Crop acreage and yield mapping of groundnut crop in erstwhile Mahabubnagar District using RS and GIS
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Mar 7, 2023
Sushma Sannidi
Madhu Bindu G. S Neelima T. L
Umadevi M
https://orcid.org/0000-0003-1773-373X
Madhu Bindu G. S Neelima T. L
Umadevi M
Abstract
An investigation was carried in the erstwhile Mahabubnagar district of Telangana during rabi 2019-20 aiming the estimation of groundnut crop acreage and yield. The crop area was estimated using the satellite images of Landsat-8-OLI sensor from September to February covering the entire crop growth period by performing an unsupervised image classification technique with 300 classes, 300 iterations and a convergence threshold of 0.99. The groundnut yield was estimated by developing the regression equation using crop-cut yield data and NDVI values of the corresponding GPS locations. The crop area was estimated to be 57,865 ha with producer’s and user’s accuracy of 100 and 90% respectively, and a relative deviation of 28.6% when compared with actual ground estimates of the Department of agriculture. The crop yields were estimated with an R2 value of 0.71 and a correlation coefficient of 0.87
How to Cite
Sannidi, S., G. S, M. B., T. L, N., & M, U. (2023). Crop acreage and yield mapping of groundnut crop in erstwhile Mahabubnagar District using RS and GIS. Environment Conservation Journal, 24(2), 266–274. https://doi.org/10.36953/ECJ.11652306
Downloads
Download data is not yet available.
##plugins.themes.bootstrap3.article.details##
Keywords
Groundnut, Erstwhile Mahbubnagar, NDVI, Landsat, Regression equation
References
Abdelraouf, M.A., Mohamed, A.A., Mohamed, A.E. & Nasser, H.S. (2018). Comparative analysis of some winter crop area estimation using Landsat-8 and sentinel-2 satellite imagery. Asian Journal of Agriculture and Biology, 6 (2), 189-197.
Agriculture action plan. (2019-20). Department of Telangana. 1-328. www.agri.telangana.gov.in
Agriculture at a Glance. (2014). Government of India. Ministry of Agriculture. Department of Agriculture & Cooperation, Directorate of Economics & Statistics.
Andrew, R., Graeme, W. & Stuart, P. (2007). Remote Sensing Applications in Peanuts: The assessment of crop maturity, yield, disease, irrigation efficiency and best management practices using temporal images. University of Western Australia (eds.) - Proceedings of “5th Australian Controlled Traffic and Precision Agriculture Conference”, University of Western Australia, Perth, 16-18 July, 2007. University of Western Australia, 188-196.
A.K. Bhandari, A. Kumar. (2012). Feature Extraction using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City. Proceedings of “Communication, Computing & Security. Procedia Technology”, Volume 6, pp. 612– 621.
Ayyanna., Polisgowdar, B.S., Ayyanagowdar, M.S., Dandekar, A.T., Yadahalli, G.S. & Bellakki, M.A. (2018). Accuracy Assessment of Supervised and Unsupervised Classification using Landsat-8 Imagery of D-7 Shahapur Branch Canal of UKP Command Area Karnataka, India. International Journal of Current Microbiology and Applied Science, 7 (7), 205-216.
District census handbook – Mahabubnagar. (2011). Directorate of Census Operations, Andhra Pradesh. Series-29, 1-600.
Gumma, M.K., Kadiyala, M.D.M., Panjala, P., Ray, S.S., Akuraju, V.R., Dubey, S., Smith, A.P., Das, R. & Whitbread, A.M. (2021). Assimilation of remote sensing data into crop growth model for yield estimation: A case study from India. Journal of the Indian Society of Remote Sensing. DOI:10.1007/s12524-021-01341-6.
Hung, M.C., & Wu, Y.H. (2005). Mapping and visualizing the Great Salt Lake, landscape dynamics using multi-temporal satellite images, International Journal of Remote Sensing, 1972-1996.
Justice, C.O., Townshend, J.R.G., Vermata, E.F., Masuoka, E., Wolfe, R.E., Saleons, N., Ray, D.P. & Morisette, J.T. (2002). An overview of MODIS Land data processing and product status, Remote Sensing Environment, 83, 3-15.
