GROUND-BASED REMOTE SENSING FOR ESTIMATING THE CUMULATIVE EFFECT OF DIFFERENT IRRIGATION WATER QUALITIES ON THE PRODUCTIVITY AND GROWTH OF POTATO | ||||
| Misr Journal of Agricultural Engineering | ||||
| Article 23, Volume 33, Issue 3, July 2016, Page 1141-1168 PDF (1.08 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjae.2016.97798 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| S. Elsayed1; M. M. Ibrahim2 | ||||
| 1Assistant Prof. of Agric. Eng. Evaluation of Natural Resources Department, Environmental Studies and Research Institute, Sadat City University, Egypt. | ||||
| 2Assistant Prof. of Agric. Eng. Dept., Faculty of Agric., Al-Mansoura University, Egypt. | ||||
| Abstract | ||||
| The growing competition of water available from Nile fresh water, coupled with laws limiting ground water pumping, has led to utilization of different water qualities in irrigation proposals. In this study, the effect of mixed water on yield, biomass and relative chlorophyll meter was estimated. The performance of ground-based remote sensing of hyperspectral passive reflectance sensor and digital image analysis as precision agriculture tools was tested at tubers bulking growth stage to assess their relationship to detractively measured parameters via simple linear regression and three models based on partial least square regression (PLSR) analysis. The results showed that the newly developed indices (R970 - R730)/(R970 + R730) and (R990 - R730)/(R990 + R730) showed close and highly significant associations with yield, biomass and relative chlorophyll meter, with R2 values reach to 0.87, 0.80, and 0.86, respectively, while the green cover (%) was highly significantly related the same parameters, with R2 values reach to of 0.82, 0.73 and 0.70, respectively. Three models of PLSR based on (i) the spectral reflectance from 302 to 1148 nm, (ii) selected nine spectral indices and (iii) selected eight red–blue–green (RBG) indices of the images analysis for the measured parameters were apparently useful for estimating the yield, aerial biomass and relative chlorophyll meter of potato cultivar under fresh and mixed water treatments. In conclusion, the assessment of measured parameters was improved and more robust when using the multivariate analysis of PLSR models than with previously assayed normalized difference spectral indices and RGB indices from digital image analysis. | ||||
| Keywords | ||||
| sewage water; remote sensing; precision agriculture; spectral indices; RGB indices; potato | ||||
| References | ||||
|
Adamsen, F. G., Pinter, J. P., Barnes, M. E., LaMorte, L. R., Wall, W. G., Leavitt, W. S., Kimball., A. B., (1999). Measuring wheat senescence with a digital camera. Crop. Sci. 39, 719-724.
Allam A.E. and Negm, A.M., (2013). Agricultural drainage water quality analysis and its suitability for direct reuse in irrigation: case study: Kafr El-Sheikh governorate, Egypt”, Seventeenth International Water Technology Conference, IWTC17 Istanbul, 5-7 November, 2013.
Auearunyawat, P., Kasetkasem S.A. (2012). An Automatic Nitrogen Estimation Method inSugarcane Leaves Using Image Processing Techniques‖ International Conference on Agricultural, Environment and Biological Sciences (ICAEBS'2012) May 26-27.
El-Hendawy, S., Al-Suhaibani, N., Salem, A., Ur Rehman, S., Schmidhalter, U. (2015). Spectral reflectance indices as a rapid nondestructive phenotyping tool for estimating different morphophysiological traits of contrasting spring wheat germplasms under arid conditions. Turk. J. Agric. For. 39: 572-587.
Elsayed, S., Elhoweity, M., Schmidhalter, U. (2015a). Normalized difference spectral indices and partial least squares regression to assess the yield and yield components of peanut. Aust. J. Crop Sci. 10: 976-986
Elsayed, S., Rischbeck, P., Schmidhalter, U. (2015b). Comparing the performance of active and passive reflectance sensors to assess the normalized relative canopy temperature and grain yield of drought-stressed barley cultivars. Field Crop Res. 177:148-160.
El-Shikha, D.M., Waller, P., Hunsaker, D., Clarke, T., Barnes E. (2007). Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agr Water Mange. 92:183-193.
Erdle, K., Mistele, B., Schmidhalter, U. (2011). Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 124: 74-84. Fpereira, B.F., He, Z.L., Silva, M.S., Herpin, U., Nogueira, S.F., Montes, C.R., Melfi, A.J. (2011). Reclaimed wastewater: Impact on soil-plant system under tropical conditions. J. Hazard. Mater. 192: 54-61
Gutierrez, M., Reynolds, M.P., Raun, W.R., Stone, M.L., Klatt, A.R. (2010). Spectral water indices for assessing yield in elite bread wheat genotypes in well irrigated, water stressed, and high temperature conditions. Crop Sci. 50: 197-214.
