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Remote Sensing and Machine Learning techniques for acreage estimation of mango (Mangifera indica)

Indian Agricultural Research Journals

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Title Remote Sensing and Machine Learning techniques for acreage estimation of mango (Mangifera indica)
 
Creator UDGATA, ASHIS RANJAN
SAHOO*, PRACHI MISRA
AHMAD, TAUQUEER
RAI, ANIL
KRISHNA, GOPAL
 
Subject Artificial Neural network (ANN), Maximum Likelihood Classifier (MLC), Sentinel and Support Vector Machine (SVM)
 
Description 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.
 
Publisher Indian Council of Agricutural Research
 
Contributor —
 
Date 2020-06-22
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/101473
 
Source The Indian Journal of Agricultural Sciences; Vol 90, No 3 (2020)
0019-5022
 
Language eng
 
Relation http://epubs.icar.org.in/ejournal/index.php/IJAgS/article/view/101473/39846
 
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