Kingra, P.K., Majumder, D. & Singh, S.P. (2016). Application of remote sensing and GIS in agriculture and natural resource management under changing climatic conditions. Agricultural Research Journal, 53 (3), 295-302.
Longley, P.A., Goodchild, M.F., Maguire, D.J. & Rhind, D.W. (2005). Geographical information systems and science. 2nd edition of Manual of Geographical Information Systems, Chichester: John Wiley and sons, 1, 1-26.
Miles, J. & Shevlin, M. (2001). Applying regression & correlation: A guide for students and researchers. Sage Publications, London, United Kingdom.
Mukherjee, S. & Mukherjee, P. (2009). Assessment and composition of classification techniques for forest inventory estimation: A case study using IRS-1D imagery. International Journal of Geoinformatics, 5 (2), 63-73.
Nageswara, P.P.R., Shobha, S.V., Ramesh, K.S. & Somashekhar, R.K. (2005). Satellite -based assessment of Agricultural drought in Karnataka State. Journal of the Indian society of remote sensing, 33 (3), 429-434.
Rabi 2019-20, Pre-harvest price forecast for groundnut, 2020. Government of India. http://pjtsau.edu.in.
Season and Crop Coverage Report, Rabi. (2018-19). Commissionerate of Agriculture, Department of Agriculture, Government of Telangana, 1-16.
Sharafi, M.A. (2000). Crop inventory and production forecasting using remote sensing and agrometeorological models: the case of major agricultural commodities in Hamadan province, Iran. International Archives of Photogrammetry and Remote Sensing, XXXIII (B7), 1364-1372.
Sharma, N., Saxena, S., Dubey, S., Choudhary, K., Sehgal, S. & Ray, S.S. (2019). Analysis of sugarcane acreage and yield estimates derived from remote sensing data and other hybrid approaches under FASAL project. In the proceedings of “The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences”, ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18-20 February 2019, New Delhi, India, XLII-3/W6, 157-163.
Shruthi, G., Dayakar Rao, B., Latika Devi & Jolly Masih. 2017. Analysis of area, production and productivity of groundnut crop in Telangana. Agricultural Science Digest, 37 (2), 151-153.
Sreelekha, M. & Reddy, S.N. (2019). Accuracy Assessment of Supervised and Unsupervised Classification using NOAA Data in Andhra Pradesh Region. International Journal of Engineering Research & Technology, 8 (12), 60-64.
Watson, P.K. & Teelucksingh, S.S. (2002). A practical introduction to econometric methods: Classical and modern. The University of the West Indies Press, Barbados, Jamaica, West Indies, 1-323.
Zhe Ma., Zhe Liu1., Yuanyuan Zhao., Lin Zhang., Diyou Liu., Tianwei Ren., Xiaodong Zhang, & Shaoming Li. (2020). An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. International Journal of Geo-information, 9 (648), 1-24.
Zonal Annual Report, Southern Telangana Zone, (2017-18). Regional Agricultural Research Station, Palem. 5-6.
Agriculture action plan. (2019-20). Department of Telangana. 1-328. www.agri.telangana.gov.in
Agriculture at a Glance. (2014). Government of India. Ministry of Agriculture. Department of Agriculture & Cooperation, Directorate of Economics & Statistics.
Andrew, R., Graeme, W. & Stuart, P. (2007). Remote Sensing Applications in Peanuts: The assessment of crop maturity, yield, disease, irrigation efficiency and best management practices using temporal images. University of Western Australia (eds.) - Proceedings of “5th Australian Controlled Traffic and Precision Agriculture Conference”, University of Western Australia, Perth, 16-18 July, 2007. University of Western Australia, 188-196.
A.K. Bhandari, A. Kumar. (2012). Feature Extraction using Normalized Difference Vegetation Index (NDVI): A Case Study of Jabalpur City. Proceedings of “Communication, Computing & Security. Procedia Technology”, Volume 6, pp. 612– 621.
Ayyanna., Polisgowdar, B.S., Ayyanagowdar, M.S., Dandekar, A.T., Yadahalli, G.S. & Bellakki, M.A. (2018). Accuracy Assessment of Supervised and Unsupervised Classification using Landsat-8 Imagery of D-7 Shahapur Branch Canal of UKP Command Area Karnataka, India. International Journal of Current Microbiology and Applied Science, 7 (7), 205-216.
