 11 (7): 819. doi:10.3390/rs11070819.
   Web of Science ®Google Scholar
• Maxwell, A. E., T. A. Warner, and F. Fang. 2018. “Implementation of Machine-learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39 (9): 2784–2817. doi:10.1080/01431161.2018.1433343.
   Web of Science ®Google Scholar
• Nisgoski, S., A. A. de Oliveira, and G. I. B. de Muñiz. 2017. “Artificial Neural Network and SIMCA Classification in Some Wood Discrimination Based on Near-infrared Spectra.” Wood Science and Technology 51 (4): 929–942. doi:10.1007/s00226-017-0915-8.
   Web of Science ®Google Scholar
• O’Neill, J. D., M. Costa, and T. Sharma. 2011. “Remote Sensing of Shallow Coastal Benthic Substrates: In Situ Spectra and Mapping of Eelgrass (Zostera Marina) in the Gulf Islands National Park Reserve of Canada.” Remote Sensing 3 (5): 975–1005. doi:10.3390/rs3050975.
   Web of Science ®Google Scholar
• Pal, M., and G. M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data by SVM.” IEEE Transactions on Geoscience and Remote Sensing 48 (5): 2297–2307. doi:10.1109/TGRS.2009.2039484.
   Web of Science ®Google Scholar
• Paul, A., and Chaki, N. 2019. “Dimensionality reduction of hyperspectral images using pooling.” Pattern Recognition and Image Analysis, 29(1): 72–78 doi:10.1134/S1054661819010085
   Web of Science ®Google Scholar
• Peñuelas, J., and I. Filella. 1998. “Visible and Near-infrared Reflectance Techniques for Diagnosing Plant Physiological Status.” Trends in Plant Science 3 (4): 151–156. doi:10.1016/S1360-1385(98)01213-8.
   Web of Science ®Google Scholar
• Phillips, M. J., and Voeltz, G. K. 2016. “Structure and function of ER membrane contact sites with other organelles.” Nature reviews Molecular cell biology17(2): 69–82 doi:10.1038/nrm.2015.8
   PubMed Web of Science ®Google Scholar
• Prakash, V. 1999. “Indian Valerianaceae.” In A Monograph on Medicinally Important Family. Jodhpur: India, 1-74 Scientific Publishers.
   Google Scholar
• Prospere, K., K. McLaren, and B. Wilson. 2014. ““Plant Species Discrimination in a Tropical Wetland Using in Situ Hyperspectral Data.”.” Remote Sensing 6 (9): 8494–8523. doi:10.3390/rs6098494.
   Web of Science ®Google Scholar
• Quinlan, J. R. 1986. “Induction of Decision Trees.” Machine Learning 1 (1): 81–106. doi:10.1007/BF00116251.
   Google Scholar
• Raczko, E., and B. Zagajewski. 2017. “Comparison of Support Vector Machine, Random Forest and Neural Network Classifiers for Tree Species Classification on Airborne Hyperspectral APEX Images.” European Journal of Remote Sensing 50 (1): 144–154. doi:10.1080/22797254.2017.1299557.
   Web of Science ®Google Scholar
• Ripley, B., and W. Venables. 2016. “Nnet: Feed-Forward Neural Networks and Multinomial LogLinear Models.” R package version 7.3-12. https://cran.r-project.org/web/packages/nnet/index.html
   Google Scholar
• Safavian, S. R., and D. Landgrebe. 1991. “A Survey of Decision Tree Classifier Methodology.” IEEE Transactions on Systems, Man, and Cybernetics 21 (3): 660–674. doi:10.1109/21.97458.
   Web of Science ®Google Scholar
• Saini, S., A. Dubey, A. Kumar, G. Taj, S. Budhori, and A. V. Kumar. 2018. “In Vitro Direct Rhizogenesis of an Endangered Medicinal Herb: Valeriana Jatamansi Jones.” International Journal of Current Microbiology and Applied Sciences 7 (4): 3108–3114. doi:10.20546/ijcmas.2018.704.352.
   Google Scholar
• Schmidt, K. S., and A. K. Skidmore. 2003. “Spectral Discrimination of Vegetation Types in a Coastal Wetland.” Remote Sensing of Environment 85 (1): 92–108. doi:10.1016/S0034-4257(02)00196-7.
   Web of Science ®Google Scholar
• Schmidt, K. S., and Skidmore, A. K. 2001. “Exploring spectral discrimination of grass species in African rangelands.” International Journal of Remote Sensing 22(17): 3421–3434 doi:10.1080/01431160152609245
   Web of Science ®Google Scholar
• Sheela, K. G., and S. N. Deepa. 2013. “Review on Methods to Fix Number of Hidden Neurons in Neural Networks.” Mathematical Problems in Engineering 2013: 425740. doi:10.1155/2013/425740.
   Web of Science ®Google Scholar
• Shen, X., and L. Cao. 2017. “Tree-species Classification in Subtropical Forests Using Airborne Hyperspectral and LiDAR Data.” Remote Sensing 9 (11): 1180. doi:10.3390/rs9111180.
   Web of Science ®Google Scholar
• Sims, D. A., and Gamon, J. A. 2002. “Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages.” Remote sensing of environment 81(2–3): 337–354 doi:10.1016/S0034-4257(02)00010-X
   Web of Science ®Google Scholar
• Singh, A., and C. K. Atal. 1982. Cultivation and Utilization of Medicinal and Aromatic Plants. Jammu-Tawi: Regional Research Laboratory.
   Google Scholar
• Sinha, P., A. Robson, D. Schneider, T. Kilic, H. K. Mugera, J. Ilukor, and J. M. Tindamanyire. 2020. “The Potential of In-situ Hyperspectral Remote Sensing for Differentiating 12 Banana Genotypes Grown in Uganda.” ISPRS Journal of Photogrammetry and Remote Sensing 167: 85–103. doi:10.1016/j.isprsjprs.2020.06.023.
