Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929597896982528 |
|---|---|
| author | Kabir, Kazi Hasibul Aqib, Md. Zahiruddin Sultana, Sharmin Akhter, Shamim |
| author_facet | Kabir, Kazi Hasibul Aqib, Md. Zahiruddin Sultana, Sharmin Akhter, Shamim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12667 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis Kabir, Kazi Hasibul Aqib, Md. Zahiruddin Sultana, Sharmin Akhter, Shamim Machine Learning Computer Vision and Pattern Recognition 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. |
| title | Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.12667 |