Application of GIS and RS in real time crop monitoring and yield forecasting: a case study of cotton fields in low and high productive farmlands

Zokhid Mamatkulov; Eshkobil Safarov; Rustam Oymatov; Ilhom Abdurahmanov; Maksud Rajapbaev

doi:10.1051/e3sconf/202122703001

All issues

Volume 227 (2021)

E3S Web Conf., 227 (2021) 03001

References

Open Access

Issue		E3S Web Conf. Volume 227, 2021 Annual International Scientific Conference on Geoinformatics – GI 2021: “Supporting sustainable development by GIST”


Article Number		03001
Number of page(s)		10
Section		GIS in Agriculture
DOI		https://doi.org/10.1051/e3sconf/202122703001
Published online		06 January 2021

C. L. Wiegand. The value of direct observations of crop canopies for indicating growing conditions and yield. The 18th International Symposium on Remote Sensing of Environment, 18, 1551 (Paris, 1984). [Google Scholar]
N. A. Quarmby, M. Milnes, T.L. Hindle and Silleos. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Inter. J. Remote Sens., 14, 199 (1993) [CrossRef] [Google Scholar]
I. Ahmad, D. A. Awan, M. Bhatti, I. H. Akhtar, M. Ibrahim. Satellite Remote Sensing and GIS based Crop Forecasting System in Pakistan. Crop monitoring for improvement food security, 95 (FAO, 2014) [Google Scholar]
T. Sh. Beisenboyev, N.F. Bespalov, Salinization dynamics of irrigated soils and cotton productivity. Tashkent (1993) [Google Scholar]
Annual report of Ministry of Water Resources of the Republic of Uzbekistan (2018) [Google Scholar]
P. Kingra, D. Majumder. Application of Remote Sensing and GIS in Agriculture and Natural Resource Management Under Changing Climatic Conditions. Agri. Research J., 53(3), 295 (2016) [Google Scholar]
Z. J. Mamatkulov, E.Yu. Safarov, R.K. Oymatov, I.I. Abdurahmanov. Application of GIS and Remote Sensing in crop monitoring and yield forecasting in case of lowyielded farmlands. J. Problems Architecture. Const., Samarkand, Spatial Volume, 130 (2019) [Google Scholar]
C. Atzberger. Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. J. Remote Sens, 5, 949 (2013) [CrossRef] [Google Scholar]
T. Bernardes, M. A. Meriera, M. Adami, A. Giarolle and B. F. Rudorff. Monitoring biennial bearing effect on coffee yield using MODIS remote sensing imagery. Journal of Remote Sens, 4, 2492 (2012) [CrossRef] [Google Scholar]
T. Sakamoto, M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka. A crop phenology detection method using time series MODIS data. Journal of Remote Sens Environ, 96, 366 (2005) [CrossRef] [Google Scholar]
S. Macdonald, United States Department of Agriculture. Retrieved from www.usda.gov/oce/forum (2018) [Google Scholar]
Y. Huang, S. Thomson, Remote Sensing for Cotton Farming (2015) [Google Scholar]
N. Ramarao, Conformity Analysis of Cotton Crop using Remote Sensing and GIS, (Geospatial World, 2009) [Google Scholar]
D. Atakhanov, Cotton yield forecasting in Tashkent province by using Remote Sensing techniques. https://www.wur.nl/en/activity/Cotton-yield-forecasting-in-Tashkent-province-by-using-Remote-Sensing-techniques.htm (2013) [Google Scholar]
Land Fund of the Republic of Uzbekistan (2019) [Google Scholar]
Report of the scientific-research institute of Soil Science and agro-chemistry on the creating soil maps of irrigated land areas and soil evaluation of massives of Jarkurgan District of the Surkhandarya region (Tashkent, 2013). [Google Scholar]
Annual report of the Ministry of Agriculture of the Republic of Uzbekistan (2018) [Google Scholar]
oReports of scientists of the centre of scientific -industrial agriculture of Uzbekistan, (2015) [Google Scholar]
L. Congcong, L. Hongjun, L. Jiazhen, L. Yuping, L. Chunqiang, K. Manevski, Y. Shen. Using NDVI percentiles to monitor real-time crop growth. Journal of Computer and Electronics Agri., 162, 357 (2019) [CrossRef] [Google Scholar]
K. Isabaev, M. Khamidov, D. Alieva, Crop irrigation and productivity. (Tashkent, 1991) [Google Scholar]
P. M. Bartier, C.P Keller. Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Journal of Computers & Geosciences, 22, 795 (1996) [CrossRef] [Google Scholar]
M. Verőné Wojtaszek, Földhasználati tervezés és monitoring (precíziós mezőgazdaság). Land use planning and monitoring (precision agriculture) https://regi.tankonyvtar.hu/hu/tartalom/tamop425/0027_FHT7/ch01s07.html (2010) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] C. L. Wiegand. The value of direct observations of crop canopies for indicating growing conditions and yield. The 18th International Symposium on Remote Sensing of Environment, 18, 1551 (Paris, 1984). [Google Scholar]

