Abstract
Plant nitrogen (N) is one of the key factors for its growth and yield. Timely assessment of plant N at a spatio-temporal scale enables its precision management in the field scale with better N use efficiency. Airborne imaging spectroscopy is a potential technique for non-invasive near real-time rapid assessment of plant N on a field scale. The present study attempted to assess plant N in a wheat field with three different irrigation levels (I1–I3) along with five nitrogen treatments (N0–N4) using a UAV hyperspectral imager with a spectral range of 400 to 1000 nm. A total of 61 vegetative indices were evaluated to find suitable indices for estimating plant N. A hybrid method of R-Square (R2) and Variable Importance Projection (VIP) followed by Variance Inflation Factor was used to limit the best suitable N-sensitive 13 spectral indices. The selected indices were used as feature vectors in the Artificial Neural Network algorithm to model and generate a spatial map of plant N in the experimental wheat field. The model resulted in R2 values of 0.97, 0.84, and 0.86 for training, validation, and testing respectively for plant N assessment.
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Adak, S., Bandyopadhyay, K. K., Sahoo, R. N., Mridha, N., Shrivastava, M., & Purakayastha, T. J. (2021). Prediction of wheat yield using spectral reflectance indices under different tillage, residue and nitrogen management practices. Current Science. https://doi.org/10.18520/cs/v121/i3/402-413
Ågren, G. I., Wetterstedt, J. Å. M., & Billberger, M. F. K. (2012). Nutrient limitation on terrestrial plant growth - modeling the interaction between nitrogen and phosphorus. New Phytologist. https://doi.org/10.1111/j.1469-8137.2012.04116.x
Anderegg, J., Tschurr, F., Kirchgessner, N., Treier, S., Schmucki, M., Streit, B., & Walter, A. (2023). On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.107558
Baresel, J. P., Zimmermann, G., & Reents, H. J. (2008). Effects of genotype and environment on N uptake and N partition in organically grown winter wheat (Triticum aestivum L.) in Germany. Euphytica. https://doi.org/10.1007/s10681-008-9718-1
Barnes, J. D., Balaguer, L., Manrique, E., Elvira, S., & Davison, A. W. (1992). A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environmental and Experimental Botany. https://doi.org/10.1016/0098-8472(92)90034-Y
Basyouni, R., Dunn, B. L., & Goad, C. (2015). Use of nondestructive sensors to assess nitrogen status in potted poinsettia (Euphorbia pulcherrima L. (Willd. ex Klotzsch)) production. Scientia Horticulturae. https://doi.org/10.1016/j.scienta.2015.05.011
Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing. https://doi.org/10.1080/014311698215919
Blekanov, I., Molin, A., Zhang, D., Mitrofanov, E., Mitrofanova, O., & Li, Y. (2023). Monitoring of grain crops nitrogen status from uav multispectral images coupled with deep learning approaches. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2023.108047
Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment. https://doi.org/10.1016/S0034-4257(00)00197-8
Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing. https://doi.org/10.1080/01431169308904370
Buthelezi, S., Mutanga, O., Sibanda, M., Odindi, J., Clulow, A. D., Chimonyo, V. G. P., & Mabhaudhi, T. (2023). Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sensing. https://doi.org/10.3390/rs15061597
Carter, G. A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing. https://doi.org/10.1080/01431169408954109
Chandel, N. S., Tiwari, P. S., Singh, K. P., Jat, D., Gaikwad, B. B., Tripathi, H., & Golhani, K. (2019). Yield prediction in wheat (Triticum aestivum L.) using spectral reflectance indices. Current Science. https://doi.org/10.18520/cs/v116/i2/272-278
Chen, J. M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing. https://doi.org/10.1080/07038992.1996.10855178
Dash, J., & Curran, P. J. (2004). The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing. https://doi.org/10.1080/0143116042000274015
Datt, B. (1999). Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves. International Journal of Remote Sensing. https://doi.org/10.1080/014311699211778
Datt, B. (1999). A new reflectance index for remote sensing of chlorophyll content in higher plants: Tests using Eucalyptus leaves. Journal of Plant Physiology. https://doi.org/10.1016/S0176-1617(99)80314-9
Dehghan-Shoar, M. H., Orsi, A. A., Pullanagari, R. R., & Yule, I. J. (2023). A hybrid model to predict nitrogen concentration in heterogeneous grassland using field spectroscopy. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.113385
Esposito, M., Crimaldi, M., Cirillo, V., Sarghini, F., & Maggio, A. (2021). Drone and sensor technology for sustainable weed management: a review. Chemical and Biological Technologies in Agriculture. https://doi.org/10.1186/s40538-021-00217-8.
