2025
Conference article
Restricted
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
Restricted
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
Conference article
Restricted
A framework for imbalanced SAR ship classification: curriculum learning, weighted loss functions, and a novel evaluation metric
Awais Ch Muhammad, Reggiannini M., Moroni D.
Accurate ship classification is essential for maritime traffic monitoring applications but is significantly hindered by imbalanced datasets. In this paper, we propose a novel methodology that combines curriculum learning with weighted loss functions to address class imbalance in the FUSAR-Ship dataset, facilitating the accurate classification of its nine classes. Our method achieved notable improvements, including a 6.53% average increase in F1-scores compared to baseline models, and successfully identified all classes, including previously misclassified ones. To better evaluate model performance on long-tailed datasets, we introduce a novel evaluation metric that provides a more nuanced assessment of classification ability across underrepresented classes. While demonstrated on the FUSAR-Ship dataset, our approach and metric are broadly applicable to other imbalanced classification problems.DOI: 10.1109/wacvw65960.2025.00171
Project(s): National Biodiversity Future Center
Metrics:
Awais Ch Muhammad, Reggiannini M., Moroni D.
Accurate ship classification is essential for maritime traffic monitoring applications but is significantly hindered by imbalanced datasets. In this paper, we propose a novel methodology that combines curriculum learning with weighted loss functions to address class imbalance in the FUSAR-Ship dataset, facilitating the accurate classification of its nine classes. Our method achieved notable improvements, including a 6.53% average increase in F1-scores compared to baseline models, and successfully identified all classes, including previously misclassified ones. To better evaluate model performance on long-tailed datasets, we introduce a novel evaluation metric that provides a more nuanced assessment of classification ability across underrepresented classes. While demonstrated on the FUSAR-Ship dataset, our approach and metric are broadly applicable to other imbalanced classification problems.DOI: 10.1109/wacvw65960.2025.00171
Project(s): National Biodiversity Future Center
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Other
Open Access
Basics of image processing and analysis
Moroni D., Salvetti O.
Syllabus – Imaging, computer vision and UAVs in the Agritech Domain Lesson 1: Basics of Image Processing and Analysis Date: 18 April Time: 14:00 -20:00 Topics: • Geometric Transformations: Resizing, rotation, translation, and their applications in image pre-processing. • Filtering: Basics of convolution, edge detection, smoothing, and sharpening filters. • Image Enhancement: Task definition; Histogram equalization, contrast adjustment, and noise reduction techniques. • Image Registration: Task definition; Aligning images from different sources or points of view using keypoint detection and matching. • Image Segmentation: Task definition; Introduction to thresholding, region growing, and clustering for image segmentation. Methodology: • Lecture: Introduction to each concept with examples. • Hands-on Tutorials: Use open-source software (e.g., OpenCV, Scikit-Image) for practical exercises.
Moroni D., Salvetti O.
Syllabus – Imaging, computer vision and UAVs in the Agritech Domain Lesson 1: Basics of Image Processing and Analysis Date: 18 April Time: 14:00 -20:00 Topics: • Geometric Transformations: Resizing, rotation, translation, and their applications in image pre-processing. • Filtering: Basics of convolution, edge detection, smoothing, and sharpening filters. • Image Enhancement: Task definition; Histogram equalization, contrast adjustment, and noise reduction techniques. • Image Registration: Task definition; Aligning images from different sources or points of view using keypoint detection and matching. • Image Segmentation: Task definition; Introduction to thresholding, region growing, and clustering for image segmentation. Methodology: • Lecture: Introduction to each concept with examples. • Hands-on Tutorials: Use open-source software (e.g., OpenCV, Scikit-Image) for practical exercises.
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
Restricted
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
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
2025
Conference article
Open Access
Assessment of dance movement therapy outcomes: a preliminary proposal
Daoudagh S., Ignesti G., Moroni D., Sebastiani L., Paradisi P.
Context: Dance Movement Therapy (DMT) is a therapeutic modality that utilizes movement to promote holistic well-being. Current DMT assessment methods rely heavily on the subjective judgment of the DMT professional. Objective: Our research aims to develop a framework composed of different components with specific functionalities that can be integrated with the DMT modality to improve the objectivity and efficiency of DMT evaluations. Method: The DMT framework consists of an experimental protocol for data collection and a reference-supporting architecture that includes components for video analysis, physiological signal management, and evaluation tools. Artificial Intelligence (AI) based human pose estimation techniques are also employed to derive the DMT participants’ poses during the DMT sessions for more reliable movement analysis. Results: Our preliminary results consist of demonstrating the effectiveness of the AI-based pose estimation tool, YOLO-NAS-Pose, in accurately detecting participants’ poses. Conclusion: The proposed framework offers a promising approach to improving DMT practices by integrating and leveraging AI-based human pose estimation to evaluate participants’ movement in the DMT setting objectively. Future research will focus on refining the framework and developing user-friendly tools for widespread adoption in real DMT contexts.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 2371, pp. 382-395. Porto, Portogallo, 21-22/11/2024
DOI: 10.1007/978-3-031-83845-3_23
Project(s): Tuscany Health Ecosystem
Metrics:
Daoudagh S., Ignesti G., Moroni D., Sebastiani L., Paradisi P.