[2] N. A. Quarmby, M. Milnes, T.L. Hindle and Silleos. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Inter. J. Remote Sens., 14, 199 (1993) [CrossRef] [Google Scholar]

[3] I. Ahmad, D. A. Awan, M. Bhatti, I. H. Akhtar, M. Ibrahim. Satellite Remote Sensing and GIS based Crop Forecasting System in Pakistan. Crop monitoring for improvement food security, 95 (FAO, 2014) [Google Scholar]

[4] T. Sh. Beisenboyev, N.F. Bespalov, Salinization dynamics of irrigated soils and cotton productivity. Tashkent (1993) [Google Scholar]

[5] Annual report of Ministry of Water Resources of the Republic of Uzbekistan (2018) [Google Scholar]

[6] P. Kingra, D. Majumder. Application of Remote Sensing and GIS in Agriculture and Natural Resource Management Under Changing Climatic Conditions. Agri. Research J., 53(3), 295 (2016) [Google Scholar]

[7] Z. J. Mamatkulov, E.Yu. Safarov, R.K. Oymatov, I.I. Abdurahmanov. Application of GIS and Remote Sensing in crop monitoring and yield forecasting in case of lowyielded farmlands. J. Problems Architecture. Const., Samarkand, Spatial Volume, 130 (2019) [Google Scholar]

[8] C. Atzberger. Advances in remote sensing of agriculture: context description, existing operational monitoring systems and major information needs. J. Remote Sens, 5, 949 (2013) [CrossRef] [Google Scholar]

[9] T. Bernardes, M. A. Meriera, M. Adami, A. Giarolle and B. F. Rudorff. Monitoring biennial bearing effect on coffee yield using MODIS remote sensing imagery. Journal of Remote Sens, 4, 2492 (2012) [CrossRef] [Google Scholar]

[10] T. Sakamoto, M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka. A crop phenology detection method using time series MODIS data. Journal of Remote Sens Environ, 96, 366 (2005) [CrossRef] [Google Scholar]

[11] S. Macdonald, United States Department of Agriculture. Retrieved from www.usda.gov/oce/forum (2018) [Google Scholar]

[12] Y. Huang, S. Thomson, Remote Sensing for Cotton Farming (2015) [Google Scholar]

[13] N. Ramarao, Conformity Analysis of Cotton Crop using Remote Sensing and GIS, (Geospatial World, 2009) [Google Scholar]

[14] D. Atakhanov, Cotton yield forecasting in Tashkent province by using Remote Sensing techniques. https://www.wur.nl/en/activity/Cotton-yield-forecasting-in-Tashkent-province-by-using-Remote-Sensing-techniques.htm (2013) [Google Scholar]

[15] Land Fund of the Republic of Uzbekistan (2019) [Google Scholar]

[16] Report of the scientific-research institute of Soil Science and agro-chemistry on the creating soil maps of irrigated land areas and soil evaluation of massives of Jarkurgan District of the Surkhandarya region (Tashkent, 2013). [Google Scholar]

[17] Annual report of the Ministry of Agriculture of the Republic of Uzbekistan (2018) [Google Scholar]

[18] oReports of scientists of the centre of scientific -industrial agriculture of Uzbekistan, (2015) [Google Scholar]

[19] L. Congcong, L. Hongjun, L. Jiazhen, L. Yuping, L. Chunqiang, K. Manevski, Y. Shen. Using NDVI percentiles to monitor real-time crop growth. Journal of Computer and Electronics Agri., 162, 357 (2019) [CrossRef] [Google Scholar]

[20] K. Isabaev, M. Khamidov, D. Alieva, Crop irrigation and productivity. (Tashkent, 1991) [Google Scholar]

[21] P. M. Bartier, C.P Keller. Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Journal of Computers & Geosciences, 22, 795 (1996) [CrossRef] [Google Scholar]

[22] M. Verőné Wojtaszek, Földhasználati tervezés és monitoring (precíziós mezőgazdaság). Land use planning and monitoring (precision agriculture) https://regi.tankonyvtar.hu/hu/tartalom/tamop425/0027_FHT7/ch01s07.html (2010) [Google Scholar]