A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities
Aquaculture is a globally widespread practice and the world’s fastest-growing food sector and requires technological advances to both increase productivity and minimize environmental impacts. Monitoring the sector is one of the priorities of state governments, international organizations, such as th...
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MDPI AG
2025-01-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/1/11 |
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| author | Maxim Veroli Marco Martinoli Arianna Martini Riccardo Napolitano Domitilla Pulcini Nicolò Tonachella Fabrizio Capoccioni |
| author_facet | Maxim Veroli Marco Martinoli Arianna Martini Riccardo Napolitano Domitilla Pulcini Nicolò Tonachella Fabrizio Capoccioni |
| author_sort | Maxim Veroli |
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| description | Aquaculture is a globally widespread practice and the world’s fastest-growing food sector and requires technological advances to both increase productivity and minimize environmental impacts. Monitoring the sector is one of the priorities of state governments, international organizations, such as the Food and Agriculture Organization of the United States (FAO), and the European Commission. Data collection in aquaculture, particularly information on the location, number, and size of production facilities, is challenging due to the time required, the extent of the area to be monitored, the frequent changes in farming infrastructures and licenses, and the lack of automated tools. Such information is usually obtained through direct communications (e.g., phone calls and e-mails) with aquaculture producers and is rarely confirmed with on-site measurements. This study describes an innovative and automated method to obtain data on the number and placement of structures for marine and freshwater finfish farming through a YOLOv4 model trained on high-resolution images. High-resolution images were extracted from Google Maps to test their use with the YOLO model for the identification and geolocation of both land (raceways used in salmonids farming) and sea-based (floating sea cages used in seabream, seabass, and meagre farming) aquaculture systems in Italy. An overall accuracy of approximately 85% of correct object recognition of the target class was achieved. Model accuracy was tested with a dataset that includes images from Tuscany (Italy), where all these farm typologies are represented. The results demonstrate that the approach proposed can identify, characterize, and geolocate sea- and land-based aquaculture structures without performing any post-processing procedure, by directly applying customized deep learning and artificial intelligence algorithms. |
| format | Article |
| id | doaj-art-d224777dd95e427eaf00ad0981515998 |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-d224777dd95e427eaf00ad09815159982025-01-24T13:16:14ZengMDPI AGAgriEngineering2624-74022025-01-01711110.3390/agriengineering7010011A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture FacilitiesMaxim Veroli0Marco Martinoli1Arianna Martini2Riccardo Napolitano3Domitilla Pulcini4Nicolò Tonachella5Fabrizio Capoccioni6Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro Ricerca “Zootecnia e Acquacoltura”, Via Salaria 31, 00015 Monterotondo, ItalyConsiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro Ricerca “Zootecnia e Acquacoltura”, Via Salaria 31, 00015 Monterotondo, ItalyConsiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro Ricerca “Zootecnia e Acquacoltura”, Via Salaria 31, 00015 Monterotondo, ItalyConsiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro Ricerca “Zootecnia e Acquacoltura”, Via Salaria 31, 00015 Monterotondo, ItalyConsiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro Ricerca “Zootecnia e Acquacoltura”, Via Salaria 31, 00015 Monterotondo, ItalyConsiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro Ricerca “Zootecnia e Acquacoltura”, Via Salaria 31, 00015 Monterotondo, ItalyConsiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Centro Ricerca “Zootecnia e Acquacoltura”, Via Salaria 31, 00015 Monterotondo, ItalyAquaculture is a globally widespread practice and the world’s fastest-growing food sector and requires technological advances to both increase productivity and minimize environmental impacts. Monitoring the sector is one of the priorities of state governments, international organizations, such as the Food and Agriculture Organization of the United States (FAO), and the European Commission. Data collection in aquaculture, particularly information on the location, number, and size of production facilities, is challenging due to the time required, the extent of the area to be monitored, the frequent changes in farming infrastructures and licenses, and the lack of automated tools. Such information is usually obtained through direct communications (e.g., phone calls and e-mails) with aquaculture producers and is rarely confirmed with on-site measurements. This study describes an innovative and automated method to obtain data on the number and placement of structures for marine and freshwater finfish farming through a YOLOv4 model trained on high-resolution images. High-resolution images were extracted from Google Maps to test their use with the YOLO model for the identification and geolocation of both land (raceways used in salmonids farming) and sea-based (floating sea cages used in seabream, seabass, and meagre farming) aquaculture systems in Italy. An overall accuracy of approximately 85% of correct object recognition of the target class was achieved. Model accuracy was tested with a dataset that includes images from Tuscany (Italy), where all these farm typologies are represented. The results demonstrate that the approach proposed can identify, characterize, and geolocate sea- and land-based aquaculture structures without performing any post-processing procedure, by directly applying customized deep learning and artificial intelligence algorithms.https://www.mdpi.com/2624-7402/7/1/11AIgeolocationfloating sea cageraceway |
| spellingShingle | Maxim Veroli Marco Martinoli Arianna Martini Riccardo Napolitano Domitilla Pulcini Nicolò Tonachella Fabrizio Capoccioni A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities AgriEngineering AI geolocation floating sea cage raceway |
| title | A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities |
| title_full | A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities |
| title_fullStr | A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities |
| title_full_unstemmed | A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities |
| title_short | A Novel and Automated Approach to Detect Sea- and Land-Based Aquaculture Facilities |
| title_sort | novel and automated approach to detect sea and land based aquaculture facilities |
| topic | AI geolocation floating sea cage raceway |
| url | https://www.mdpi.com/2624-7402/7/1/11 |
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