Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916080870490112 |
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| author | Pathak, Deepak Miranda, Miro Mena, Francisco Sanchez, Cristhian Helber, Patrick Bischke, Benjamin Habelitz, Peter Najjar, Hiba Siddamsetty, Jayanth Arenas, Diego Vollmer, Michaela Charfuelan, Marcela Nuske, Marlon Dengel, Andreas |
| author_facet | Pathak, Deepak Miranda, Miro Mena, Francisco Sanchez, Cristhian Helber, Patrick Bischke, Benjamin Habelitz, Peter Najjar, Hiba Siddamsetty, Jayanth Arenas, Diego Vollmer, Michaela Charfuelan, Marcela Nuske, Marlon Dengel, Andreas |
| contents | We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and machine learning model agnostic methods at the sub-field level. We use Sentinel-2 satellite imagery as the primary modality for input data with other complementary modalities, including weather, soil, and DEM data. The proposed method uses input modalities available with global coverage, making the framework globally scalable. We explicitly highlight the importance of input modalities for crop yield prediction and emphasize that the best-performing combination of input modalities depends on region, crop, and chosen model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_08948 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level Pathak, Deepak Miranda, Miro Mena, Francisco Sanchez, Cristhian Helber, Patrick Bischke, Benjamin Habelitz, Peter Najjar, Hiba Siddamsetty, Jayanth Arenas, Diego Vollmer, Michaela Charfuelan, Marcela Nuske, Marlon Dengel, Andreas Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning ACM-class: J.2 We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and machine learning model agnostic methods at the sub-field level. We use Sentinel-2 satellite imagery as the primary modality for input data with other complementary modalities, including weather, soil, and DEM data. The proposed method uses input modalities available with global coverage, making the framework globally scalable. We explicitly highlight the importance of input modalities for crop yield prediction and emphasize that the best-performing combination of input modalities depends on region, crop, and chosen model. |
| title | Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning ACM-class: J.2 |
| url | https://arxiv.org/abs/2308.08948 |