2025
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Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU Course # 4
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
2025
Other
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Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU Course # 3
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
2025
Other
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Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU Course # 6
Martinelli M.
Computer Vision & Applications in Agriculture Basic Techniques & Advanced ApplicationsProject(s): Agritech
Martinelli M.
Computer Vision & Applications in Agriculture Basic Techniques & Advanced ApplicationsProject(s): Agritech
2025
Other
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TiAssisto - Soluzioni per il monitoraggio clinico di pazienti in isolamento fiduciario a domicilio positivi al test per Covid-19 con associate o meno patologie croniche e situazioni di fragilità
Pratali L., Tomei A., Martinelli M.
Il poster illustra i principali obiettivi del progetto e i risultati ottenutiProject(s): TiAssisto
Pratali L., Tomei A., Martinelli M.
Il poster illustra i principali obiettivi del progetto e i risultati ottenutiProject(s): TiAssisto
2025
Other
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Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU Course # 5
Martinelli M.
Computer Vision & Applications in Agriculture - Basic Techniques & Advanced ApplicationsProject(s): Agritech
Martinelli M.
Computer Vision & Applications in Agriculture - Basic Techniques & Advanced ApplicationsProject(s): Agritech
2025
Conference article
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Improving weed control efficiency in maize fields: a methodological approach to site-specific weed management
Ercolini L., Grossi N., Martinelli M., Moroni D., Berton A., Silvestri N.
Methodologies to enhance precision agriculture.
Ercolini L., Grossi N., Martinelli M., Moroni D., Berton A., Silvestri N.
Methodologies to enhance precision agriculture.
See at:
ecpa2025.upc.edu 
| CNR IRIS | CNR IRIS
2025
Other
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Aggiornamento 2/25 Progetto Barilla Agrosat+
Martinelli M., Moroni D.
Aggiornamento modelliProject(s): Barilla Agrosat+
Martinelli M., Moroni D.
Aggiornamento modelliProject(s): Barilla Agrosat+
2025
Contribution to conference
Restricted
Corn Regional Optimized Weed Decisions (CROWD): a tool for a site-specific weed management
Ercolinia L., Grossia N., Berton A., Martinelli M., Moroni D., Silvestri N.
Weed management (WM) remains a primary challenge in contemporary agriculture, particularly within the European Union's Farm to Fork strategy, which aims to reduce pesticide usage by 50% by 2030 while maintaining high crop productivity. In order to achieve this objective, innovative and sustainable approaches are required; among these, Site-Specific Weed Management (SSWM) is regarded as a promising solution. SSWM employs precision agriculture technologies, including remote sensing and artificial intelligence, to optimise herbicide application by targeting only weed-infested areas. The methodology is comprised of three fundamental phases: i) Weed Detection (WD), ii) estimation of potential crop yield loss due to weeds, and iii) precision herbicide application using ISOBUS sprayers. Despite the strides made, the adoption of SSWM is impeded by the substantial costs associated with technology, its intricate nature, and its incompatibility with less digitised farming systems. This study proposes a cost-effective and rapid-deployment Decision Support System (DSS) for maize cultivation that requires minimal calibration. Building on a previously validated method, the system estimates Weed Green Cover (WGC) using RGB drone imagery by subtracting Maize Green Cover (MGC) from Total Green Cover (TGC). The economic intervention threshold, expressed as tolerable WGC, is used to define a Green Damage Threshold (GDT) for each MZ. This enables timely and targeted weeding interventions based on image-derived metrics, offering a scalable solution for sustainable WM in diverse agricultural contexts.
Ercolinia L., Grossia N., Berton A., Martinelli M., Moroni D., Silvestri N.
