Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India
This is a processed dataset of approximately 20,000 near-surface remote sensing images acquired using inexpensive smartphones within the context of a picture-based insurance (PBI) initiative of 1,685 smallholder farmers ?elds in northwest India. Monitoring crop growth and disturbances is critical in...
| Main Authors: | , , |
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| Format: | Conjunto de datos |
| Language: | Inglés |
| Published: |
International Food Policy Research Institute
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/144539 |
| _version_ | 1855541992838135808 |
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| author | International Food Policy Research Institute Ghent University University of Manchester |
| author_browse | Ghent University International Food Policy Research Institute University of Manchester |
| author_facet | International Food Policy Research Institute Ghent University University of Manchester |
| author_sort | International Food Policy Research Institute |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This is a processed dataset of approximately 20,000 near-surface remote sensing images acquired using inexpensive smartphones within the context of a picture-based insurance (PBI) initiative of 1,685 smallholder farmers ?elds in northwest India. Monitoring crop growth and disturbances is critical in strengthening farmers’ ability to manage production risks. The data presented monitors winter wheat growth and includes meta-data, either manually or automatically derived, to quantify 5 crop greenness, phenology and damage events as well as management practices. Our dataset offers granular visual ?eld data, with processed images and detailed meta-data that provide information on the timing of key developmental phases of winter wheat and crop growth disturbances which are not registered by common satellite remote sensing vegetation indices or national crop cut surveys. The purpose of this high-resolution dataset is to provide a rich source of inputs in supporting of crop modeling and production risk assessment in support of food security in smallholder agricultural systems. We, therefore, foresee that these data will ?nd applications in crop modeling, remote sensing validation and machine learning-based crop assessment. |
| format | Conjunto de datos |
| id | CGSpace144539 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1445392025-12-08T10:11:39Z Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India International Food Policy Research Institute Ghent University University of Manchester insurance remote sensing winter wheat machine learning This is a processed dataset of approximately 20,000 near-surface remote sensing images acquired using inexpensive smartphones within the context of a picture-based insurance (PBI) initiative of 1,685 smallholder farmers ?elds in northwest India. Monitoring crop growth and disturbances is critical in strengthening farmers’ ability to manage production risks. The data presented monitors winter wheat growth and includes meta-data, either manually or automatically derived, to quantify 5 crop greenness, phenology and damage events as well as management practices. Our dataset offers granular visual ?eld data, with processed images and detailed meta-data that provide information on the timing of key developmental phases of winter wheat and crop growth disturbances which are not registered by common satellite remote sensing vegetation indices or national crop cut surveys. The purpose of this high-resolution dataset is to provide a rich source of inputs in supporting of crop modeling and production risk assessment in support of food security in smallholder agricultural systems. We, therefore, foresee that these data will ?nd applications in crop modeling, remote sensing validation and machine learning-based crop assessment. 2020 2024-06-04T09:44:15Z 2024-06-04T09:44:15Z Dataset https://hdl.handle.net/10568/144539 en https://doi.org/10.1016/j.agrformet.2018.11.002 Open Access International Food Policy Research Institute International Food Policy Research Institute; Ghent University; University of Manchester. 2020. Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India. Washington, DC: International Food Policy Research Institute. https://doi.org/10.7910/DVN/DBAFZY. Harvard Dataverse. Version 1. |
| spellingShingle | insurance remote sensing winter wheat machine learning International Food Policy Research Institute Ghent University University of Manchester Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India |
| title | Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India |
| title_full | Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India |
| title_fullStr | Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India |
| title_full_unstemmed | Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India |
| title_short | Crop Monitoring Using Smartphone Based Near-Surface Remote Sensing: Ground Pictures of Wheat and Auxiliary Data from Northern India |
| title_sort | crop monitoring using smartphone based near surface remote sensing ground pictures of wheat and auxiliary data from northern india |
| topic | insurance remote sensing winter wheat machine learning |
| url | https://hdl.handle.net/10568/144539 |
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