District census handbook – Mahabubnagar. (2011). Directorate of Census Operations, Andhra Pradesh. Series-29, 1-600.
Gumma, M.K., Kadiyala, M.D.M., Panjala, P., Ray, S.S., Akuraju, V.R., Dubey, S., Smith, A.P., Das, R. & Whitbread, A.M. (2021). Assimilation of remote sensing data into crop growth model for yield estimation: A case study from India. Journal of the Indian Society of Remote Sensing. DOI:10.1007/s12524-021-01341-6.
Hung, M.C., & Wu, Y.H. (2005). Mapping and visualizing the Great Salt Lake, landscape dynamics using multi-temporal satellite images, International Journal of Remote Sensing, 1972-1996.
Justice, C.O., Townshend, J.R.G., Vermata, E.F., Masuoka, E., Wolfe, R.E., Saleons, N., Ray, D.P. & Morisette, J.T. (2002). An overview of MODIS Land data processing and product status, Remote Sensing Environment, 83, 3-15.
Kingra, P.K., Majumder, D. & Singh, S.P. (2016). Application of remote sensing and GIS in agriculture and natural resource management under changing climatic conditions. Agricultural Research Journal, 53 (3), 295-302.
Longley, P.A., Goodchild, M.F., Maguire, D.J. & Rhind, D.W. (2005). Geographical information systems and science. 2nd edition of Manual of Geographical Information Systems, Chichester: John Wiley and sons, 1, 1-26.
Miles, J. & Shevlin, M. (2001). Applying regression & correlation: A guide for students and researchers. Sage Publications, London, United Kingdom.
Mukherjee, S. & Mukherjee, P. (2009). Assessment and composition of classification techniques for forest inventory estimation: A case study using IRS-1D imagery. International Journal of Geoinformatics, 5 (2), 63-73.
Nageswara, P.P.R., Shobha, S.V., Ramesh, K.S. & Somashekhar, R.K. (2005). Satellite -based assessment of Agricultural drought in Karnataka State. Journal of the Indian society of remote sensing, 33 (3), 429-434.
Rabi 2019-20, Pre-harvest price forecast for groundnut, 2020. Government of India. http://pjtsau.edu.in.
Season and Crop Coverage Report, Rabi. (2018-19). Commissionerate of Agriculture, Department of Agriculture, Government of Telangana, 1-16.
Sharafi, M.A. (2000). Crop inventory and production forecasting using remote sensing and agrometeorological models: the case of major agricultural commodities in Hamadan province, Iran. International Archives of Photogrammetry and Remote Sensing, XXXIII (B7), 1364-1372.
Sharma, N., Saxena, S., Dubey, S., Choudhary, K., Sehgal, S. & Ray, S.S. (2019). Analysis of sugarcane acreage and yield estimates derived from remote sensing data and other hybrid approaches under FASAL project. In the proceedings of “The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences”, ISPRS-GEOGLAM-ISRS Joint Int. Workshop on “Earth Observations for Agricultural Monitoring”, 18-20 February 2019, New Delhi, India, XLII-3/W6, 157-163.
Shruthi, G., Dayakar Rao, B., Latika Devi & Jolly Masih. 2017. Analysis of area, production and productivity of groundnut crop in Telangana. Agricultural Science Digest, 37 (2), 151-153.
Sreelekha, M. & Reddy, S.N. (2019). Accuracy Assessment of Supervised and Unsupervised Classification using NOAA Data in Andhra Pradesh Region. International Journal of Engineering Research & Technology, 8 (12), 60-64.
Watson, P.K. & Teelucksingh, S.S. (2002). A practical introduction to econometric methods: Classical and modern. The University of the West Indies Press, Barbados, Jamaica, West Indies, 1-323.
Zhe Ma., Zhe Liu1., Yuanyuan Zhao., Lin Zhang., Diyou Liu., Tianwei Ren., Xiaodong Zhang, & Shaoming Li. (2020). An Unsupervised Crop Classification Method Based on Principal Components Isometric Binning. International Journal of Geo-information, 9 (648), 1-24.
Zonal Annual Report, Southern Telangana Zone, (2017-18). Regional Agricultural Research Station, Palem. 5-6.
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