Fageria, N. K., Baligar, V. C., & Jones, C. A. (2010). Growth and mineral nutrition of field crops, third edition. Growth and Mineral Nutrition of Field Crops (3rd ed.). CRC Press. https://doi.org/10.1201/b10160
Fredeen, A. L., Raab, T. K., Rao, I. M., & Terry, N. (1990). Effects of phosphorus nutrition on photosynthesis in Glycine max (L.) Merr. Planta. https://doi.org/10.1007/BF00195894
Fu, Z., Yu, S., Zhang, J., Xi, H., Gao, Y., Lu, R., et al., (2022). Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat. European Journal of Agronomy. https://doi.org/10.1016/j.eja.2021.126405
Garbulsky, M. F., Peñuelas, J., Gamon, J., Inoue, Y., & Filella, I. (2011). The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies. A review and meta-analysis. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2010.08.023.
Ge, X., Wang, J., Ding, J., Cao, X., Zhang, Z., Liu, J., & Li, X. (2019). Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ. https://doi.org/10.7717/peerj.6926
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment. https://doi.org/10.1016/S0034-4257(96)00072-7
Gitelson, A. A., & Merzlyak, M. N. (1998). Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research. https://doi.org/10.1016/S0273-1177(97)01133-2
Gitelson, A., & Merzlyak, M. N. (1994). Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology. https://doi.org/10.1016/S0176-1617(11)81633-0
Good, A. G., & Beatty, P. H. (2011). Fertilizing nature: A tragedy of excess in the commons. PLoS Biology. https://doi.org/10.1371/journal.pbio.1001124
Guebel, D. V., Nudel, B. C., & Giulietti, A. M. (1991). A simple and rapid micro-Kjeldahl method for total nitrogen analysis. Biotechnology Techniques. https://doi.org/10.1007/BF00155487
Guo, J., Jia, Y., Chen, H., Zhang, L., Yang, J., Zhang, J., et al., (2019). Growth, photosynthesis, and nutrient uptake in wheat are affected by differences in nitrogen levels and forms and potassium supply. Scientific Reports. https://doi.org/10.1038/s41598-018-37838-3
Gupta, R. K., Vijayan, D., & Prasad, T. S. (2001). New hyperspectral vegetation characterization parameters. Advances in Space Research. https://doi.org/10.1016/S0273-1177(01)00346-5
Gupta, R. K., Vijayan, D., & Prasad, T. S. (2003). Comparative analysis of red-edge hyperspectral indices. Advances in Space Research. https://doi.org/10.1016/S0273-1177(03)90545-X
Guyot, G., & Baret, F. (1988). Utilisation de la haute resolution spectrale pour suivre l’etat des couverts vegetaux. Journal of Chemical Information and Modeling, 53(9), 279.
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2003.12.013
Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment. https://doi.org/10.1016/S0034-4257(03)00131-7
Haque, T. (2006). Resource use efficiency in Indian agriculture. Indian Journal of Agricultural Economics, 61, 66.
