Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning.
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an ex...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0311530 |
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| author | M Isabel Ramos Juan J Cubillas Ruth M Córdoba Lidia M Ortega |
| author_facet | M Isabel Ramos Juan J Cubillas Ruth M Córdoba Lidia M Ortega |
| author_sort | M Isabel Ramos |
| collection | DOAJ |
| description | In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help. Numerous predictive models infer accurate crop yield data from aerobiological variables and pollen analysis; this is around spring, in the middle stage of the crop year, when planning and investments are already done. The aim of this study is to anticipate accurate crop yield data at an early stage of the cropping season. In the case of olive groves in Spain, this period is in February. This work is developed for an entire province, Jaen, belonging to the region of Andalusia, in Southern Spain. The methodology uses Machine Learning algorithms together with an exhaustive analysis of predictor variables. Temporal data come from public web services, such as spatial data infrastructures of some state agencies. The processing of the satellite imagery is carried out by the geospatial processing service Google Earth Engine. The result is the early prediction of kilograms of both olive crop and olive oil, eight months prior to the beginning of the first harvesting campaigns of the year, with an average absolute error of prediction better than 26%. The relevance of this work is the early availability of predicted crop yield data together with the multi-scale applicability of the predictive models. This makes this model a useful tool for all the agents involved in olive grove management. From farmers, to agricultural technicians, researchers and scientists dedicated to the study of the olive tree, to governmental institutions and agricultural associations that provide technical support, advice and regulation to ensure responsible practices and the long-term viability of the olive industry. |
| format | Article |
| id | doaj-art-b33b43f9829a4dfca98edd0214ebce0d |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b33b43f9829a4dfca98edd0214ebce0d2025-02-05T05:31:24ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031153010.1371/journal.pone.0311530Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning.M Isabel RamosJuan J CubillasRuth M CórdobaLidia M OrtegaIn the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help. Numerous predictive models infer accurate crop yield data from aerobiological variables and pollen analysis; this is around spring, in the middle stage of the crop year, when planning and investments are already done. The aim of this study is to anticipate accurate crop yield data at an early stage of the cropping season. In the case of olive groves in Spain, this period is in February. This work is developed for an entire province, Jaen, belonging to the region of Andalusia, in Southern Spain. The methodology uses Machine Learning algorithms together with an exhaustive analysis of predictor variables. Temporal data come from public web services, such as spatial data infrastructures of some state agencies. The processing of the satellite imagery is carried out by the geospatial processing service Google Earth Engine. The result is the early prediction of kilograms of both olive crop and olive oil, eight months prior to the beginning of the first harvesting campaigns of the year, with an average absolute error of prediction better than 26%. The relevance of this work is the early availability of predicted crop yield data together with the multi-scale applicability of the predictive models. This makes this model a useful tool for all the agents involved in olive grove management. From farmers, to agricultural technicians, researchers and scientists dedicated to the study of the olive tree, to governmental institutions and agricultural associations that provide technical support, advice and regulation to ensure responsible practices and the long-term viability of the olive industry.https://doi.org/10.1371/journal.pone.0311530 |
| spellingShingle | M Isabel Ramos Juan J Cubillas Ruth M Córdoba Lidia M Ortega Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. PLoS ONE |
| title | Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. |
| title_full | Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. |
| title_fullStr | Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. |
| title_full_unstemmed | Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. |
| title_short | Improving early prediction of crop yield in Spanish olive groves using satellite imagery and machine learning. |
| title_sort | improving early prediction of crop yield in spanish olive groves using satellite imagery and machine learning |
| url | https://doi.org/10.1371/journal.pone.0311530 |
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