Remote Sensing and Machine Learning techniques for acreage estimation of mango (Mangifera indica)
430 / 294
Authors
-
ASHIS RANJAN UDGATA
ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India
-
PRACHI MISRA SAHOO
ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India
-
TAUQUEER AHMAD
ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India
-
ANIL RAI
ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India
-
GOPAL KRISHNA
ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi 110 012, India
Keywords:
Artificial Neural network (ANN), Maximum Likelihood Classifier (MLC), Sentinel and Support Vector Machine (SVM)Abstract
Mango (Mangifera indica) is the most important commercially grown fruit crop in India. Uttar Pradesh, Andhra Pradesh, Karnataka, Bihar, Gujarat and Tamil Nadu are the major producers of mango. It covers around 42% total area accounting for 40% of total production in the country. Hence, development of reliable and timely estimates of area under mango at national level is essential for policymakers and planners for market planning and export. Earlier only survey technologies were used for area estimation which was a time consuming and laborious process. Modern space technology like remote sensing can be used as an alternative. Therefore, a study was carried out for acreage estimation of mango in West Godavari district of Andhra Pradesh using Sentinel 2 satellite data in the year 2017. Acreage estimation of mango was done after the preparation of land use and land cover map. Three supervised classification techniques, viz. Maximum Likelihood Classification (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were used for land use and land cover map preparation. Support Vector Machine using three different kernel functions, viz. Radial Basis Function (RBF), Sigmoid kernel and Polynomial kernel were used to improve the classification accuracy. SVMRBF was found to be the best classification technique with overall accuracy of 94.44 and kappa coefficient 0.9218. The mango area obtained from the classified satellite image using SVMRBF was 9372.96 ha.Downloads
References
Ahmad T, Sahoo P M and Jally S K. 2015. Estimation of area under agroforestry using high resolution satellite data. Agroforestry System 90(2): 289–303. DOI: https://doi.org/10.1007/s10457-015-9854-2
Atkinson P M and Tatnall A R L. 1997. Introduction neural networks in remote sensing. International Journal of Geographic Information Systems 18(4): 699–709. DOI: https://doi.org/10.1080/014311697218700
Dakshinamurti C, Krishnamurthy B, Summanwar A S, Shanta P and Pisharoty P R. 1971. Remote Sensing for Coconut Wilt. (In) Proceedings of International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, May 17-21, pp. 25-9.
Dadhwal V K. 1999. Remote sensing applications for agriculture: retrospective and perspective. (In) Proceedings of National Symposium on Remote Sensing Applications for Natural Resource and Perspective, Bengaluru, January 19-21, pp. 11-2.
Dixon B and Candade N. 2008. Multispectral land use classification using neural networks and support vector machines: one or the other, or both. International Journal of Remote Sensing 29(4):1185–1206. DOI: https://doi.org/10.1080/01431160701294661
Duda R and Hart P E. 1973. Pattern Classification and Scene Analysis. 2nd edn, pp. 45-55. John Wiley & Sons, New York.
GoswamiA K, Gakhar S and Kaur H. 2014. Automatic object recognition from satellite images using artificial neural network. International Journal of Computer Application 95(10): 33–9. DOI: https://doi.org/10.5120/16633-6502
Huang C, Townshend J R G, Liang S, Kalluri S N V and Defries R S. 2001. Impact of sensor’s point spread function on land cover characterization: assessment and deconvolution. Remote Sensing of Environment 80(2): 203–12. DOI: https://doi.org/10.1016/S0034-4257(01)00298-X
Huang C, Davis L S and Townshend J R G. 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing 23(4):725–49. DOI: https://doi.org/10.1080/01431160110040323
Jensen R R, Hardin P J and Yu G. 2009. Artificial neural networks and remote sensing. Geography Compass 3(2):630–46. DOI: https://doi.org/10.1111/j.1749-8198.2008.00215.x
Joachims T. 1998. Text categorization with support vector machines-learning with many relevant features. (In) Proceedings of European Conference on Machine Learning, Chemnitz, Germany, April 21-23, pp. 37–142. DOI: https://doi.org/10.1007/BFb0026683
Li G, Lu D, Moran E and Sant’Anna S J S. 2012. A Comparative Analysis of Classification Algorithms and Multiple Sensor Data for Land Use/Land Cover Classification in the Brazilian Amazon. Journal of Applied Remote Sensing 6(1): 0617061-23. DOI: https://doi.org/10.1117/1.JRS.6.061706
Lu D and Weng Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing 28(5):823–70. DOI: https://doi.org/10.1080/01431160600746456
Pal M. 2008.Ensemble of support vector machines for land cover classification. International Journal of Remote Sensing 29(10): 3043–9. DOI: https://doi.org/10.1080/01431160802007624
Pal M and Mather P M. 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing 26(5):1007–11. DOI: https://doi.org/10.1080/01431160512331314083
Patel N K, Ravi N, Navalgund R R, Dash R N, Das K C and Patnaik S. 1991. Estimation of rice yield using IRS 1A digital data in coastal tract of Orissa. International Journal of Remote Sensing 12(11): 2259–66. DOI: https://doi.org/10.1080/01431169108955256
Pradhan S, Bandyopadhyay K K, Sahoo R N, Sehgal V K, Singh Joshi D K and Gupta V K. 2013. Prediction of wheat (Triticum aestivum) grain and biomass yield under different irrigation and nitrogen management practices using canopy reflectance spectra model. Indian Journal of Agricultural Sciences 83(11): 1136–43.
Rosenfield G H. and Fitzpatrick-Lins K. 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering & Remote Sensing 52(2): 223–7.
Tripathi R, Sahoo, R N, Gupta V K, Sehgal V K and Sahoo P M. 2013. Retrieval of Leaf Area Index using IRS-P6, LISSIII data and validation of MODIS LAI product (MOD15 V5) over trans Gangetic Plains of India. Indian Journal Agicultural Sciences 83(4): 380–5.
Yadav I S, Rao N K S, Reddy B M C, Rawal R D, Srinivasan V R, Sujatha N T, Bhattacharya C, Rao P P N, Ramesh K S and Elango S. 2002. Acreage and production estimation of mango orchards using Indian Remote Sensing (IRS) satellite data. Scientia Horticulture 93(2):105–23. DOI: https://doi.org/10.1016/S0304-4238(01)00321-1
Yang C, Everitt J H and Murdenb D. 2011. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture 75(11): 347–54. DOI: https://doi.org/10.1016/j.compag.2010.12.012
Zhu G and Blumberg D G. 2002. Classification using ASTER data and SVM algorithms: The case study of Beer Sheva, Israel. Remote Sensing of Environment 80(2): 233–40. DOI: https://doi.org/10.1016/S0034-4257(01)00305-4
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2020 The Indian Journal of Agricultural Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.
How to Cite