Hirel, B., Tétu, T., Lea, P. J., & Dubois, F. (2011). Improving nitrogen use efficiency in crops for sustainable agriculture. Sustainability. https://doi.org/10.3390/su3091452
Hunt, E. R., Cavigelli, M., Daughtry, C. S. T., McMurtrey, J. E., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture. https://doi.org/10.1007/s11119-005-2324-5
Jamali, M., Soufizadeh, S., Yeganeh, B., & Emam, Y. (2023). Wheat leaf traits monitoring based on machine learning algorithms and high-resolution satellite imagery. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2022.101967
Jiang, J., Cai, W., Zheng, H., Cheng, T., Tian, Y., Zhu, Y., et al., (2019). Using digital cameras on an unmanned aerial vehicle to derive optimum color vegetation indices for leaf nitrogen concentration monitoring in winter wheat. Remote Sensing. https://doi.org/10.3390/rs11222667
Jiang, J., Atkinson, P. M., Chen, C., Cao, Q., Tian, Y., Zhu, Y., et al. (2023). Combining UAV and Sentinel-2 satellite multi-spectral images to diagnose crop growth and N status in winter wheat at the county scale. Field Crops Research. https://doi.org/10.1016/j.fcr.2023.108860
Jiang, J., Wang, C., Wang, Y., Cao, Q., Tian, Y., Zhu, Y., et al. (2020). Using an active sensor to develop new critical nitrogen dilution curve for winter wheat. Sensors (Switzerland). https://doi.org/10.3390/s20061577
Jurado-Expósito, M., Torres-Sánchez, J., López-Granados, F., & Jiménez-Brenes, F. M. (2021). Monitoring the spatial variability of knapweed (Centaurea diluta aiton) in wheat crops using geostatistics and uav imagery: Probability maps for risk assessment in site-specific control. Agronomy. https://doi.org/10.3390/agronomy11050880
Kauth, R. J., & Thomas, G. S. (1976). The tasselled cap—a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In LARS symposia, p. 159.
Li, S., Ding, X., Kuang, Q., Ata-UI-Karim, S. T., Cheng, T., & Liu, X. (2018). Potential of UAV-based active sensing for monitoring rice leaf nitrogen status. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2018.01834
Liang, L., Di, L., Huang, T., Wang, J., Lin, L., Wang, L., & Yang, M. (2018). Estimation of leaf nitrogen content in wheat using new hyperspectral indices and a random forest regression algorithm. Remote Sensing. https://doi.org/10.3390/rs10121940
Lichtenthaler, H. K., Gitelson, A., & Lang, M. (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. https://doi.org/10.1016/S0176-1617(96)80283-5
Liu, H., Zhu, H., & Wang, P. (2017). Quantitative modelling for leaf nitrogen content of winter wheat using UAV-based hyperspectral data. International Journal of Remote Sensing, 38, 8–10. https://doi.org/10.1080/01431161.2016.1253899.
Lu, N., Wang, W., Zhang, Q., Li, D., Yao, X., Tian, Y., et al. (2019). Estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi-angular multispectral imagery. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2019.01601
Mălinaş, A., Vidican, R., Rotar, I., Mălinaş, C., Moldovan, C. M., & Proorocu, M. (2022). Current status and future prospective for nitrogen use efficiency in wheat (Triticum aestivum L). Plants. https://doi.org/10.3390/plants11020217
Maresma, Á., Ariza, M., Martínez, E., Lloveras, J., & Martínez-Casasnovas, J. A. (2016). Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays l.) from a standard uav service. Remote Sensing, https://doi.org/10.3390/rs8120973
Marshak, A., Knyazikhin, Y., Davis, A. B., Wiscombe, W. J., & Pilewskie, P. (2000). Cloud - vegetation interaction: Use of normalized difference cloud index for estimation of cloud optical thickness. Geophysical Research Letters. https://doi.org/10.1029/1999GL010993
Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B., & Rakitin, V. Y. (1999). Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum. https://doi.org/10.1034/j.1399-3054.1999.106119.x
Osco, L. P., Junior, J. M., Ramos, A. P. M., Furuya, D. E. G., Santana, D. C., Teodoro, L. P. R., et al. (2020). Leaf nitrogen concentration and plant height prediction for maize using UAV-based multispectral imagery and machine learning techniques. Remote Sensing. https://doi.org/10.3390/rs12193237
Penuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221.
Penuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing. https://doi.org/10.1080/01431169308954010
Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(94)90136-8
Pradhan, S., Bandyopadhyay, K. K., Sahoo, R. N., Sehgal, V. K., Singh, R., Joshi, D. K., & Gupta, V. K. (2013). Prediction of wheat (Triticum aestivum) grain and biomass yield under different irrigation and nitrogen management practices using canopy reflectance spectra model. Indian Journal of Agricultural Sciences, 83(11), 1136.
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(94)90134-1
Rabatel, G., Al Makdessi, N., Ecarnot, M., & Roumet, P. (2017). A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat. Advances in Animal Biosciences. https://doi.org/10.1017/s2040470017000164
Raj, R. (2021). Drone-based sensing for identification of at-risk water and nitrogen stress areas for on-farm management. PhD Dissertation, IITB-Monash Research Academy.
Ranjan, R., Chopra, U. K., Sahoo, R. N., Singh, A. K., & Pradhan, S. (2012). Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2012.687473
Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., & Schepers, J. S. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal. https://doi.org/10.2134/agronj2001.931131x
Rejith, R. G., Sundararajan, M., Gnanappazham, L., & Loveson, V. J. (2020). Satellite-based spectral mapping (ASTER and landsat data) of mineralogical signatures of beach sediments: a precursor insight. Geocarto International. https://doi.org/10.1080/10106049.2020.1750061
Rodriguez, D., Fitzgerald, G. J., Belford, R., & Christensen, L. K. (2006). Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Australian Journal of Agricultural Research. https://doi.org/10.1071/AR05361
Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(95)00186-7
Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment. https://doi.org/10.1016/0034-4257(94)00114-3
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication. NASA special publication, 24(1), 309.
Rouse, J. W., Hass, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Final Report, RSC 1978-4, Texas A & M University, College Station.
Sahoo, R. N., Ray, S. S., & Manjunath, K. R. (2015). Hyperspectral remote sensing of agriculture. Current Science, 108(5), 848.
Sahoo, R. N., Gakhar, S., Rejith, R. G., Ranjan, R., Meena, M. C., Dey, A., et al. (2023). Unmanned Aerial Vehicle (UAV)–Based Imaging Spectroscopy for Predicting Wheat Leaf Nitrogen. Photogrammetric Engineering & Remote Sensing. https://doi.org/10.14358/pers.22-00089r2
Santos-Rufo, A., Mesas-Carrascosa, F. J., García-Ferrer, A., & Meroño-Larriva, J. E. (2020). Wavelength selection method based on partial least square from hyperspectral unmanned aerial vehicle orthomosaic of irrigated olive orchards. Remote Sensing. https://doi.org/10.3390/rs12203426
Sims, D. A., & 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. https://doi.org/10.1016/S0034-4257(02)00010-X
Späti, K., Huber, R., & Finger, R. (2021). Benefits of increasing information accuracy in variable rate technologies. Ecological Economics. https://doi.org/10.1016/j.ecolecon.2021.107047
Stone, M. L., Solie, J. B., Raun, W. R., Whitney, R. W., Taylor, S. L., & Ringer, J. D. (1996). Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Transactions of the American Society of Agricultural Engineers. https://doi.org/10.13031/2013.27678
Tian, Y. C., Yao, X., Yang, J., Cao, W. X., Hannaway, D. B., & Zhu, Y. (2011). Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research. https://doi.org/10.1016/j.fcr.2010.11.002
UgCS (2017). https://www.ugcs.com/.
