Date: 13 Apr 2012
A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring
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Image-based remote sensing is one promising technique for precision crop management. In this study, the use of an ultra light aircraft (ULA) equipped with broadband imaging sensors based on commercial digital cameras was investigated to characterize crop nitrogen status in cases of combined nitrogen and water stress. The acquisition system was composed of two Canon® EOS 400D digital cameras: an original RGB camera measuring luminance in the Red, Green and Blue spectral bands, and a modified camera equipped with an external band-pass filter measuring luminance in the near-infrared. A 5 month experiment was conducted on a sugarcane (Saccharum officinarum) trial consisting of three replicates. In each replicate, two sugarcane cultivars were grown with two levels of water input (rainfed/irrigated) and three levels of nitrogen (0, 65 and 130 kg/ha). Six ULA flights, coupled with ground crop measurements, took place during the experiment. For nitrogen status characterisation, three indices were tested from the closed canopy: the normalised difference vegetation index (NDVI), the green normalised difference vegetation index (GNDVI), and a broadband version of the simple ratio pigment index (hereafter referred to as the SRPIb), calculated from the ratio between blue and red bands of the digital camera. The indices were compared with two nitrogen crop variables: leaf nitrogen content (NL) and canopy nitrogen content (NC). SRPIb showed the best correlation (R 2 = 0.7) with NL, independently of the water and the N treatment. NDVI and GNDVI were best correlated with NC values with correlation coefficients of 0.7 and 0.64 respectively, but the regression coefficients were dependent on the water and N treatment. These results showed that SRPIb could characterise the nitrogen status of sugarcane crop, even in the case of combined stress, and that such acquisition systems are promising for crop nitrogen monitoring.
Abdel-Rahman, E. M., Ahmed, F. B., & van den Berg, M. (2010). Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 12, S52–S57.CrossRef
Atkinson, P. R., & Nuss, K. J. (1989). Associations between host-plant nitrogen and infestation of sugarcane borer, Eldana saccharina Walker (Lepidoptera: Pyralidae). Bulletin of Entomological Research, 79, 489–506.CrossRef
Bellairs, S. M., Turner, N. C., Hick, P. T., & Smith, R. C. G. (1996). Plant and soil influences on estimating biomass of wheat in plant breeding plots using field spectral radiometers. Australian Journal of Agricultural Research, 47, 1017–1034.CrossRef
Blackmer, T. M., Schepers, J. S., Varvel, G. E., & Meyer, G. E. (1996). Analysis of aerial photography for nitrogen stress within corn fields. Agronomy Journal, 88, 729–733.CrossRef
Carter, G. A., & Miller, R. L. (1994). Early detection of plant stress by digital imaging within narrow stress-sensitive wavebands. Remote Sensing of Environment, 50(3), 295–302.CrossRef
Chappelle, E. W., Kim, M. S., & McMurtrey, J. E., I. I. I. (1992). Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll A, chlorophyll B, and carotenoids in soybean leaves. Remote Sensing of Environment, 39(3), 239–247.CrossRef
Déliot, P., Duffaut, J., & Lacan, A. (2005). Characterization and calibration of a high-resolution multi-spectral airborne digital camera. Proceedings of the 20th Congress of the International Commission for Optics, Changchun, China, 21–25 August 2005 (pp. 603104:603101-603104:603110). Changchun.
Draper, N. R., & Smith, H. (1998). Applied regression analysis (3rd ed.). New York: Wiley.
Filella, J., & Penuelas, J. (1994). The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. International Journal of Remote Sensing, 15, 1459–1470.CrossRef
Fois, S., Motzo, R., & Giunta, F. (2009). The effect of nitrogenous fertiliser application on leaf traits in durum wheat in relation to grain yield and development. Field Crops Research, 110(1), 69–75.CrossRef
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, 58, 289–298.CrossRef
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, 90(3), 337–352.CrossRef
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.CrossRef
Han, S., Hendrickson, L. L., & Ni, B. (2002). Comparison of satellite and aerial imagery for detecting leaf chlorophyll content in corn. Transactions of the American Society of Agricultural and Biological Engineers, 45(4), 1229–1236.