Weed management (WM) remains a primary challenge in contemporary agriculture, particularly within the European Union's Farm to Fork strategy, which aims to reduce pesticide usage by 50% by 2030 while maintaining high crop productivity. In order to achieve this objective, innovative and sustainable approaches are required; among these, Site-Specific Weed Management (SSWM) is regarded as a promising solution. SSWM employs precision agriculture technologies, including remote sensing and artificial intelligence, to optimise herbicide application by targeting only weed-infested areas. The methodology is comprised of three fundamental phases: i) Weed Detection (WD), ii) estimation of potential crop yield loss due to weeds, and iii) precision herbicide application using ISOBUS sprayers. Despite the strides made, the adoption of SSWM is impeded by the substantial costs associated with technology, its intricate nature, and its incompatibility with less digitised farming systems. This study proposes a cost-effective and rapid-deployment Decision Support System (DSS) for maize cultivation that requires minimal calibration. Building on a previously validated method, the system estimates Weed Green Cover (WGC) using RGB drone imagery by subtracting Maize Green Cover (MGC) from Total Green Cover (TGC). The economic intervention threshold, expressed as tolerable WGC, is used to define a Green Damage Threshold (GDT) for each MZ. This enables timely and targeted weeding interventions based on image-derived metrics, offering a scalable solution for sustainable WM in diverse agricultural contexts.
2025
Other
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Contents of the digital agriculture for sustainable development. MASTER AGRITECH EU Course # 2 / 2025
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
2025
Other
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Computer Vision & Applications in Agriculture - Basic Techniques & Advanced Applications # 1
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
Martinelli M.
Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU CourseProject(s): Agritech
2025
Other
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Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU Course # 7
Martinelli M.
Computer Vision & Applications in Agriculture. Basic Techniques & Advanced ApplicationsProject(s): Agritech
Martinelli M.
Computer Vision & Applications in Agriculture. Basic Techniques & Advanced ApplicationsProject(s): Agritech
2025
Other
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Aggiornamento 1/25 Progetto Barilla Agrosat+
Martinelli M., Moroni D., Dainelli R., Toscano P.
Aggiornamento modelliProject(s): Barilla Agrosat+
Martinelli M., Moroni D., Dainelli R., Toscano P.
Aggiornamento modelliProject(s): Barilla Agrosat+
2025
Other
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Contents of the Digital agriculture for sustainable development. MASTER AGRITECH EU Course # 8
Martinelli M.
Computer Vision & Applications in Agriculture. Basic Techniques & Advanced ApplicationsProject(s): Agritech
Martinelli M.
Computer Vision & Applications in Agriculture. Basic Techniques & Advanced ApplicationsProject(s): Agritech
2025
Conference article
Restricted
You’ve got the wrong outfit: evaluating deep learning paradigms on digit and fashion recognition
Ignesti G., Martinelli M., Moroni D.
To build more accurate and trustworthy artificial intelligence algorithms in deep learning, it is essential to understand the mechanisms driving classification systems to identify their targets. Typically, post hoc methods are used to provide insights into this process. By contrast, in this work, we investigate the possibility of using class activation maps in combination with contrastive loss to enhance the reliability of train- ing of a deep learning model. MNIST and Fashion MNIST datasets are considered in our investigation since they have already proven a prac- tical starting point for assessing an almost tautologic training strategy for deep learning algorithms, given that classification targets are the pri- mary significant content of the images in these datasets. Starting from the raw comparison of accuracy and system complexity of the proposed approach, a further investigation of the technique’s feasibility in a deep learning study is conducted over six random seed splits of the training data and model performance. A modern deep learning network, such as ConvNeXT, determines whether a more robust architecture trained with the proposed mechanics provides better insights than a simple convolu- tional neural network. This investigation also addresses the importance of skip connections, structured learning layers, and feature map dimen- sions in the learning process.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 202-215. Kolkata, India, 01/12/2024
DOI: 10.1007/978-3-031-87663-9
Metrics:
Ignesti G., Martinelli M., Moroni D.
