Summary of Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis, by Kazi Hasibul Kabir et al.
Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis
by Kazi Hasibul Kabir, Md. Zahiruddin Aqib, Sharmin Sultana, Shamim Akhter
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel approach for recognizing crop patterns using deep neural networks (DNNs), aiming to enhance large-scale monitoring of agricultural activities. The study leverages time-series remote sensing data, employing classification algorithms like support vector machines (SVMs) and decision trees as benchmarks. By integrating DNN-based classification with Naive Bayes and Random Forest approaches, the researchers aim to improve crop pattern recognition accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For farmers and food security enthusiasts, this study is about using advanced computer vision techniques to help monitor agricultural activities on a large scale. The goal is to better understand what crops are being grown where, when, and why. By analyzing satellite images over time, scientists can identify patterns in crop cultivation, which can inform decision-making for sustainable agriculture practices. |
Keywords
» Artificial intelligence » Classification » Naive bayes » Pattern recognition » Random forest » Time series
