Hybridized Clustering Algorithm and Ensemble Learning for Monitoring Paddy Crop Growth Analysis

Abstract

In food security control and agricultural management, crop monitoring is important to improve the crop yield rate and ensure the paddy growth. The paddy crop is monitored via remote sensing satellite images that cover the entire cultivating region. The satellite images are affected due to satellite movement and human errors, which causes over fitting issues. In addition, the remote images are facing difficulties while managing system robustness. The research issue is overcome by applying the Hybridized Clustering Algorithm-based Ensemble Learning Hybridized Clustering Algorithm with Ensemble Learning (HCA-EL) approach. Initially, the satellite images are handled to remove geometric, radiometric, and atmospheric corrections using pre-processing techniques. This process uses the Ground Control Points (GCP) and histogram process to remove unwanted information. Then vegetation index value data points are obtained to progress the overall crop monitoring. The data points are analyzed using spectral clustering based on the eigenvector and eigenvalue, minimizing the dimensionality issues. Finally, the crop monitoring criteria, such as soil quality and water levels are computed to improve paddy growth. The monitoring process is done with the help of the Ensemble Learning Extreme Networks (ELEN), which uses the voting criteria to identify the quality parameters. The HCA-EL approach-based crop monitoring process was implemented using the Python tool with respective performance metrics like Accuracy, Error Rate, MCC and F1-Score.