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

López-Andreu, Francisco Javier; López-Morales, Juan Antonio; Erena, Manuel; Skarmeta-Gómez, Antonio F.; Martı́nez, Juan Antonio

doi:10.3390/electronics11030325

Cited by 9 publications

(3 citation statements)

References 36 publications

(33 reference statements)

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“…The moisture content, temperature, and humidity from various locations can be monitored to quantify the thermal performance of roofs. IoT has been used in research and agriculture to maximize crop yield and facilitate early diagnosis, real-time monitoring, production efficiency, and the management of water resources [ 16 , 24 , 25 ]. A sensor-equipped system can monitor and record the change in the environmental parameters in real-time and enable roof operators to respond quickly in critical conditions [ 26 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Monitoring of a Productive Blue-Green Roof Using Low-Cost Sensors

Akhie,

Joksimovic

2023

Sensors

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Considering the rising concern over climate change and the need for local food security, productive blue-green roofs (PBGR) can be an effective solution to mitigate many relevant environmental issues. However, their cost of operation is high because they are intensive, and an economical operation and maintenance approach will render them as more viable alternative. Low-cost sensors with the Internet of Things can provide reliable solutions to the real-time management and distributed monitoring of such roofs through monitoring the plant as well soil conditions. This research assesses the extent to which a low-cost image sensor can be deployed to perform continuous, automated monitoring of a urban rooftop farm as a PBGR and evaluates the thermal performance of the roof for additional crops. An RGB-depth image sensor was used in this study to monitor crop growth. Images collected from weekly scans were processed by segmentation to estimate the plant heights of three crops species. The devised technique performed well for leafy and tall stem plants like okra, and the correlation between the estimated and observed growth characteristics was acceptable. For smaller plants, bright light and shadow considerably influenced the image quality, decreasing the precision. Six other crop species were monitored using a wireless sensor network to investigate how different crop varieties respond in terms of thermal performance. Celery, snow peas, and potato were measured with maximum daily cooling records, while beet and zucchini showed sound cooling effects in terms of mean daily cooling.

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Section: Literature Reviewmentioning

confidence: 99%

Monitoring of a Productive Blue-Green Roof Using Low-Cost Sensors

Akhie,

Joksimovic

2023

Sensors

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“…The ARD is then fundamental to the EO data cube, which integrates this data into one logical array multi-temporal thematic analysis. The DC input on machine learning [24,25] allows for generating crop modeling [26].…”

Section: Introductionmentioning

confidence: 99%

Protecting Steppe Birds by Monitoring with Sentinel Data and Machine Learning under the Common Agricultural Policy

López-Andreu¹,

Hernández-Guillen²,

Domínguez-Gómez³

et al. 2022

Agronomy

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This paper shows the work carried out to obtain a methodology capable of monitoring the Common Agricultural Policy (CAP) aid line for the protection of steppe birds, which aims to improve the feeding and breeding conditions of these species and contribute to the improvement of their overall biodiversity population. Two methodologies were initially defined, one based on remote sensing (BirdsEO) and the other on Machine Learning (BirdsML). Both use Sentinel-1 and Sentinel-2 data as a basis. BirdsEO encountered certain impediments caused by the land’s slope and the crop’s height. Finally, the methodology based on Machine Learning offered the best results. It evaluated the performance of up to 7 different Machine Learning classifiers, the most optimal being RandomForest. Fourteen different datasets were generated, and the results they offered were evaluated, the most optimal being the one with more than 150 features, including a time series of 8 elements with Sentinel-1, Sentinel-2 data and derived products, among others. The generated model provided values higher than 97% in metrics such as accuracy, recall and Area under the ROC Curve, and 95% in precision and recall. The methodology is transformed into a tool that continuously monitors 100% of the area requesting aid, continuously over time, which contributes positively to optimizing the use of administrative resources and a fairer distribution of CAP funds.

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“…The following is an explanation of the atomic tasks carried out in each of the elements of the ML Model Generator [60] element workflow, which can be seen in Figure 11: 1.…”

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confidence: 99%

Monitoring System of the Mar Menor Coastal Lagoon (Spain) and Its Watershed Basin Using the Integration of Massive Heterogeneous Data

López-Andreu

López-Morales

Juárez

et al. 2022

Sensors

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The tool created aims at the environmental monitoring of the Mar Menor coastal lagoon (Spain) and the monitoring of the land use of its watershed. It integrates heterogeneous data sources ranging from ecological data obtained from a multiparametric oceanographic sonde to agro-meteorological data from IMIDA’s network of stations or hydrological data from the SAIH network as multispectral satellite images from Sentinel and Landsat space missions. The system is based on free and open source software and has been designed to guarantee maximum levels of flexibility and scalability and minimum coupling so that the incorporation of new components does not affect the existing ones. The platform is designed to handle a data volume of more than 12 million records, experiencing exponential growth in the last six months. The tool allows the transformation of a large volume of data into information, offering them through microservices with optimal response times. As practical applications, the platform created allows us to know the ecological state of the Mar Menor with a very high level of detail, both at biophysical and nutrient levels, being able to detect periods of oxygen deficit and delimit the affected area. In addition, it facilitates the detailed monitoring of the cultivated areas of the watershed, detecting the agricultural use and crop cycles at the plot level. It also makes it possible to calculate the amount of water precipitated on the watershed and to monitor the runoff produced and the amount of water entering the Mar Menor in extreme events. The information is offered in different ways depending on the user profile, offering a very high level of detail for research or data analysis profiles, concrete and direct information to support decision-making for users with managerial profiles and validated and concise information for citizens. It is an integrated and distributed system that will provide data and services for the Mar Menor Observatory.

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

Cited by 9 publications

References 36 publications

Monitoring of a Productive Blue-Green Roof Using Low-Cost Sensors

Monitoring of a Productive Blue-Green Roof Using Low-Cost Sensors

Protecting Steppe Birds by Monitoring with Sentinel Data and Machine Learning under the Common Agricultural Policy

Monitoring System of the Mar Menor Coastal Lagoon (Spain) and Its Watershed Basin Using the Integration of Massive Heterogeneous Data

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