Crop cover identification based on different vegetation indices by using machine learning algorithms

Abstract

In this article, three different indices NDVI (Normalized Difference Vegetation Index), BNDVI (Blue Normalized Difference Vegetation Index) and GNDVI (Green Normalized Difference Vegetation Index) are used for the identification of wheat, mustard and sugarcane crop of Saharanpur district’s region of Uttar Pradesh. Sentinel 2B satellite images are collected from October 02, 2018 to April 15, 2019. These images are processed using Google Earth Engine. These sentinel images are used to generate NDVI, BNDVI and GNDVI images using GEE. These three different indices images are further processed using SNAP software and particular indices values for 210 different locations are calculated. The same process is used for calculating BNDVI and GNDVI values. ARIMA, LSTM and Prophet models are used to train the time series indices values (NDVI, BNDVI and GNDVI) of wheat, mustard and sugarcane crop. these models are used to analyse MSE (mean absolute percentage error) and RMSE values by considering various parameters. Using ARIMA Model, for wheat crop GNDVI indices shows minimum RMSE 0.020, For Sugarcane crop NDVI indices shows minimum RMSE 0.053, For Mustard crop GNDVI indices shows minimum RMSE 0.024. Using LSTM model, for wheat crop NDVI indices shows minimum RMSE 0.036, For Sugarcane crop BNDVI indices shows minimum RMSE 0.054, For Mustard crop GNDVI indices shows minimum RMSE 0.026. Using Prophet model, for wheat crop GNDVI indices shows minimum RMSE 0.055, For Sugarcane crop NDVI indices shows minimum RMSE 0.088, For Mustard crop GNDVI indices using Prophet model shows minimum RMSE 0.101.

Keywords

• NDVI
• GNDVI
• BNDVI
• LSTM
• ARIMA
• MSE

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