Abstract
High-resolution information is needed for precision agriculture to achieve precise management of inputs. High spatial and temporal resolution is requisite to get the actionable information for the timely response. The objective of the present study is to estimate the leaf chlorophyll Concentration using high-resolution (2 cm) images captured from UAV-mounted multispectral sensors for crop health monitoring. In this study, a hexacopter was flown at an elevation of 25 m to capture the images in green, red, red edge and NIR bands of turmeric plots grown at ICAR Research Complex, Northeast Hilly Region, India. A handheld SVC spectroradiometer having spectral range from 350 to 2500 nm was also used to collect the spectra of sample plants to support the UAV study. We evaluated an advanced machine learning algorithm kernel ridge regression combined with spectral information and ground-truth chlorophyll data to model the chlorophyll estimation. The multivariate analysis was also applied on spectroradiometer and UAV data, which recommended red band for chlorophyll prediction with R2 value greater than 0.6. We also found that kernel ridge regression is a robust method for developing chlorophyll estimation model with lesser training time. The results indicate that kernel ridge regression with a radial basis kernel function with four multispectral input bands can be utilized to evaluate the leaf chlorophyll concentration with an root mean squared error RMSE = 0.10 mg/g and regression coefficient R2 = 0.7452. However, this study is site specific and needs to be practiced in different crop sites in order to generalize this method for precision agriculture.
Similar content being viewed by others
References
Aggarwal, S. (2004). Principles of remote sensing. In Satellite remote sensing and GIS applications in agricultural meteorology (pp. 23–38).
Agüera, F., Carvajal, F., & Pérez, M. (2011). Measuring sunflower nitrogen status from an unmanned aerial vehicle-based system and an on the ground device. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 33–37.
Aguilar, M., Saldaña, M., & Aguilar, F. (2013). GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. International Journal of Remote Sensing, 34(7), 2583–2606.
Arnon, D. I. (1949). Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiology, 24(1), 1.
Bacour, C., Baret, F., Béal, D., Weiss, M., & Pavageau, K. (2006). Neural network estimation of LAI, fAPAR, fCover and LAI × Cab, from top of canopy MERIS reflectance data: Principles and validation. Remote Sensing of Environment, 105(4), 313–325.
Baluja, J., Diago, M. P., Balda, P., Zorer, R., Meggio, F., Morales, F., et al. (2012). Assessment of vineyard water status variability by thermal and multispectral imagery using an unmanned aerial vehicle (UAV). Irrigation Science, 30(6), 511–522.
Bansod, B., Singh, R., Thakur, R., & Singhal, G. (2017). A comparison between satellite based and drone based remote sensing technology to achieve sustainable development: A review. Journal of Agriculture and Environment for International Development (JAEID), 111(2), 383–407.
Berni, J. A., Zarco-Tejada, P. J., Suárez, L., & Fereres, E. (2009). Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 722–738.
Caicedo, J. P. R., Verrelst, J., Muñoz-Marí, J., Moreno, J., & Camps-Valls, G. (2014). Toward a semiautomatic machine learning retrieval of biophysical parameters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4), 1249–1259.
Camps-Valls, G., & Bruzzone, L. (Eds.). (2009). Kernel methods for remote sensing data analysis. John Wiley & Sons.
Camps-Valls, G., Munoz-Mari, J., Gomez-Chova, L., Guanter, L., & Calbet, X. (2012). Nonlinear statistical retrieval of atmospheric profiles from MetOp-IASI and MTG-IRS infrared sounding data. IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1759–1769.
Camps-Valls, G., Gómez-Chova, L., Muñoz-Marí, J., Lázaro-Gredilla, M., & Verrelst. J. (2013). simpleR: A simple educational MATLAB toolbox for statistical regression, v 2.1 [Online]. Available: http://www.uv.es/gcamps/code/simpleR.html.
Dutta, D., Das, P. K., Bhunia, U. K., Singh, U., Singh, S., Sharma, J. R., et al. (2015). Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach. International Journal of Applied Earth Observation and Geoinformation, 36, 22–29.
Eisenbeis, R. A., & Avery, R. B. (1972). Discriminant analysis and classification procedures: Theory and applications. Lexington: DC Heath Lexington.
Elarab, M., Ticlavilca, A. M., Torres-Rua, A. F., Maslova, I., & McKee, M. (2015). Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. International Journal of Applied Earth Observation and Geoinformation, 43, 32–42.
Gholizadeh, H., Robeson, S. M., & Rahman, A. F. (2015). Comparing the performance of multispectral vegetation indices and machine-learning algorithms for remote estimation of chlorophyll content: A case study in the Sundarbans mangrove forest. International Journal of Remote Sensing, 36(12), 3114–3133.
Gopal, S., & Woodcock, C. (1996). Remote sensing of forest change using artificial neural networks. IEEE Transactions on Geoscience and Remote Sensing, 34(2), 398–404.
Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., & Dextraze, L. (2002). Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81(2–3), 416–426.
Hiscox, J., & Israelstam, G. (1979). A method for the extraction of chlorophyll from leaf tissue without maceration. Canadian Journal of Botany, 57(12), 1332–1334.
