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Reseach Article

Application of Geoinformatics in Automated Crop Inventory

Published on August 2015 by Sandeep Kumar Singla, O. P. Dubey, R. D. Garg
International Conference on Advancements in Engineering and Technology
Foundation of Computer Science USA
ICAET2015 - Number 11
August 2015
Authors: Sandeep Kumar Singla, O. P. Dubey, R. D. Garg
3fbddb41-e367-45f2-9036-bef9673e67b0

Sandeep Kumar Singla, O. P. Dubey, R. D. Garg . Application of Geoinformatics in Automated Crop Inventory. International Conference on Advancements in Engineering and Technology. ICAET2015, 11 (August 2015), 22-29.

@article{
author = { Sandeep Kumar Singla, O. P. Dubey, R. D. Garg },
title = { Application of Geoinformatics in Automated Crop Inventory },
journal = { International Conference on Advancements in Engineering and Technology },
issue_date = { August 2015 },
volume = { ICAET2015 },
number = { 11 },
month = { August },
year = { 2015 },
issn = 0975-8887,
pages = { 22-29 },
numpages = 8,
url = { /proceedings/icaet2015/number11/22284-4158/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advancements in Engineering and Technology
%A Sandeep Kumar Singla
%A O. P. Dubey
%A R. D. Garg
%T Application of Geoinformatics in Automated Crop Inventory
%J International Conference on Advancements in Engineering and Technology
%@ 0975-8887
%V ICAET2015
%N 11
%P 22-29
%D 2015
%I International Journal of Computer Applications
Abstract

An attempt has been made in this study to review the role of geoinformatics to discriminate different crops at various levels of classification, monitoring crop growth and prediction of the crop yield. The suitability of geoinformatics techniques suited to Indian conditions has also been assessed. Development in applications of computers and information technology has enhanced the capability of gathering huge and mottled data as well as information, ranging from historical data, ground truth values and aerial photography to satellite data. Thus remote sensing data and the information derived from it, is attractive to agricultural management system in the India. It is concluded that, in addition to the remote sensing technology, the use of many other techniques such as ground observations, reviews, GIS and soil analysis is highly appreciable.

