REMOTE SENSING FOR DETECTING AND DISTINGUISHING MOISTURE AND NITROGEN STRESS IN MAIZE | ||
| Misr Journal of Agricultural Engineering | ||
| Article 3, Volume 31, Issue 3, July 2014, Pages 753-764 PDF (668.11 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/mjae.2014.98895 | ||
| Author | ||
| A. H. Elmetwalli | ||
| Assi. Prof., Agric. Eng. Dept., Fac. of Agric., Tanta Univ., Egypt. | ||
| Abstract | ||
| Remote sensing has been known as a robust technique in precision farming over the last quarter of the 20th century. It has been successfully used to asses many biophysical and biochemical properties of various crops. Detecting stress in crops at an early growth stage is important to limit crop reductions and therefore increasing productivity. Thus, remote sensing may be a valuable tool for precision farming in cereal production. The present study was conducted to investigate the effectiveness of broad band and hyperspectral remotely sensed data to quantify maize (Zea maize L.) grain yield under moisture and nitrogen deficiency stresses. The results demonstrated strong significant correlations between various crop properties and some vegetation indices. RVI, SAVI, OSAVI and R750/R550 were found to be sensitive to maize grain yield (r > 0.80). The correlations with grain yield were found to be strongest at the grain filling stage. Penalized Linear Discrimnant Analysis (PLDA) and Principle Component Analysis (PCA) demonstrated the possibility to distinguish between moisture and nitrogen deficiency stress. | ||
| Keywords | ||
| Remote sensing; Reflectance; nitrogen deficiency; moisture; stress | ||
| References | ||
|
Abdel-Rahman, E. M.; Ahmed, F. B. and Van Dan Berg, M. (2010). Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 125: 552-557. Araus, J. L.; Casadesus, J. and Bort, J. (2001). Recent tools for the screening of physiological traits determining yield. P. 59-77. In M.P. Reynolds, J. I. Ortiz-Monasterio and A. Mcnab (Eds.) Application of physiology in wheat breeding. CIMMYT, Mexico. Babar, M. A.; Reynolds, M. P.; van Ginkel, M.; Klatt, A. R.; Raun, W. R. and Stone, M. L. (2006). Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci. 46: 578-588. Banziger, M. and Diallo, A. O. (2004). Progress in developing drought and N stress tolerant maize cultivars for eastern and southern Africa. Pp. 189-194 In D.K. Friesen and A.F.E. Palmer (eds). Integrated approaches to higher maize productivity in the new millennium. Proceeding of the 7th eastern and southern Africa regional maize conference, 5-11 February 2002. CIMMYT/KARI, Nairobi, Kenya. Ciganda, V.; Gitelson, A. and Schepers, J. (2009). Non-destructive determination of maize leaf and canopy chlorophyll content. Journal of Plant Physiology, 166: 157-167. Daughtry, C. S. T.; Walthall, C. L.; Kim, M. S.; Brown de Colstoun, E. and McMurtrey, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74: 229-239. Elmetwalli, A.M. (2008). Remote sensing as a precision farming tool in the Nile Vally, Egypt. Ph.D Thesis in Environmental Sciences, School of Biological and Environmental Sciences, University of Stirling, Stirling, UK. Elmetwalli, A.H. (2010). The potential of Remotely sensed data to predict wheat yield under moisture and nitrogen defeciency stress. Misr J. Agric. Eng., 27(4): 1823-1835. Hong, S.-D.; Schepers, J. S.; Francis, D. D. and Schlemmer, M. R. (2007). Comparisons of ground-based remote sensors for evaluation of corn biomass affected by nitrogen stress. Communications in Soil Science and Plant Analysis 38: 2209-2226. Osborne, L. S., Schepers, J. S., Francis, D. D. and Schlemmer, M. R. (2002). Detecting Phosphorus and Nitrogen Deficiencies in Corn Using Radiance Measurements. Agronomy Journal 94: 1215-1221. Paolo, E. and Rinaldi, M. (2008). Yield response of corn to irrigation and nitrogen fertilization in a Mediterranean environment. Field Crops Research 105: 202-210. Prasad, B., Carver, B. F.; Stone, M. L.; Babar, M. A.; Raun, W. R. and Klatt, A. R. (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under Great Plains conditions. Crop Science 47: 1426-1440. Souza, R. P., Machado, E. C., Silva, J. A. B., Lagoa, A. M. M. A. and Silveira, J. A. G. (2004). Photosynthetic gas exchange, chlorophyll fluorescence, and some associated metabolic changes in cowpea (Vigna unguiculata) during water stress and recovery. Environ. Exp. Bot. 51: 45-56. Tilling, A. K.; Leary, G. J.; Ferwerda, J. G.; Jones, S. D.; Fitzgerald, G. J.; Rodriguez, D. and Belford, R. (2007). Remote sensing of nitrogen and water stress in wheat. Field Crops Research 104: 77-85. Wang, D.; Wilson, C. and Shannon, M. C. (2002). Interpretation of salinity and irrigation effects on soybean canopy reflectance in visible and near infrared spectrum domain. International Journal of Remote Sensing 23(5): 811-824. Weber, V.S.; Araus, J.L.; Cairns, J.E.; Sanchez, C.; Melchinger, A.E. and Orsini, E. (2012). Prediction of grain yield using reflectance spectra of canopy and leaves in maize plants grown under different water regimes. Field Crops Research, 128: 82-90. Zhang, J. C.; Pu, R.L.; Wang, J.H.; Haung, W.J. Yuan, L. and Luo, J.H. (2012). Detecting powder mildew of winter wheat using leaf level hyperspectral measurements. Computers and electronics in Agriculture, 85: 13-23 | ||
|
Statistics Article View: 220 PDF Download: 233 |
||