Intermountain Research Station (Ogden Utah), Forestry Sciences Laboratory (Missoula Mont.). Fire Behavior Research Work Unit., Rocky Mountain Research Station (Fort Collins Colo.), & Intermountain Fire Sciences Laboratory (Missoula Mont.) (1997). NDVI and derived products. 1989-1996: General technical report INT GTR, (pp. CD-ROMs). Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station.
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P. J., Asner, G. P., et al. (2009). PROSPECT + SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, 113, S56–S66.
Jha, A., & Deka, B. C. (2012). Present status and prospects of ginger and turmeric in NE States.
Johnson, L. F., Hlavka, C. A., & Peterson, D. L. (1994). Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon transect. Remote Sensing of Environment, 47(2), 216–230.
Kimes, D., Nelson, R., Manry, M., & Fung, A. (1998). Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. International Journal of Remote Sensing, 19(14), 2639–2663.
Lázaro-Gredilla, M., Titsias, M. K., Verrelst, J., & Camps-Valls, G. (2014). Retrieval of biophysical parameters with heteroscedastic Gaussian processes. IEEE Geoscience and Remote Sensing Letters, 11(4), 838–842.
Lichtenthaler, H., Lang, M., Sowinska, M., Heisel, F., & Miehe, J. (1996). Detection of vegetation stress via a new high resolution fluorescence imaging system. Journal of Plant Physiology, 148(5), 599–612.
Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., et al. (2017). Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 43–58.
Meisinger, J. J., Schepers, J. S., & Raun, W. R. (2008). Crop nitrogen requirement and fertilization. Nitrogen in agricultural systems, 49, 563–612.
Miura, T., & Huete, A. R. (2009). Performance of three reflectance calibration methods for airborne hyperspectral spectrometer data. Sensors (Basel), 9(2), 794–813.
Neale, C. M., & Crowther, B. G. (1994). An airborne multispectral video/radiometer remote sensing system: Development and calibration. Remote Sensing of Environment, 49(3), 187–194.
Pen Uelas, J., Filella, I., Lloret, P., Mun Oz, F., & Vilajeliu, M. (1995). Reflectance assessment of mite effects on apple trees. International Journal of Remote Sensing, 16(14), 2727–2733.
Rao, B., Gopi, A. G., & Maione, R. (2016). The societal impact of commercial drones. Technology in Society, 45, 83–90.
Rivera-Caicedo, J. P., Verrelst, J., Muñoz-Marí, J., Camps-Valls, G., & Moreno, J. (2017). Hyperspectral dimensionality reduction for biophysical variable statistical retrieval. ISPRS Journal of Photogrammetry and Remote Sensing, 132, 88–101.
Scharf, P. C., & Lory, J. A. (2002). Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agronomy Journal, 94(3), 397–404.
Simic Milas, A., Romanko, M., Reil, P., Abeysinghe, T., & Marambe, A. (2018). The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images. International Journal of Remote Sensing, 39(15–16), 5415–5431.
Sonobe, R., Sano, T., & Horie, H. (2018). Using spectral reflectance to estimate leaf chlorophyll content of tea with shading treatments. Biosystems Engineering, 175, 168–182.
Thomas, J., & Gausman, H. (1977). Leaf reflectance vs. leaf chlorophyll and carotenoid concentrations for eight crops 1. Agronomy Journal, 69(5), 799–802.
Verrelst, J., Rivera, J. P., Gitelson, A., Delegido, J., Moreno, J., & Camps-Valls, G. (2016). Spectral band selection for vegetation properties retrieval using Gaussian processes regression. International Journal of Applied Earth Observation and Geoinformation, 52, 554–567.
Verrelst, J., Rivera, J. P., Leonenko, G., Alonso, L., & Moreno, J. (2014). Optimizing LUT-based RTM inversion for semiautomatic mapping of crop biophysical parameters from Sentinel-2 and-3 data: Role of cost functions. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 257–269.
Verrelst, J., Rivera, J. P., Veroustraete, F., Muñoz-Marí, J., Clevers, J. G., Camps-Valls, G., et al. (2015). Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods—A comparison. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 260–272.
Vovk, V. (2013). Kernel Ridge Regression. In B. Schölkopf, Z. Luo, & V. Vovk (Eds.), Empirical Inference. Heidelberg: Springer, Berlin.
Welling, M. (2013). Kernel ridge regression. In Max Welling's Classnotes in Machine Learning (pp. 1–3).
Zarco-Tejada, P. J., González-Dugo, V., & Berni, J. A. (2012). Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sensing of Environment, 117, 322–337.
Acknowledgements
This study was supported by the ICAR-NESAC joint project funded by the Department of Science and Technology. The authors appreciate the support of UAV team of NESAC that helped in the critical data collection procedure. We specially thank Director, NESAC, for their support and motivation.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest and financial interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Singhal, G., Bansod, B., Mathew, L. et al. Estimation of Leaf Chlorophyll Concentration in Turmeric (Curcuma longa) Using High-Resolution Unmanned Aerial Vehicle Imagery Based on Kernel Ridge Regression. J Indian Soc Remote Sens 47, 1111–1122 (2019). https://doi.org/10.1007/s12524-019-00969-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12524-019-00969-9