Village level identification of sugarcane in Sangali, Maharashtra using open source data

Authors

  • AMAN LONARE Center for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology, Bombay (IITB), Mumbai, Maharashtra 400076, India
  • BASANT MAHESHWARI Western Sydney University, Sydney 2751, Australia
  • PENNAN CHINNASAMY Center for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology, Bombay (IITB), Mumbai, Maharashtra 400076, India

DOI:

https://doi.org/10.54386/jam.v24i3.1688

Keywords:

Sugarcane, Remote Sensing, Google Earth Engine, Machine Learning, Crop classification

Abstract

Agriculture and crop monitoring are very important for an agrarian country like India. This study is done in June Khed village in the Sangli district of Maharashtra, India to assessing the efficiency of an open source cloud-based remote sensing platform Google Earth Engine (GEE), in the village-scale identification of sugarcane. The ground-truth data was collected and the efficiency of Landsat-8 and Sentinel-2 satellite data was assessed in driving GEE’s inbuilt Machine Learning classifiers: Classification and Regression Tree (CART), Support Vector Machine (SVM) and Random Forest (RF). Results were validated with the ground truth data and the official data. Of the methods used, SVM outperformed RF and CART with the lowest relative deviation (+9.2%), highest F1- score (0.8) and overall accuracy (78%), using the Sentinel-2 data. Results also indicated the in-situ use of observation data with high spatio-temporal resolution data. The validated model was then up-scaled for the Walwa Taluka level, to map sugarcane area that can be used for further agriculture tasks such as crop monitoring and yield prediction, leading to better management of crop and better formulating of sugarcane farmer policy.

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Published

31-08-2022

How to Cite

LONARE, A. ., MAHESHWARI, B. ., & CHINNASAMY, P. . (2022). Village level identification of sugarcane in Sangali, Maharashtra using open source data. Journal of Agrometeorology, 24(3), 249–254. https://doi.org/10.54386/jam.v24i3.1688

Issue

Vol. 24 No. 3 (2022): September

Section

Research Paper

License

Copyright (c) 2022 AMAN LONARE, BASANT MAHAESHWARI , PENNAN CHINNASAMY

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