Exploring Machine Learning Approaches on Crop Pattern Recognition

Abstract

Agriculture activities monitoring is important to ensure food security. Remote sensing plays a signi cant role for large scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation of the cropping pattern. Classi cation algorithms are used to classify crop patterns and mapped agriculture land used. Some conventional classi cation methods including support vector machine (SVM) and decision trees were applied for crop pattern recognition. However, in this report, we are proposing di erent machine learning approaches such as Naive Bayes (NB), Deep Neural Network (DNN) and Random Forest (RF) classi cation to improve and nd a better solution for crop pattern recognition.