Claudia Giannone
@distal.unibo.it
University of Bologna
Scopus Publications
- Monitoring horse behaviour with deep learning models
Claudia Giannone, Chiara Maccario, Emanuela Dalla Costa, Elie Atallah, Marco Bovo
Veterinary Quarterly, 2026
Detailed analysis of stabled horse behaviour can reveal accurate information about its well-being. Advances in deep learning now allow these behaviours to be tracked without being invasive through the use of video data. This study evaluated a convolutional neural network for recognising standing, lying, and drinking behaviours in a horse housed in a wooden stall and recorded continuously over 29 consecutive days. Model predictions were compared with manually annotated ground truth data. Standing was detected with high precision (97.5%) and high recall (89.2%). Lying behaviour was classified with high precision (92.8%) but lower recall (63.1%). Activity patterns showed that standing dominated daily time budgets (>85%), lying accounted for 5-10%, and drinking occurred most often between 04:00 pm and 10:00 pm. These results demonstrate that deep learning can classify common equine behaviours from video, supporting its use in automated welfare monitoring. Future evaluations will explore the recognition of less frequent behaviours. - Impact of the technology to monitor horse behaviour and health: a scoping review
Claudia Giannone, Elie Atallah, Emanuela Dalla Costa, Eleonora Benetti, Enrica Santolini, Patrizia Tassinari, Marco Bovo
Journal of Equine Veterinary Science, 2025 - Automated dairy cow identification and feeding behaviour analysis using a computer vision model based on YOLOv8
Claudia Giannone, Mohsen Sahraeibelverdy, Martina Lamanna, Damiano Cavallini, Andrea Formigoni, Patrizia Tassinari, Daniele Torreggiani, Marco Bovo
Smart Agricultural Technology, 2025
Monitoring changes in the feeding behaviour of dairy cows is essential for assessing their feeding preferences, milk production, and health status. Sick cows often exhibit altered feeding patterns, such as reduced feeding time and frequency, making early detection crucial for effective farm management. Traditional methods for monitoring feeding behaviour are labour-intensive, time-consuming, and prone to errors. To address these challenges, precision livestock farming technologies have gained increasing attention. While wearable sensors, such as accelerometers and RFID tags, provide accurate data, they have limitations, including high costs and potential stress on animals. Alternatively, computer vision-based approaches offer a non-invasive and efficient solution for monitoring feeding behaviour. Deep learning techniques, particularly the YOLO (You Only Look Once) object detection model, have been widely applied in animal husbandry. Despite advancements in object detection, individual cow recognition in operational environment remains a challenge due to the lack of a standardized and viable approach. The main aim of the paper is to evaluate the reliability and validate a deep learning-based computer vision model for automatically recognizing individual cows at the feeding lane in a relevant environment. By identifying individual cows, it is possible to determine their feeding time, feeding duration and daily frequency. The paper describes the work phases from data collection to analysis and validation of an improved YOLOv8n model that, after a fine-tuning on the collected video set, achieved a precision of 85 %, a recall of 62 % (F1 score 0.72) at IoU 0.5 and processes a 640 × 640 pixels frame in just 12 ms on an NVIDIA RTX 2080. The promising results presented here contribute to the advancement and validation of computer vision applications in herd monitoring, supporting the commercial adoption of these technologies for analysing cow behaviour so increasing animal welfare and the sustainability of the animal production. - Artificial intelligence meets dairy cow research: Large language model's application in extracting daily time-activity budget data for a meta-analytical study
M. Lamanna, E. Muca, C. Giannone, M. Bovo, F. Boffo, A. Romanzin, D. Cavallini
Journal of Dairy Science, 2025