Heege, H. J., Reusch, S., & Thiessen, E. (2008). Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany. Precision Agriculture, 9, 115–131.CrossRef
Ingram, K. T., & Hilton, H. W. (1986). Nitrogen–potassium fertilization and soil moisture effects on growth and development of drip irrigated sugarcane. Crop Science, 26, 1034–1039.CrossRef
Lamb, D. W. (2000). The use of qualitative airborne multispectral imaging for managing agricultural crops—A case study in south-eastern Australia. Australian Journal of Experimental Agriculture, 40, 725–738.CrossRef
Lebourgeois, V., Bégué, A., Labbé, S., Mallavan, B., Prévot, L., & Roux, B. (2008). Can commercial digital cameras be used as multispectral sensors? A crop monitoring test. Sensors, 8(11), 7300–7322.CrossRef
Lee, W. S., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., & Li, C. (2010). Sensing technologies for precision specialty crop production. Computers and Electronics in Agriculture, 74(1), 2–33.CrossRef
Lelong, C., Burger, P., Jubelin, G., Roux, B., Labbé, S., & Baret, F. (2008). Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, 8, 3557–3585.CrossRef
Martiné, J. F. (2004). Estimation of total surface of green leaves through simple in situ measurements on sugarcance crop (p. 4). Saint Denis: CIRAD.
Miao, Y., Mulla, D. J., Randall, G. W., Vetsch, J. A., & Vintila, R. (2009). Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture, 10, 45–62.CrossRef
Moran, M. S. 2000. Image-based remote sensing for agricultural management—Perspectives of image providers, research scientists and users. Proceedings of the 2nd international conference on geospatial information in agriculture and forestry, vol 1, Lake Buena Vista, FL, USA, January 10–12, 2000 (pp. 23–30). Ann Arbor: ERIM International.
Moran, M. S., Fitzgerald, G., Rango, A., Walthall, C., Barnes, E., Bausch, W., et al. (2003). Sensor development and radiometric correction for agricultural applications. Photogrammetric Engineering and Remote Sensing, 69(6), 705–718.
Moran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61, 319–346.CrossRef
Penuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31(2), 221–230.
Penuelas, J., & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3(4), 151–156.CrossRef
Penuelas, 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, 48, 135–146.CrossRef
Penuelas, J., Gamon, J. A., Griffin, K. L., & Field, C. B. (1993). Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sensing of Environment, 46(2), 110–118.CrossRef
Pierrot-Deseilligny, M., Cléry, I. (2011). APERO, an open source bundle adjusment—“Software for automatic calibration and orientation of a set of images.” In F. Remondino & S. El-Hakim (Eds.), Proceedings of the ISPRS commission V symposium on image engineering and vision metrology, March 2–4, 2011. Trento, Italy.
Pinter, P., Jr, Hatfield, J., Schepers, J., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., et al. (2003). Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing, 69(6), 647–664.
Rouse, J. W., Hass, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS. In Proceedings of the third ERTS symposium, NASA special publication No. 351 (pp. 309–317). Washington: NASA.
Suits, D. B. (1957). Use of dummy variables in regression equations. Journal of the American Statistical Association, 52(280), 548–551.CrossRef
Wiedenfeld, R. P. (1995). Effects of irrigation and N fertilizer application on sugarcane yield and quality. Field Crops Research, 43, 101–108.CrossRef
Williams, J. D., Kitchen, N. R., Scharf, P. C., & Stevens, W. E. (2010). Within-field nitrogen response in corn related to aerial photograph color. Precision Agriculture, 11, 291–305.CrossRef
Wu, J., Wang, D., Rosen, C. J., & Bauer, M. E. (2007). Comparison of petiole nitrate concentrations, SPAD chlorophyll readings, and QuickBird satellite imagery in detecting nitrogen status of potato canopies. Field Crops Research, 101(1), 96–103.CrossRef
- A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring
Volume 13, Issue 5 , pp 525-541
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Ultra light aircraft
- Digital camera
- Spectral index
- SPAD chlorophyll meter
- Industry Sectors
- Author Affiliations
- 1. CIRAD UPR SCA, Station La Bretagne, 97408, Saint-Denis, La Réunion, France
- 2. CIRAD UMR TETIS, Maison de la Télédétection, 500 rue Jean-François Breton, 34093, Montpellier, France
- 3. IRSTEA UMR TETIS, Maison de la Télédétection, 500 rue Jean-François Breton, 34093, Montpellier, France
- 4. L’Avion Jaune, 1, Chemin du Fescau, 34980, Montferrier-sur-Lez, France