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 in precision agriculture. Remote Sens. Environ. 814: 16-426.
Hu, H., Zhang, J., Sun, X., Zhang, X. (2013). Estimation of leaf chlorophyll content of rice using image color analysis. Can. J. Remote Sens. 39: 185-190.
Jia, L., Chen, X., Zhang, F., Buerkert, A., Roemheld, V. (2004). Use of a digital camera to assess nitrogen status of winter wheat in the Northern China Plain. J. Plant Nutr. 27: 441-450.
Jurwakar, A.S. Jurwakar, A., Deshbharatar, P.B., Bal, A.S. (1991). Asian Experiences in Integrated Plant Nutrition, 178-201, RAPA. FAO, Bangkok.
Kiziloglu, F.M., Turan, M., Sahin, U., Kuslu, Y., Dursun, A., (2008). Effects of untreated and treated wastewater irrigation on some chemical properties of cauliflower (Brassica olerecea L. var. botrytis) and red cabbage (Brassica olerecea L. var. rubra) grown on calcareous soil in Turkey. Agr Water Mange. 95: 716-724.
Kipp, S., Mistele, B., Baresel, P., Schmidhalter, U. (2014a). High-throughput phenotyping early plant vigour of winter wheat. Eur. J. Agron. 52, 271-278.
Lee, J. k., Lee, W.B. (2013). Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. Eur. J. Agron. 48: 57- 65.
Li, F., Mistele, B., Hu, Y., Chen, X., Schmidhalter, U. (2014). Reflectance estimation of canopy nitrogen content in winter wheat using optimized hyperspectral spectral indices and partial least squares regression. Eur. J. Agron. 52:198-209.
Lobos, G.A., Matus, I., Rodriguez, A., Romero-Bravo, S., Araus, J.L, Pozo, A.D. (2014). Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance. J Integr Plant Biol. 56: 470-479.
Lukina, E.V., Stone, M. L., Raun, W.R. (1999). Estimating vegetation coverage in wheat using digital images. J. Plant Nutr. 22, 341-350.
Mahida, U.N. (1981). Influence of sewage irrigation on vegetable crops. In: Water pollution and disposal of waste water on land, Tata Mcgrew Hill Pub., New Delhi.
Mistele, B., Schmidhalter, U. (2008). Spectral measurements of the total aerial N and biomass dry weight in maize using a quadrilateral-view optic. Field Crops Res. 106: 94-103.
Paruelo, J.M., Lauenroth, W.K., Roset, P.A., (2000). Estimating aboveground plant biomass using a photographic technique. J. Range Manage. 53: 190-193.
Prasad B, Carver BF, Stone ML, Babar MA, Raun WR, Klatt AR (2007). Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under Great Plains conditions. Crop Sci. 47:1426-1440.
Pity, Auaerunyawat., Teerasit Kasetkasem., Audthasit Wongmaneeroj, Akinori Nishirara and Rachaporn Keinprasit. (2012). An Automatic Nitrogen Estimation Method in Sugarcane Leaves using Image Processing Techniques. International Conference on Agricultural, Environment and Biological Sciences.
Rischbeck, P., Elsayed, S., Mistele, B., Barmeier, G., Heil K., Schmidhalter U. (2016). Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. Eur. J. Agron. 78: 44-59.
Sharabian, R.V., Noguchi, N., Ishi, K. (2014). Significant wavelengths for prediction of winter wheat growth status and grain yield using multivariate analysis. Eng. Agr. Environ. Food. 7: 14-21.
Schmidhalter, U., Glas, J., Heigl, R., Manhart, R., Wiesent, St., Gutser, R., Neudecker, E. (2001). Application and testing of a crop scanning instrument – field experiments with reduced crop width, tall maize plants and monitoring of cereal yield. In: Proceedings of the 3rd European Conference on Precision Agriculture. Eds. Grenier G., Blankmore S., agro Montpellier, France, 953-958.
Townsend, P.A, Foster, J.R., Chastain R.A., Currie W.S. (2003). Application of imaging spectroscopy to mapping canopy nitrogen in the forests of the central Appalachian Mountains using Hyperion and AVIRIS. IEEE T Geosci Remote. 41:1347-1354.
Yadar, R.K., Goyal, R., Sharma, R.K., Dubey, S.K., Minchas, R.S. (2002). Post irrigation impact of domestic sewage effluent on composition of soils, crops and ground water – a case study. J. Environ. Int. 28, 481–486. | ||||
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