This study investigates the application of ChatGPT-4 in extracting and classifying behavioral data from scientific literature, focusing on the daily time-activity budget of dairy cows. Accurate analysis of time-activity budgets is crucial for understanding dairy cow welfare and productivity. Traditional methods are time-intensive and prone to bias. This study evaluates the accuracy and reliability of ChatGPT-4 in data extraction and data categorization, considering explicit, inferred, and ambiguous labels for the data, compared with human analysis. A collection of 55 papers on dairy cow behavior were used in the studies. Data extraction for eating, ruminating, and lying behaviors was performed manually and via ChatGPT-4. The artificial intelligence (AI) model's accuracy and labeling performance were assessed through descriptive and statistical analyses. Mixed model analysis was used to compare human and AI outcomes. Artificial intelligence and human time budget data showed significant differences for eating and ruminating but not for lying. ChatGPT-4 estimated daily eating time at 22.3% compared with 23.8% by humans. For ruminating, AI reported 33.4% against 31.7% by humans. Daily lying times were nearly identical, with AI at 44.4% and human analysis at 44.2%. The global accuracy in data extraction was ∼75%, and labeling accuracy reached 67.3%, with significant variability across behavioral categories. In general, the AI model demonstrates moderate accuracy in extracting and categorizing behavioral data, particularly for inferred and ambiguous data. However, explicit data extraction posed challenges, highlighting AI's dependence on input quality and structure. The consistency between AI and human analyses for lying behavior underscores AI's potential for specific applications. ChatGPT-4 offers a promising complementary tool for behavioral research, enabling efficient and scalable data extraction. However, improvements in AI algorithms and standardized reporting in scientific literature are essential for broader applicability. The study advocates for hybrid approaches combining AI capabilities with human oversight to enhance the reliability and accuracy of dairy cow behavioral research. - Reducing life cycle environmental impacts of milk production through precision livestock farming
Daniela Lovarelli, Marco Bovo, Claudia Giannone, Enrica Santolini, Patrizia Tassinari, Marcella Guarino
Sustainable Production and Consumption, 2024
In recent decades, the livestock sector has significantly improved its efficiency, productivity, and environmental sustainability. Precision Livestock Farming (PLF) represents a driver in this direction, since it enables to monitor individual animals and herds, and supports the farmer in making better decisions. Although the benefits are clear on a livestock perspective, it is difficult to quantify the environmental benefit of having technology on farm, mostly due to the complexity of collecting data on the same farm before and after a certain solution. In this context, this paper focuses on the assessment of the environmental sustainability of a case-study Italian dairy cattle farm where different technologies were installed one by one: first a mechanical ventilation system (MV) and second an automatic milking system (AMS), without introducing other significant changes to the farm management and practices in the meantime. The environmental impact of milk production on the farm was quantified through the Life Cycle Assessment (LCA) method, and the initial farm configuration was compared with the two scenarios in which each technology was incorporated. Fat and protein corrected milk (FPCM) was used as Functional Unit, and a cradle to farm gate system boundary and biophysical allocation method were selected. This enabled to provide valuable insights for stakeholders about the effect on the environmental sustainability of the use of the two technologies. The results show that for all the evaluated impact categories there is an environmental benefit of the improved scenarios. The biggest benefit can be observed with the installation of mechanical ventilation, to which correspond benefits in terms of animal health, welfare and productivity. Then, also AMS entails sustainability improvements, mainly linked with increased efficiency and productivity. In conclusion, the use of technology on dairy farms improves not only the farm efficiency and the animal management, but also the environmental sustainability. Furthermore, the rapid technological advancements may further enhance this positive trend in reducing the contribution of livestock farming to the environmental impacts provided that farmers adopt them. - Real time identification of individual dairy cows through computer vision
11th European Conference on Precision Livestock Farming, 2024 - A link between PLF, animal welfare and LCA: the objectives of SUS3D project
11th European Conference on Precision Livestock Farming, 2024 - Review of the Heat Stress-Induced Responses in Dairy Cattle
Claudia Giannone, Marco Bovo, Mattia Ceccarelli, Daniele Torreggiani, Patrizia Tassinari
Animals, 2023
