Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis
This work addresses agricultural monitoring for food security, but it is incremental as it applies existing methods to a specific domain without a clear breakthrough.
The paper tackled crop pattern recognition from remote sensing data by proposing a Deep Neural Network (DNN) classification method and comparing it with Naive Bayes and Random Forest, aiming to improve performance.
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.