Vogelmann, J. E., Rock, B. N., & Moss, D. M. (1993). Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing. https://doi.org/10.1080/01431169308953986
Walburg, G., Bauer, M. E., & Daughtry, C. S. T. (1981). Effects of nitrogen nutrition on the growth, yield and reflectance characteristics of corn canopies. Purdue University, LARS Technical Report, 030381. https://doi.org/10.2134/agronj1982.00021962007400040020x.
Wang, L., Chen, S., Li, D., Wang, C., Jiang, H., Zheng, Q., & Peng, Z. (2021). Estimation of paddy rice nitrogen content and accumulation both at leaf and plant levels from uav hyperspectral imagery. Remote Sensing. https://doi.org/10.3390/rs13152956
Wang, X., Wang, Y., Zhou, C., Yin, L., & Feng, X. (2021). Urban forest monitoring based on multiple features at the single tree scale by UAV. Urban Forestry and Urban Greening. https://doi.org/10.1016/j.ufug.2020.126958
Xu, S., Xu, X., Blacker, C., Gaulton, R., Zhu, Q., Yang, M., et al. (2023). Estimation of leaf nitrogen content in rice using vegetation indices and feature variable optimization with information fusion of multiple-sensor images from UAV. Remote Sensing. https://doi.org/10.3390/rs15030854
Xu, S., Xu, X., Zhu, Q., Meng, Y., Yang, G., Feng, H., et al. (2023). Monitoring leaf nitrogen content in rice based on information fusion of multi-sensor imagery from UAV. Precision Agriculture. https://doi.org/10.1007/s11119-023-10042-8.
Xue, L., Cao, W., Luo, W., Dai, T., & Zhu, Y. (2004). Monitoring leaf nitrogen status in rice with canopy spectral reflectance. Agronomy Journal. https://doi.org/10.2134/agronj2004.0135
Yang, B., Wang, M., Sha, Z., Wang, B., Chen, J., Yao, X., et al. (2019). Evaluation of aboveground nitrogen content of winter wheat using digital imagery of unmanned aerial vehicles. Sensors (Switzerland). https://doi.org/10.3390/s19204416
Zarco-Tejada, P. J., Berjón, A., López-Lozano, R., Miller, J. R., Martín, P., Cachorro, V., et al. (2005). Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2005.09.002
Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H., & Sampson, P. H. (2001). Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1491–1507. https://doi.org/10.1109/36.934080.
Zhang, J., Cheng, T., Shi, L., Wang, W., Niu, Z., Guo, W., & Ma, X. (2022). Combining spectral and texture features of UAV hyperspectral images for leaf nitrogen content monitoring in winter wheat. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2021.2019847
Zhao, D., Reddy, K. R., Kakani, V. G., & Reddy, V. R. (2005). Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. European Journal of Agronomy. https://doi.org/10.1016/j.eja.2004.06.005
Zheng, H., Li, W., Jiang, J., Liu, Y., Cheng, T., Tian, Y., et al. (2018). A comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sensing. https://doi.org/10.3390/rs10122026
Zhong, Y., Hu, X., Luo, C., Wang, X., Zhao, J., & Zhang, L. (2020). WHU-Hi: UAV-borne hyperspdectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2020.112012
Zhu, Y., Tian, Y., Yao, X., Liu, X., & Cao, W. (2007). Analysis of common canopy reflectance spectra for indicating leaf nitrogen concentrations in wheat and rice. Plant Production Science. https://doi.org/10.1626/pps.10.400
Zhu, Y., Yao, X., Tian, Y. C., Liu, X. J., & Cao, W. X. (2008). Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2007.02.006
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This research activity was conducted under financial support of the Indian Council of Agricultural Research (ICAR) and is hereby duly acknowledged under the project, titled ‘Network Program on Precision Agriculture (NePPA)’.
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Sahoo, R.N., Rejith, R.G., Gakhar, S. et al. Drone remote sensing of wheat N using hyperspectral sensor and machine learning. Precision Agric 25, 704–728 (2024). https://doi.org/10.1007/s11119-023-10089-7
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DOI: https://doi.org/10.1007/s11119-023-10089-7