This paper summarizes the development of a weed monitoring system in the Parco archeologico del Colosseo (hereinafter, Parco) using Deep Learning (DL) techniques to recognize forty-one species of plants now present in the area. The project is part of SyPEAH (System for the Protection and Education of Archaeological Heritage), a platform designed to safeguard the Parco by its Authority. This study emanates from an extended phase of the photographic collection spanning ten months. This endeavour facilitated the compilation of a dataset comprising nearly 5,000 photographs depicting the flora of pertinent significance. In the paper, we detail the first version of the system, consisting of a neural network trained to predict the species of plants and the materials on which they grow. We also describe transfer learning techniques aimed at improving performance. The present system attains recognition accuracy exceeding 90% for common species, enabling near real-time monitoring of the entire Park’s flora through image analysis using supplied fixed and mobile devices. It will support proactive interventions for maintenance. The paper details data analysis and neural network design and envisions future developments.

ArcheoWeedNet: Weed Classification in the Parco archeologico del Colosseo / Saurio, G.; Muscas, M.; Spinelli, I.; Rughetti, V.; Della Giovampaola, I.; Scardapane, S.. - 14365:(2024), pp. 430-441. (Intervento presentato al convegno Proceedings of the 22nd International Conference on Image Analysis and Processing, ICIAP 2023 tenutosi a Udine; Italia) [10.1007/978-3-031-51023-6_36].

ArcheoWeedNet: Weed Classification in the Parco archeologico del Colosseo

Saurio G.
;
Muscas M.;Spinelli I.;Rughetti V.;Scardapane S.
2024

Abstract

This paper summarizes the development of a weed monitoring system in the Parco archeologico del Colosseo (hereinafter, Parco) using Deep Learning (DL) techniques to recognize forty-one species of plants now present in the area. The project is part of SyPEAH (System for the Protection and Education of Archaeological Heritage), a platform designed to safeguard the Parco by its Authority. This study emanates from an extended phase of the photographic collection spanning ten months. This endeavour facilitated the compilation of a dataset comprising nearly 5,000 photographs depicting the flora of pertinent significance. In the paper, we detail the first version of the system, consisting of a neural network trained to predict the species of plants and the materials on which they grow. We also describe transfer learning techniques aimed at improving performance. The present system attains recognition accuracy exceeding 90% for common species, enabling near real-time monitoring of the entire Park’s flora through image analysis using supplied fixed and mobile devices. It will support proactive interventions for maintenance. The paper details data analysis and neural network design and envisions future developments.
2024
Proceedings of the 22nd International Conference on Image Analysis and Processing, ICIAP 2023
archaeological site conservation; CNN; deep Learning; PlantNet; weed classification
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
ArcheoWeedNet: Weed Classification in the Parco archeologico del Colosseo / Saurio, G.; Muscas, M.; Spinelli, I.; Rughetti, V.; Della Giovampaola, I.; Scardapane, S.. - 14365:(2024), pp. 430-441. (Intervento presentato al convegno Proceedings of the 22nd International Conference on Image Analysis and Processing, ICIAP 2023 tenutosi a Udine; Italia) [10.1007/978-3-031-51023-6_36].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1702063
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