Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing
The European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/3/325 |
| _version_ | 1797488363508334592 |
|---|---|
| author | Francisco Javier López-Andreu Juan Antonio López-Morales Manuel Erena Antonio F. Skarmeta Juan A. Martínez |
| author_facet | Francisco Javier López-Andreu Juan Antonio López-Morales Manuel Erena Antonio F. Skarmeta Juan A. Martínez |
| author_sort | Francisco Javier López-Andreu |
| collection | DOAJ |
| description | The European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications continuously for an agricultural year. This paper presents a generic methodology developed for implementing monitoring controls. To achieve this, the dataset provided by the Sentinel-2 time series is transformed into information through the combination of classifications with machine learning using random forest and remote sensing-based biophysical indices. This work focuses on monitoring the helpline associated with rice cultivation, using 13 Sentinel-2 images whose grouping and characteristics change depending on the event or landmark being sought. Moreover, the functionality to check, before harvesting the crop, that the area declared is equal to the area cultivated is added. The 2020 results are around 96% for most of the metrics analysed, demonstrating the potential of Sentinel-2 for controlling subsidies, particularly for rice. After the quality assessment, the hit rate is 98%. The methodology is transformed into a tool for regular use to improve decision making by determining which declarants comply with the crop-specific aid obligations, contributing to optimising the administrations’ resources and a fairer distribution of funds. |
| first_indexed | 2024-03-10T00:02:05Z |
| format | Article |
| id | doaj.art-2629180929a64c95b0ca423e6cf8bf41 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T00:02:05Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2629180929a64c95b0ca423e6cf8bf412023-11-23T16:15:04ZengMDPI AGElectronics2079-92922022-01-0111332510.3390/electronics11030325Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote SensingFrancisco Javier López-Andreu0Juan Antonio López-Morales1Manuel Erena2Antonio F. Skarmeta3Juan A. Martínez4Institute of Agricultural and Environment Research and Development of Murcia-IMIDA, Mayor Street, La Alberca, 30150 Murcia, SpainInstitute of Agricultural and Environment Research and Development of Murcia-IMIDA, Mayor Street, La Alberca, 30150 Murcia, SpainInstitute of Agricultural and Environment Research and Development of Murcia-IMIDA, Mayor Street, La Alberca, 30150 Murcia, SpainDepartment of Information and Communications Engineering, Computer Science Faculty, University of Murcia, 30100 Murcia, SpainDepartment of Information and Communications Engineering, Computer Science Faculty, University of Murcia, 30100 Murcia, SpainThe European Commission promotes new technologies and data generated by the Copernicus Programme. These technologies are intended to improve the management of the Common Agricultural Policy aid, implement new monitoring controls to replace on-the-spot checks, and apply up to 100% of the applications continuously for an agricultural year. This paper presents a generic methodology developed for implementing monitoring controls. To achieve this, the dataset provided by the Sentinel-2 time series is transformed into information through the combination of classifications with machine learning using random forest and remote sensing-based biophysical indices. This work focuses on monitoring the helpline associated with rice cultivation, using 13 Sentinel-2 images whose grouping and characteristics change depending on the event or landmark being sought. Moreover, the functionality to check, before harvesting the crop, that the area declared is equal to the area cultivated is added. The 2020 results are around 96% for most of the metrics analysed, demonstrating the potential of Sentinel-2 for controlling subsidies, particularly for rice. After the quality assessment, the hit rate is 98%. The methodology is transformed into a tool for regular use to improve decision making by determining which declarants comply with the crop-specific aid obligations, contributing to optimising the administrations’ resources and a fairer distribution of funds.https://www.mdpi.com/2079-9292/11/3/325CopernicusSentinelcommon agricultural policymonitoringland userice crop |
| spellingShingle | Francisco Javier López-Andreu Juan Antonio López-Morales Manuel Erena Antonio F. Skarmeta Juan A. Martínez Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing Electronics Copernicus Sentinel common agricultural policy monitoring land use rice crop |
| title | Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing |
| title_full | Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing |
| title_fullStr | Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing |
| title_full_unstemmed | Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing |
| title_short | Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing |
| title_sort | monitoring system for the management of the common agricultural policy using machine learning and remote sensing |
| topic | Copernicus Sentinel common agricultural policy monitoring land use rice crop |
| url | https://www.mdpi.com/2079-9292/11/3/325 |
| work_keys_str_mv | AT franciscojavierlopezandreu monitoringsystemforthemanagementofthecommonagriculturalpolicyusingmachinelearningandremotesensing AT juanantoniolopezmorales monitoringsystemforthemanagementofthecommonagriculturalpolicyusingmachinelearningandremotesensing AT manuelerena monitoringsystemforthemanagementofthecommonagriculturalpolicyusingmachinelearningandremotesensing AT antoniofskarmeta monitoringsystemforthemanagementofthecommonagriculturalpolicyusingmachinelearningandremotesensing AT juanamartinez monitoringsystemforthemanagementofthecommonagriculturalpolicyusingmachinelearningandremotesensing |