References
  1. Ahmad, T. , Singh, R. and Rai, A. , (2003). Development of GIS based technique for Identification of Potential Agro-forestry area. Project Report, IASRI, New Delhi.
  2. Anson, A. , (1966). Color photography comparison, Photogrammetric Engineering, 32, 286-297.
  3. Anup, K. P. , Chai, L. , Ramesh, P. S. and Kafatos, M. , (2005). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1), 26-33.
  4. Benedetti, R. and Rossini, P. , (1993). On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing of Environment, 45(3), 311–326.
  5. Chimnarong, V. , Rethinam, S. , Seechan, M. and Pliansinchai, U. , (2012). In the proceedings of 33rd asian conference on remote sensing, Thailand.
  6. Coleman, V. B. , Johnson, C. W. and Lewis, L. N. , (1974). Remote sensing in control of pink bollworm in Cotton, California Agriculture, 28(9), 10-12.
  7. Das, D. , Mishra, K. and Kalra, N. , (1993). Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices, International Journal of Remote Sensing, 14(17), 3081-3092.
  8. Das, S. K. , (2004). Application of multiple frame sampling technique for crop surveys using remote sensing satellite data. A Ph. D. thesis submitted to P. G. School, IARI, New Delhi.
  9. Doorenbos, J. and Kassam, A. H. , (1979). Yield Response to Water. FAO Irrigation Drainage Paper 33, United Nations, Rome, p. 193.
  10. Doraiswamy, P. C. , Moulin, S. , Cook, P. W. and Stern, A. , (2003). Crop yield assessment from remote sensing, Photogrammetric Engineering and Remote Sensing, 69(6), 665-674.
  11. Ferencz, Cs. , Bognar, P. , Lichtenberger, J. , Hamar, D. , Tarcsai, Gy. , Timar, G. , Molnar, G. , Pasztor, Sz. , Steinbach P. , Szekely B. , Ferencz O. and Ferencz-Arkos, I. , (2004). Crop Yield Estimation by Satellite Remote Sensing. International Journal of Remote Sensing, 25(20), 4113-4149.
  12. Gallego, F. J. , Delince, J. , and Rueda C. , (1993). Crop area estimates through remote sensing: stability of the regression correction, International Journal of Remote Sensing, 14(18), 3433-3445.
  13. Goodman, M. S. , (1959). A technique for the identification of farm crops on aerial photographs, Photogrammetric Engineering, 28, 984-990.
  14. Goodman, M. S. , (1964). Criteria for the identification of types of farming on aerial photographs, Photogrammetric Engineering, 30, 131-137.
  15. Goyal, R. C. , (1990). Use of Remote Sensing Planning of Agricultural Surveys. Project Report, IASRI, New Delhi.
  16. Gupta, N. K. , (2002). Applications of spatial models in estimation of wheat production in rohtak distrct of haryana. Unpublished M. Sc. thesis submitted to P. G. School IARI, New Delhi.
  17. Hamar, D. , Ferencz, C. , Lichtenberger, J. , Tarcsai, G. and Ferencz-Árkos, I. , (1996). Yield estimation for corn and wheat in the Hungarian Great Plain using Landsat MSS data. International Journal of Remote Sensing, 17(9), 1689-1699.
  18. Hayes, M. J. and Decker, W. L. , (1996). Using NOAA AVHRR data to estimate maize production in the United States Corn Belt. International Journal of Remote Sensing, 17, 3189-3200.
  19. Hoffer, R. M. and Goodrick, F. E. , (1971). Geographic considerations in automatic cover type identification, in Proceedings of the Indiana Academy of Science, 80, 230-44.
  20. Houseman, E. E. and Huddleston, H. F. , (1966). Forecasting and estimating crop yields from plant measurements, Monthly bulletin of Agriculture Economics and Statistics, 15(10).
  21. Hu, S. and Mo, X. , (2011). Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Ecological Modelling, 222(14), 2530-2541.
  22. Ibrahim, A. E. I. , (1992). Use of remote sensing data in a markov chain model for crop yield forecasting. Project report IASRI, New Delhi.
  23. Johnson C. W. , Bowden, L. W. and Pease, R. W. , (1969). Studies in remote sensing of Southern California and related environment, University of California, Riverside, California, Status Report III, Technical Report V.
  24. Jung, M. , Churkina, G. , Henkel, K. , Herold, M. and Churkina, G. , (2006). Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101(4), 534-553.
  25. Kristof, S. J. , (1969). Preliminary multispectral studies of soil, Journal of Soil water conservation, 26, 15-18.
  26. Landgrebe, D. A. and Staff, (1967). Automatic Identification and classification of wheat by remote sensing, Purdue Agric. Experiment Station Res. Prog. Report, 279.
  27. Langley, S. K. , Cheshire, H. M. and Humes, K. S. , (2001). A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland. Journal of Arid Environments, 49(2), 401-411.
