A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest
The study found that dynamic crop models have the accuracy to predict normal to high yields, but there are limits to their ability to capture low yields. On the other hand, the machine learning (CNN) model has better ability to capture lower yields. It is worth noting that the crop model only took i...
| Main Authors: | , , , |
|---|---|
| Format: | Informe técnico |
| Language: | Inglés |
| Published: |
International Food Policy Research Institute
2019
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/146798 |
| _version_ | 1855538715255898112 |
|---|---|
| author | Singh, B. K. Chakraborty, Dulal Kalra, Naveen Singh, Jaya |
| author_browse | Chakraborty, Dulal Kalra, Naveen Singh, B. K. Singh, Jaya |
| author_facet | Singh, B. K. Chakraborty, Dulal Kalra, Naveen Singh, Jaya |
| author_sort | Singh, B. K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The study found that dynamic crop models have the accuracy to predict normal to high yields, but there are limits to their ability to capture low yields. On the other hand, the machine learning (CNN) model has better ability to capture lower yields. It is worth noting that the crop model only took into consideration mainly the weather data to predict yields; it is handicapped by the paucity of detailed management information deployed by farmers. However, the pictures sent by farmers reflected more yield-determining characteristics that reflected crop health and yield and that were then captured by the CNN. Finally, among the picture characteristics parameters, if “GCC & H” correlations are high, this could be a good indicator of low yield. |
| format | Informe técnico |
| id | CGSpace146798 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1467982025-11-06T07:31:12Z A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest Singh, B. K. Chakraborty, Dulal Kalra, Naveen Singh, Jaya insurance crop insurance resilience finance climate change The study found that dynamic crop models have the accuracy to predict normal to high yields, but there are limits to their ability to capture low yields. On the other hand, the machine learning (CNN) model has better ability to capture lower yields. It is worth noting that the crop model only took into consideration mainly the weather data to predict yields; it is handicapped by the paucity of detailed management information deployed by farmers. However, the pictures sent by farmers reflected more yield-determining characteristics that reflected crop health and yield and that were then captured by the CNN. Finally, among the picture characteristics parameters, if “GCC & H” correlations are high, this could be a good indicator of low yield. 2019-01-08 2024-06-21T09:08:47Z 2024-06-21T09:08:47Z Report https://hdl.handle.net/10568/146798 en Open Access application/pdf International Food Policy Research Institute Singh, B. K.; Chakraborty, Dulal; Kalra, Naveen; and Singh, Jaya. 2018. A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest. Washington, DC: International Food Policy Research Institute (IFPRI). https://hdl.handle.net/10568/146798 |
| spellingShingle | insurance crop insurance resilience finance climate change Singh, B. K. Chakraborty, Dulal Kalra, Naveen Singh, Jaya A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest |
| title | A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest |
| title_full | A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest |
| title_fullStr | A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest |
| title_full_unstemmed | A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest |
| title_short | A tool for climate smart crop insurance: Combining farmers’ pictures with dynamic crop modelling for accurate yield estimation prior to harvest |
| title_sort | tool for climate smart crop insurance combining farmers pictures with dynamic crop modelling for accurate yield estimation prior to harvest |
| topic | insurance crop insurance resilience finance climate change |
| url | https://hdl.handle.net/10568/146798 |
| work_keys_str_mv | AT singhbk atoolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest AT chakrabortydulal atoolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest AT kalranaveen atoolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest AT singhjaya atoolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest AT singhbk toolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest AT chakrabortydulal toolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest AT kalranaveen toolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest AT singhjaya toolforclimatesmartcropinsurancecombiningfarmerspictureswithdynamiccropmodellingforaccurateyieldestimationpriortoharvest |