Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis

TL;DR

This paper compares traditional machine learning methods with a deep neural network approach for crop pattern recognition using remote sensing data, aiming to improve classification accuracy in agricultural monitoring.

Contribution

It introduces a DNN-based classification method and provides a comparative analysis against Naive Bayes and Random Forest for crop pattern recognition.

Findings

DNN outperforms Naive Bayes and Random Forest in accuracy.

Deep learning enhances crop pattern classification performance.

The study demonstrates the effectiveness of DNN in remote sensing applications.

Abstract

Monitoring agricultural activities is important to ensure food security. Remote sensing plays a significant role for large-scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation of the cropping pattern. Classification algorithms are used to classify crop patterns and mapped agriculture land used. Some conventional classification methods including support vector machine (SVM) and decision trees were applied for crop pattern recognition. However, in this paper, we are proposing Deep Neural Network (DNN) based classification to improve the performance of crop pattern recognition and make a comparative analysis with two (2) other machine learning approaches including Naive Bayes and Random Forest.