Context: Dance Movement Therapy (DMT) is a therapeutic modality that utilizes movement to promote holistic well-being. Current DMT assessment methods rely heavily on the subjective judgment of the DMT professional. Objective: Our research aims to develop a framework composed of different components with specific functionalities that can be integrated with the DMT modality to improve the objectivity and efficiency of DMT evaluations. Method: The DMT framework consists of an experimental protocol for data collection and a reference-supporting architecture that includes components for video analysis, physiological signal management, and evaluation tools. Artificial Intelligence (AI) based human pose estimation techniques are also employed to derive the DMT participants’ poses during the DMT sessions for more reliable movement analysis. Results: Our preliminary results consist of demonstrating the effectiveness of the AI-based pose estimation tool, YOLO-NAS-Pose, in accurately detecting participants’ poses. Conclusion: The proposed framework offers a promising approach to improving DMT practices by integrating and leveraging AI-based human pose estimation to evaluate participants’ movement in the DMT setting objectively. Future research will focus on refining the framework and developing user-friendly tools for widespread adoption in real DMT contexts.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 2371, pp. 382-395. Porto, Portogallo, 21-22/11/2024
DOI: 10.1007/978-3-031-83845-3_23
Project(s): Tuscany Health Ecosystem
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2024
Other
Restricted
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
Restricted
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
2024
Other
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2024
Other
Restricted
TiAssisto - Una piattaforma di tele-medicina e tele-monitoraggio basata su metodi di Intelligenza Artificiale
Alessandro Tonacci, Lorenza Pratali, Davide Moroni, Massimo Martinelli
Metodi di Intelligenza Artificiale utilizzati nel progetto TiAssistoProject(s): TiAssisto
Alessandro Tonacci, Lorenza Pratali, Davide Moroni, Massimo Martinelli
Metodi di Intelligenza Artificiale utilizzati nel progetto TiAssistoProject(s): TiAssisto
2024
Conference article
Open Access
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Other
Restricted
D6.2 – TiAssisto: Manuale di uso
Giacomo Ignesti, Chiara Deri, Gennaro D’angelo, Davide Cicalini, Davide Moroni, Lorenza Pratali, Massimo Martinelli
Project(s): TiAssisto
Giacomo Ignesti, Chiara Deri, Gennaro D’angelo, Davide Cicalini, Davide Moroni, Lorenza Pratali, Massimo Martinelli
Project(s): TiAssisto
2024
Book
Open Access
17th International Workshop on Advanced Infrared Technology and Applications (AITA 2023)
Bison Paolo, Cadelano Gianluca, D'Acunto Mario, Ferrarini Giovanni, Maldague Xavier, Martyniuk Piotr, Moroni Davide, Raimondi Valentina, Rogalski Antoni, Sakagami Takahide, Strojnik Marija, Volinia Monica
AITA is an international conference that has run since 1992 and is aimed at assessing the state of the art of the infrared spectral range technology and to present its most interesting applications. Venice was initially selected to host the XVI edition of AITA, which was planned for 2021; however, due to the pandemic, an in-person meeting was not feasible. As a result, AITA 2021 was conducted online, and the in-person gathering in Venice was rescheduled for 2023. The 17th International Workshop on Advanced Infrared Technology and Applications (AITA 2023) was organized in Venice.Source: ENGINEERING PROCEEDINGS, vol. 51 (issue 1)
DOI: 10.3390/engproc2023051051
Metrics:
Bison Paolo, Cadelano Gianluca, D'Acunto Mario, Ferrarini Giovanni, Maldague Xavier, Martyniuk Piotr, Moroni Davide, Raimondi Valentina, Rogalski Antoni, Sakagami Takahide, Strojnik Marija, Volinia Monica
AITA is an international conference that has run since 1992 and is aimed at assessing the state of the art of the infrared spectral range technology and to present its most interesting applications. Venice was initially selected to host the XVI edition of AITA, which was planned for 2021; however, due to the pandemic, an in-person meeting was not feasible. As a result, AITA 2021 was conducted online, and the in-person gathering in Venice was rescheduled for 2023. The 17th International Workshop on Advanced Infrared Technology and Applications (AITA 2023) was organized in Venice.Source: ENGINEERING PROCEEDINGS, vol. 51 (issue 1)
DOI: 10.3390/engproc2023051051
Metrics:
See at:
Engineering Proceedings | doi.org | CNR IRIS | www.mdpi.com | CNR IRIS
2024
Other
Open Access
A comprehensive system supporting sustainable agricultural production from farm to fork
Carboni A., Galesi G., Ignesti G., Leone G. R., Magrini M., Martinelli M., Martino G., Moroni D., Pardini F., Scozzari A.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.DOI: 10.5281/zenodo.12168200
Metrics:
Carboni A., Galesi G., Ignesti G., Leone G. R., Magrini M., Martinelli M., Martino G., Moroni D., Pardini F., Scozzari A.
