Naïve Bayes and Random Forest for Crop Yield Prediction
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913327472443392 |
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| author | Maazallahi, Abbas Thota, Sreehari Kondaboina, Naga Prasad Muktineni, Vineetha Annem, Deepthi Rokkam, Abhi Stephen Amini, Mohammad Hossein Salari, Mohammad Amir Norouzzadeh, Payam Snir, Eli Rahmani, Bahareh |
| author_facet | Maazallahi, Abbas Thota, Sreehari Kondaboina, Naga Prasad Muktineni, Vineetha Annem, Deepthi Rokkam, Abhi Stephen Amini, Mohammad Hossein Salari, Mohammad Amir Norouzzadeh, Payam Snir, Eli Rahmani, Bahareh |
| contents | This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Naïve Bayes, K-Mean Clustering, and Random Forest. The models, particularly Naïve Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Naïve Bayes and Random Forest for Crop Yield Prediction Maazallahi, Abbas Thota, Sreehari Kondaboina, Naga Prasad Muktineni, Vineetha Annem, Deepthi Rokkam, Abhi Stephen Amini, Mohammad Hossein Salari, Mohammad Amir Norouzzadeh, Payam Snir, Eli Rahmani, Bahareh Machine Learning Artificial Intelligence This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Naïve Bayes, K-Mean Clustering, and Random Forest. The models, particularly Naïve Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science. |
| title | Naïve Bayes and Random Forest for Crop Yield Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.15392 |