To build more accurate and trustworthy artificial intelligence algorithms in deep learning, it is essential to understand the mechanisms driving classification systems to identify their targets. Typically, post hoc methods are used to provide insights into this process. By contrast, in this work, we investigate the possibility of using class activation maps in combination with contrastive loss to enhance the reliability of train- ing of a deep learning model. MNIST and Fashion MNIST datasets are considered in our investigation since they have already proven a prac- tical starting point for assessing an almost tautologic training strategy for deep learning algorithms, given that classification targets are the pri- mary significant content of the images in these datasets. Starting from the raw comparison of accuracy and system complexity of the proposed approach, a further investigation of the technique’s feasibility in a deep learning study is conducted over six random seed splits of the training data and model performance. A modern deep learning network, such as ConvNeXT, determines whether a more robust architecture trained with the proposed mechanics provides better insights than a simple convolu- tional neural network. This investigation also addresses the importance of skip connections, structured learning layers, and feature map dimen- sions in the learning process.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 202-215. Kolkata, India, 01/12/2024
DOI: 10.1007/978-3-031-87663-9
Metrics:
See at:
CNR IRIS | CNR IRIS | link.springer.com
2025
Journal article
Open Access
You've got the wrong number: evaluating deep learning training paradigms using handwritten digit recognition data
Ignesti G., Martinelli M., Moroni D.
To build more accurate and trustworthy artificial intelligence algorithms in deep learning, it is essential to understand the mechanisms driving classification systems to identify their targets. Typically, post hoc methods provide insights into this process. In this preliminary work, we shift the reconstruction of the class activation map to the training phase to evaluate how the model's performance changes compared to standard classification approaches. The MNIST dataset and its variants, such as Fashion MNIST, consist of well-defined images that facilitate testing this type of training process. Specifically, the classification targets are the only significant content in the images, excluding the background, allowing for a direct comparison of the reconstruction against the input images. To enhance the guidance of the network, we introduce a contrastive loss term to complement the standard classification function, which often uses categorical cross-entropy. By comparing the accuracy and the extracted pattern of the standard approach with the proposed method, we can gain valuable insights into the network's learning process. This approach aims toimprove the interpretability and effectiveness of the model during training, ultimately leading to higher classification accuracy and reliability.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4), pp. 978-983
DOI: 10.1134/s1054661824700974
Metrics:
Ignesti G., Martinelli M., Moroni D.
To build more accurate and trustworthy artificial intelligence algorithms in deep learning, it is essential to understand the mechanisms driving classification systems to identify their targets. Typically, post hoc methods provide insights into this process. In this preliminary work, we shift the reconstruction of the class activation map to the training phase to evaluate how the model's performance changes compared to standard classification approaches. The MNIST dataset and its variants, such as Fashion MNIST, consist of well-defined images that facilitate testing this type of training process. Specifically, the classification targets are the only significant content in the images, excluding the background, allowing for a direct comparison of the reconstruction against the input images. To enhance the guidance of the network, we introduce a contrastive loss term to complement the standard classification function, which often uses categorical cross-entropy. By comparing the accuracy and the extracted pattern of the standard approach with the proposed method, we can gain valuable insights into the network's learning process. This approach aims toimprove the interpretability and effectiveness of the model during training, ultimately leading to higher classification accuracy and reliability.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4), pp. 978-983
DOI: 10.1134/s1054661824700974
Metrics:
See at:
CNR IRIS 
| link.springer.com | CNR IRIS | CNR IRIS
2024
Journal article
Open Access
Remote sensing for maritime traffic understanding
Reggiannini M, Salerno E, Bacciu C, D'Errico A, Lo Duca A, Marchetti A, Martinelli M, Mercurio C, Mistretta A, Righi M, Tampucci M, Di Paola C
The capability of prompt response in case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed ICT framework devoted to understand the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of i) detecting the presence of vessel targets in the input imagery, ii) estimating the vessel types on the basis of their geometric and scatterometric features, iii) estimating the vessel kinematics, iv) classifying the navigation behaviour of the vessel and predicting its route and, eventually, v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public, annotated data sets.Source: REMOTE SENSING (BASEL), vol. 16 (issue 3)
DOI: 10.3390/rs16030557
Metrics:
Reggiannini M, Salerno E, Bacciu C, D'Errico A, Lo Duca A, Marchetti A, Martinelli M, Mercurio C, Mistretta A, Righi M, Tampucci M, Di Paola C
The capability of prompt response in case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed ICT framework devoted to understand the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of i) detecting the presence of vessel targets in the input imagery, ii) estimating the vessel types on the basis of their geometric and scatterometric features, iii) estimating the vessel kinematics, iv) classifying the navigation behaviour of the vessel and predicting its route and, eventually, v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public, annotated data sets.Source: REMOTE SENSING (BASEL), vol. 16 (issue 3)
DOI: 10.3390/rs16030557
Metrics:
See at:
Remote Sensing | Remote Sensing | IRIS Cnr | IRIS Cnr | CNR IRIS | CNR IRIS
2024
Other
Open Access
Strumenti innovativi per introdurre gli escursionisti ad una migliore lettura dell'ambiente
Ducci F, Dell'Orso R, Martinelli M
Lo scorso 30 agosto 2023 il Club Alpino Italiano Regione Toscana, tramite il suo Comitato Scientifico Toscano, e l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche hanno stipulato un accordo di collaborazione per attività di ricerca volta a sviluppare strumenti innovativi per introdurre gli escursionisti ad una migliore lettura dell'ambiente.