Poster presented at ISTI Day 2023-2024 edition on June 14 2024.DOI: 10.5281/zenodo.12168200
Metrics:
2024
Journal article
Open Access
GranoScan: an AI-powered mobile app for in-field identification of biotic threats of wheat
Dainelli Riccardo, Bruno Antonio, Martinelli Massimo, Moroni Davide, Rocchi Leandro, Morelli Silvia, Ferrari Emilio, Silvestri Marco, Agostinelli Simone, La Cava Paolo, Toscano Piero
Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.Source: FRONTIERS IN PLANT SCIENCE, vol. 15
DOI: 10.3389/fpls.2024.1298791
Metrics:
Dainelli Riccardo, Bruno Antonio, Martinelli Massimo, Moroni Davide, Rocchi Leandro, Morelli Silvia, Ferrari Emilio, Silvestri Marco, Agostinelli Simone, La Cava Paolo, Toscano Piero
Capitalizing on the widespread adoption of smartphones among farmers and the application of artificial intelligence in computer vision, a variety of mobile applications have recently emerged in the agricultural domain. This paper introduces GranoScan, a freely available mobile app accessible on major online platforms, specifically designed for the real-time detection and identification of over 80 threats affecting wheat in the Mediterranean region. Developed through a co-design methodology involving direct collaboration with Italian farmers, this participatory approach resulted in an app featuring: (i) a graphical interface optimized for diverse in-field lighting conditions, (ii) a user-friendly interface allowing swift selection from a predefined menu, (iii) operability even in low or no connectivity, (iv) a straightforward operational guide, and (v) the ability to specify an area of interest in the photo for targeted threat identification. Underpinning GranoScan is a deep learning architecture named efficient minimal adaptive ensembling that was used to obtain accurate and robust artificial intelligence models. The method is based on an ensembling strategy that uses as core models two instances of the EfficientNet-b0 architecture, selected through the weighted F1-score. In this phase a very good precision is reached with peaks of 100% for pests, as well as in leaf damage and root disease tasks, and in some classes of spike and stem disease tasks. For weeds in the post-germination phase, the precision values range between 80% and 100%, while 100% is reached in all the classes for pre-flowering weeds, except one. Regarding recognition accuracy towards end-users in-field photos, GranoScan achieved good performances, with a mean accuracy of 77% and 95% for leaf diseases and for spike, stem and root diseases, respectively. Pests gained an accuracy of up to 94%, while for weeds the app shows a great ability (100% accuracy) in recognizing whether the target weed is a dicot or monocot and 60% accuracy for distinguishing species in both the post-germination and pre-flowering stage. Our precision and accuracy results conform to or outperform those of other studies deploying artificial intelligence models on mobile devices, confirming that GranoScan is a valuable tool also in challenging outdoor conditions.Source: FRONTIERS IN PLANT SCIENCE, vol. 15
DOI: 10.3389/fpls.2024.1298791
Metrics:
See at:
Frontiers in Plant Science | PubMed Central | IRIS Cnr | IRIS Cnr | Software Heritage | Software Heritage | Software Heritage | GitHub | GitHub | GitHub | GitHub | GitHub | CNR IRIS
2024
Journal article
Open Access
Harnessing topological machine learning in Raman spectroscopy: perspectives for Alzheimer’s disease detection via cerebrospinal fluid analysis
Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 361 (issue 18)
DOI: 10.1016/j.jfranklin.2024.107249
Project(s): Proteomics, RAdiomics & Machine learning-integrated strategy for precision medicine for Alzheimer’s
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Conti F., Banchelli M., Bessi V., Cecchi C., Chiti F., Colantonio S., D'Andrea C., De Angelis M., Moroni D., Nacmias B., Pascali M. A., Sorbi S., Matteini P.
The cerebrospinal fluid of 21 subjects who received a clinical diagnosis of Alzheimer’s disease (AD) as well as of 22 pathological controls has been collected and analysed by Raman spectroscopy (RS). We investigated whether the Raman spectra could be used to distinguish AD from controls, after a preprocessing procedure. We applied machine learning to a set of topological descriptors extracted from the spectra, achieving a high classification accuracy of 86%. Our experimentation indicates that RS and topological analysis may be a reliable and effective combination to confirm or disprove a clinical diagnosis of Alzheimer’s disease. The following steps will aim at leveraging the intrinsic interpretability of the topological data analysis to characterize the AD subtypes, e.g. by identifying the bands of the Raman spectrum relevant for AD detection, possibly increasing and/or confirming the knowledge about the precise molecular events and biological pathways behind the Alzheimer’s disease.Source: JOURNAL OF THE FRANKLIN INSTITUTE, vol. 361 (issue 18)
DOI: 10.1016/j.jfranklin.2024.107249
Project(s): Proteomics, RAdiomics & Machine learning-integrated strategy for precision medicine for Alzheimer’s
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