Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing

Francisco Javier López-Andreu,Juan Antonio López-Morales,Manuel Erena,Antonio F Skarmeta,Juan A Martínez

doi:10.3390/electronics11030325

Francisco Javier López-Andreu, Juan Antonio López-Morales + Show 3 more

Open Access

https://doi.org/10.3390/electronics11030325

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Abstract

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.

Highlights

The main objective of the new European Common Agricultural Policy (CAP) (EU Regulation No 1307/2013) is the development of favourable and sustainable actions to promote efficient agricultural practices
This section discusses in detail the metrics obtained by the random forest (RF) models, an analysis of how the selected features influenced the RF-generated models, the results of the quality assessment that was performed based on the technical guidelines issued by the Joint Research Centre (JRC), and a comparison of the results obtained by the different methods individually, without resorting to the traditional decision tree
Crop monitoring controls based on satellite imagery aim to perform a periodic and continuous check that verifies the activity declared by the farmer, observed through time series of images

Summary

Introduction

The main objective of the new European Common Agricultural Policy (CAP) (EU Regulation No 1307/2013) is the development of favourable and sustainable actions to promote efficient agricultural practices To this end, they must ensure food security, provide raw materials to the agri-food industries, and generate benefits for a sustainable and competitive agricultural sector [1]. Monitoring controls are a procedure of periodic and systematic observation of the earth, based mainly on satellite images These checks are preventive, and their objective is the regular and continuous verification of the compatibility between the agricultural activity declared by the farmer and that is observed in the time series through the use of satellite images [2]. The automation of processes tends to reduce the administrative burden and the cost of carrying out CAP aid controls, making the processing of applications and payments more agile

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