Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis

2024/11/19 by Kabir, Kazi Hasibul, Md. Zahiruddin Aqib, Sharmin Sultana +4
Agricultural and Biological Sciences · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Agriculture and AI

paper · pdf · doi:10.48550/arxiv.2411.12667

openalex publication_date 2024/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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