Agricultural analysis and crop yield prediction of Habiganj using multispectral bands of satellite imagery with machine learning

Abstract

Bangladesh is predominately an agriculture-based country, which faces uncertain crop yields and inefficient farming infrastructure resulting in adverse effect in food security. Habiganj is selected as the study area because of its vulnerability to floods and drought due to its unique terrain. This paper aims to present a combinational agricultural mapping and monitoring of Habiganj with crop growth and yield prediction. Multi-spectral band images of Habiganj from Landsat 8 are processed and remote sensing indices are extracted. With options of K-means and Mask R-CNN methods, crop growth is evaluated using both Python and MATLAB. Then using two type of machine learning algorithms crop yield of Habiganj is predicted from its existing parameters and the datasets are predicted by using two type of time series model. Furthermore, comparative studies are concluded between two platforms and time series model to determine the most suited environment for this research purpose.