Ducci F, Dell'Orso R, Martinelli M
Lo scorso 30 agosto 2023 il Club Alpino Italiano Regione Toscana, tramite il suo Comitato Scientifico Toscano, e l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche hanno stipulato un accordo di collaborazione per attività di ricerca volta a sviluppare strumenti innovativi per introdurre gli escursionisti ad una migliore lettura dell'ambiente.
See at:
CNR IRIS | ISTI Repository | www.loscarpone.cai.it | CNR IRIS
2024
Other
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BANDO RICERCA COVID 19 TOSCANA. Relazione tecnica finale
Lorenza Pratali, Michela Rial, Serena Cardinali, Jessica De Giovanni, Emma Buzzigoli, Luca Bastiani, Chiara Deri, Gennaro D'Angelo, Laura Roth, Ida Rebecca Borth, Antonella Tomei, Silvia Memmini, Alessandro Iala, Alessandro Dini, Franca Marzoli, Cesare Rivieri, Mirko Passera, Luca Serasini, Davide Moroni, Antonio Bruno, Ignesti Giacomo, Giulio Galesi, Francesca Pardini, Francesca Borri, Massimo Martinelli
Project(s): TiAssisto
Lorenza Pratali, Michela Rial, Serena Cardinali, Jessica De Giovanni, Emma Buzzigoli, Luca Bastiani, Chiara Deri, Gennaro D'Angelo, Laura Roth, Ida Rebecca Borth, Antonella Tomei, Silvia Memmini, Alessandro Iala, Alessandro Dini, Franca Marzoli, Cesare Rivieri, Mirko Passera, Luca Serasini, Davide Moroni, Antonio Bruno, Ignesti Giacomo, Giulio Galesi, Francesca Pardini, Francesca Borri, Massimo Martinelli
Project(s): TiAssisto
2024
Other
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Aggiornamento 3/24 Progetto Barilla Agrosat+
Martinelli M., Moroni D.
Aggiornamento modelli.Project(s): Barilla Agrosat+
Martinelli M., Moroni D.
Aggiornamento modelli.Project(s): Barilla Agrosat+
2024
Conference article
Open Access
Plant-traits: how citizen science and artificial intelligence can impact natural science
Ignesti G., Moroni D., Martinelli M.
Citizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models. However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection. This study investigates the application of AI techniques to address these issues within natural science. We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data. Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on open- access platforms such as iNaturalist. By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 625-630. Belgrade, Serbia, 9-11/09/2024
DOI: 10.15439/2024f8703
Metrics:
Ignesti G., Moroni D., Martinelli M.
Citizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models. However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection. This study investigates the application of AI techniques to address these issues within natural science. We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data. Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on open- access platforms such as iNaturalist. By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.Source: ANNALS OF COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 39, pp. 625-630. Belgrade, Serbia, 9-11/09/2024
DOI: 10.15439/2024f8703
Metrics:
See at:
IRIS Cnr | annals-csis.org | Annals of computer science and information systems | IRIS Cnr | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS