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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2502.04054 |
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| _version_ | 1866929700747608064 |
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| author | Banerjee, Sayan Mukherjee, Aniruddha Kamboj, Suket |
| author_facet | Banerjee, Sayan Mukherjee, Aniruddha Kamboj, Suket |
| contents | With the help of a digital twin structure, Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS (Global Positioning System) modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production through this concept. In addition to providing precise crop growth forecasts, the combination of real-time data on soil composition, meteorological dynamics, and geographic coordinates aims to support crop recommendation models and simulate predictive scenarios for improved water and pesticide management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04054 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Precision Agriculture Revolution: Integrating Digital Twins and Advanced Crop Recommendation for Optimal Yield Banerjee, Sayan Mukherjee, Aniruddha Kamboj, Suket Machine Learning With the help of a digital twin structure, Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS (Global Positioning System) modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production through this concept. In addition to providing precise crop growth forecasts, the combination of real-time data on soil composition, meteorological dynamics, and geographic coordinates aims to support crop recommendation models and simulate predictive scenarios for improved water and pesticide management. |
| title | Precision Agriculture Revolution: Integrating Digital Twins and Advanced Crop Recommendation for Optimal Yield |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.04054 |