  28. Lennington, R. K. , and Sorensen, C. T. , (1984). A mixture model approach for estimating crop areas from Landsat data, Remote Sensing of Environment, 14, 197-206.
  29. MacDonald, R. B. and Hall, F. G. , (1980). Global crop forecasting, Science, 208, 670-679.
  30. Mccloy, K. R. , Smith, F. R. and Robinson, M. R. , (1987). Monitoring rice areas using Landsat MSS data, International Journal of Remote Sensing, 8(5), 741-749.
  31. Mishra, V. , Cruise, J. F. , Mecikalski, J. R. , Hain, C. R. and Anderson, M. C. , (2013). A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields, Remote Sensing, 5(7), 3331-3356.
  32. Mkhabela, M. S. , Bullock, P. , Raj, S. , Wang, S. and Yang, Y. , (2011), Crop yield forecasting on the Canadian Prairies using MODIS NDVI data, Agricultural and Forest Meteorology, 151(3), 385-393.
  33. Morain, S. A. , (1970). Radar sensing in agriculture; an overview, condensed from CRES Technical report, 177-214.
  34. Morain, S. A. , Wood, C. and Conte D. , (1970). Earth observation Survey program 90-day mission report, NASA/MSC mission 102, site 87, CRES Technical Memo 169-4, Centre for Research, University of Kansas, Lawerence, Kansas, 16.
  35. Nordberg, M. L. and Evertson, J. , (2003), Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM and ETM+ imagery, Land Degradation and Development, 16, 139-149.
  36. Rao, P. P. N. , and Rao, V. R. , (1987). Rice crop identification and area estimation using remotely sensed data from India cropping patterns, International Journal of Remote Sensing, 8(4),639-650.
  37. Ren, J. , Chen, Z. , Zhou, Q. and Tang, H. , (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10(4), 403-413.
  38. Reynolds, C. A. , Yitayew, M. , Slack, D. C. , Hutchinson, C. F. , Huete, A. and Petersen, M. S. , (2000). Estimation crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data. International Journal of Remote Sensing, 21(18), 3487-3508.
  39. Roberts, E. H. and Gialdini, M. J. , (1970). Multispectral Analysis for crop identification, A Report to USDA Status Reporting Service by the Forestry Remote Sensing Lab. , University of California.
  40. Rouse, J. W. , Haas, R. H. , Schell, J. A. and Deering, D. W. , (1974). Monitoring vegetation sys-tems in the Great Plains with ERTS. Third NASA ERTS Symposium, NASA SP-351, United States, pp. 309–317.
  41. Rudorff, B. F. T. and Batista, G. T. , (1990). Yield Estimation of Sugarcane Based on Agrometeorological-Spectral Models. Remote Sensing of Environment, 33, 183-192.
  42. Schwarz, D. E. and Caspall, F. , (1968). The use of Radar in discrimination and identification of agricultural land use, in Proceedings of the 5th Symposium on Remote Sensing and Environment, University of Michigan, 233-247.
  43. Singh R. , Goyal, R. C. , Pandey, L. M. and Shah, S. K. , (2000). Use of remote sensing technology in crop yield estimation survey-II. Project report IASRI, New Delhi.
  44. Singh, R. and Goyal, R. C. , (1993). Use of remote sensing technology in crop yield estimation surveys. Project Report, IASRI, New Delhi.
  45. Singh, R. , Semwal, D. P. , Rai, A. and Chhikara, R. S. , (2002). Small area estimation of crop yield using remote sensing satellite data. International Journal of Remote Sensing, 23(1), 49-56.
  46. Small, R. P. , (1967). Research report on tart cherry objective yield surveys, Statistical Reporting Service, USDA.
  47. Sun, Jiulin, Chen, S. , Qian, H. and Zhang, D. , (1996). Series Monographs of the Study on Dynamic Monitoring and Yield Estimation of Crops with Remote Sensing in China, Science and Technology Press of China, Beijing, 238 p.
  48. Tennakoon, S. B. , Murty, V. V. N. and Eiumnoh, A. , (1992). Estimation of cropped area and grain yield of rice using remote sensing data, International Journal of Remote Sensing, 13(3), 427-439.
  49. Tucker, C. J. , Holben, B. N. , Elgin, J. H. and McMurtrey, J. E. , (1980). Relationship of spectral data to grain yield variation. Photogrammetric. Engineering and Remote Sensing, 46, 657-666.
  50. Wang, L. , Tian, Y. , Yao, X. , Zhu, Y. and Cao, W. , (2014). Predicting grain yield and protein content in wheat by fusing multi-sensor and multi-temporal remote-sensing images. Field Crop Research, 164, 178-188.
Index Terms

Computer Science
Information Sciences

Keywords

Remote Sensing Crop Yield Geoinformatics Gis Gps Rdbms Satellite Data Crop Inventory Crop Models.