Yield forecasting based on short time series with high spatial resolution data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917581427834880 |
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| author | Pokal, Sayli Zhou, Yuzhen Franz, Trenton |
| author_facet | Pokal, Sayli Zhou, Yuzhen Franz, Trenton |
| contents | Precision agriculture, also known as site-specific crop management, plays a crucial role in modern agriculture. Yield maps are an essential tool as they help identify the within-field variability that forms the basis of precision agriculture. If farmers could obtain yield maps for their specific site based on their field's soil and weather conditions, then site-specific crop management techniques would be more efficient and profitable. However, forecasting yield and producing reliable yield maps for an individual field can be challenging due to limited historical data. This paper proposes a novel two-stage approach for site-specific yield forecasting based on short-time series and high-resolution yield data. The proposed approach was successfully applied to predict yield maps at three different sites in Nebraska, demonstrating the method's ability to provide fine resolution and accurate yield maps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01946 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Yield forecasting based on short time series with high spatial resolution data Pokal, Sayli Zhou, Yuzhen Franz, Trenton Applications Precision agriculture, also known as site-specific crop management, plays a crucial role in modern agriculture. Yield maps are an essential tool as they help identify the within-field variability that forms the basis of precision agriculture. If farmers could obtain yield maps for their specific site based on their field's soil and weather conditions, then site-specific crop management techniques would be more efficient and profitable. However, forecasting yield and producing reliable yield maps for an individual field can be challenging due to limited historical data. This paper proposes a novel two-stage approach for site-specific yield forecasting based on short-time series and high-resolution yield data. The proposed approach was successfully applied to predict yield maps at three different sites in Nebraska, demonstrating the method's ability to provide fine resolution and accurate yield maps. |
| title | Yield forecasting based on short time series with high spatial resolution data |
| topic | Applications |
| url | https://arxiv.org/abs/2402.01946 |