   Web of Science ®Google Scholar
• Slaton, M. R., E. Raymond Hunt Jr, and W. K. Smith. 2001. “Estimating Near‐infrared Leaf Reflectance from Leaf Structural Characteristics.” American Journal of Botany 88 (2): 278–284. doi:10.2307/2657019.
   PubMed Web of Science ®Google Scholar
• Sommer, C., Holzwarth, S., Heiden, U., Heurich, M., Müller, J., and Mauser, W. 2015. “Feature based tree species classification using hyperspectral and lidar data in the Bavarian Forest National Park.” Paper presented at The 9th EARSEL Imaging Spectroscopy Workshop, Luxembourg, April 49–70
   Google Scholar
• Sonobe, R., H. Yamashita, H. Mihara, A. Morita, and T. Ikka. 2021. “Hyperspectral Reflectance Sensing for Quantifying Leaf Chlorophyll Content in Wasabi Leaves Using Spectral Pre-processing Techniques and Machine Learning Algorithms.” International Journal of Remote Sensing 42 (4): 1311–1329. doi:10.1080/01431161.2020.1826065.
   Web of Science ®Google Scholar
• Tan, C., Y. Du, J. Zhou, D. Wang, M. Luo, Y. Zhang, and W. Guo. 2018. “Analysis of Different Hyperspectral Variables for Diagnosing Leaf Nitrogen Accumulation in Wheat.” Frontiers in Plant Science 9: 674. doi:10.3389/fpls.2018.00674.
   PubMed Web of Science ®Google Scholar
• Thenkabail, P. S., E. A. Enclona, M. S. Ashton, and B. Van Der Meer. 2004. “Accuracy Assessments of Hyperspectral Waveband Performance for Vegetation Analysis Applications.” Remote Sensing of Environment 91 (3–4): 354–376. doi:10.1016/j.rse.2004.03.013.
   Web of Science ®Google Scholar
• Thenkabail, P. S., Mariotto, I., Gumma, M. K., Middleton, E. M., Landis, D. R., and Huemmrich, K. F. 2013. “Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral two band vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6(2): 427–439 doi:10.1109/JSTARS.2013.2252601
   Web of Science ®Google Scholar
• Therneau, T., B. Atkinson, and B. Ripley. 2017. “Rpart: Recursive Partitioning and Regression Trees.” R package version 4.1-11. https://cran.r-project.org/web/packages/rpart/index.html
   Google Scholar
• Tilly, N., H. Aasen, and G. Bareth. 2015. “Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass.” Remote Sensing 7 (9): 11449–11480. doi:10.3390/rs70911449.
   Web of Science ®Google Scholar
• Upadhyay, V., Kandpal, K. C., Jaiswal, S., Kumar, S., and Kumar, A. 2020. “Revisit and optimisation of spectral data collection techniques from vegetation using handheld non-imaging spectroscopic sensor for minimising errors.” Vibrational Spectroscopy 111: 103159. doi:10.1016/j.vibspec.2020.103159
   Google Scholar
• Ustin, S. L., and S. Jacquemoud. 2020. ““How the Optical Properties of Leaves Modify the Absorption and Scattering of Energy and Enhance Leaf Functionality.”.” In In Remote Sensing of Plant Biodiversity, edited by Cavender-Bares J., Gamon J.A., Townsend P.A., 349–384. Cham: Springer. doi:10.1007/978-3-030-33157-3_14
   Google Scholar
• Vaiphasa, C., S. Ongsomwang, T. Vaiphasa, and A. K. Skidmore. 2005. “Tropical Mangrove Species Discrimination Using Hyperspectral Data: A Laboratory Study.” Estuarine, Coastal and Shelf Science 65 (1–2): 371–379. doi:10.1016/j.ecss.2005.06.014.
   Web of Science ®Google Scholar
• Vihinen, M. 2012. “How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis.” BMC genomics 13(4): 1–10. doi:10.1186/1471-2164-13-S4-S2
   PubMed Web of Science ®Google Scholar
• Wright, M. N., and A. Ziegler 2015. “Ranger: A Fast Implementation of Random Forests for High 650Q22 Dimensional Data in C++ and R.” https://cran.r-project.org/web/packages/ranger/index.html“ to „Wright, M. N. 2017. “Ranger: A Fast Implementation of Random Forests.” R package version 0.8.0. https://cran.r-project.org/web/packages/ranger/index.html
   Google Scholar
• Yang, G., Y. Zhao, B. Li, Y. Ma, R. Li, J. Jing, and Y. Dian. 2019. “Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors.” Journal of Sensors 2019: 1–12.
   Web of Science ®Google Scholar
• Yendrek, C. R., T. Tomaz, C. M. Montes, Y. Cao, A. M. Morse, P. J. Brown, E. A. Ainsworth, A. D. B. Leakey, and E. A. Ainsworth. 2017. “High-throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.” Plant Physiology 173 (1): 614–626. doi:10.1104/pp.16.01447.
   PubMed Web of Science ®Google Scholar
• Zarco-Tejada, P. J., A. Hornero, R. Hernández-Clemente, and P. S. A. Beck. 2018. “Understanding the Temporal Dimension of the Red-edge Spectral Region for Forest Decline Detection Using High-resolution Hyperspectral and Sentinel-2a Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 137: 134–148. doi:10.1016/j.isprsjprs.2018.01.017.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., Y. Yuan, and Q. Wang. 2019. “Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification.” Remote Sensing 11 (4): 399. doi:10.3390/rs11040399.
   Web of Science ®Google Scholar