In the dairy cattle sector, the evaluation of the effects induced by heat stress is still one of the most impactful and investigated aspects as it is strongly connected to both sustainability of the production and animal welfare. On the other hand, more recently, the possibility of collecting a large dataset made available by the increasing technology diffusion is paving the way for the application of advanced numerical techniques based on machine learning or big data approaches. In this scenario, driven by rapid change, there could be the risk of dispersing the relevant information represented by the physiological animal component, which should maintain the central role in the development of numerical models and tools. In light of this, the present literature review aims to consolidate and synthesize existing research on the physiological consequences of heat stress in dairy cattle. The present review provides, in a single document, an overview, as complete as possible, of the heat stress-induced responses in dairy cattle with the intent of filling the existing research gap for extracting the veterinary knowledge present in the literature and make it available for future applications also in different research fields. - Dietary Grape Pomace Supplementation in Lambs Affects the Meat Fatty Acid Composition, Volatile Profiles and Oxidative Stability
Francesca Bennato, Camillo Martino, Andrea Ianni, Claudia Giannone, Giuseppe Martino
Foods, 2023
The aim of this study was to evaluate the effects of supplementing grape pomace (GP) in lambs’ diets. A total of 30 lambs homogeneous for body weight (13.1 ± 2.1 kg) and age (25–30 days) were randomly allocated into two groups. The control group (CTR) received a standard diet for 45 days, while in the same period the experimental group (GP+) was fed with a diet containing 10% GP on a dry matter (DM) basis. The meat samples from the two groups showed no significant differences in drip loss, cooking loss, meat color and total lipid amount. However, the experimental feeding strategy influenced the meat fatty acid composition, with an increase in the relative percentages of stearic, vaccenic and rumenic acids. In particular, the increase in rumenic acids is associated with several health benefits attributed to its high bioactive properties. In cooked meat samples stored for 5 days at 4 °C, the dietary GP supplementation induced an increase in nonanal and 1-octen-3-ol and a significant reduction of hexanal, an indicator of oxidation; this improved resistance to oxidation in the GP+ samples and was also confirmed by the thiobarbituric acid reactive species (TBARS) test. In summary, the present study showed that the dietary GP supplementation was effective in improving the fatty acid composition and the oxidative stability of lamb meat. The use and valorization of the GP as a matrix of interest for zootechnical nutrition can, therefore, represent a suitable strategy for improving the qualitative aspects of animal production. - Algorithms for the identification of yield anomalies in cattle dataset collected by automatic milking systems
Mattia Ceccarelli, Miki Agrusti, Claudia Giannone, Marco Bovo, Alberto Barbaresi, Enrica Santolini, Stefano Benni, Daniele Torreggiani, Patrizia Tassinari
2023 IEEE International Workshop on Metrology for Agriculture and Forestry Metroagrifor 2023 Proceedings, 2023
Despite the growing interest in new animal housing, management strategies for reducing impacts, and collecting daily data, there is a lack of studies investigating factors that can lead to productive anomalies. On the other hand, the use of automatic milking robots, milking parlours, collars and pedometers allows for a precise monitoring of dairy cows, providing farmers with real time information. In this context, the early detection of production anomalies is fundamental for animal health and safety. In this work, two numerical methods for detecting daily milk production anomalies are presented and applied to three different farms selected as case studies. The methods described in this paper provide a numerical procedure having the scope of detecting milk yield anomalies. Both the algorithms presented hereinafter are based on statistical calculations and take as input daily resting time and daily milk yield recorded respectively by the pedometers worn by the cows and by the automatic milking system of the barns. The first method take into consideration two indicators, namely the Difference in Relative Production (DRP) and the Daily Rest time (DR). DRP is defined as the relative difference in daily milk yield between real-time data of a single animal and a baseline curve considered as an ideal trend. An anomaly (i.e. a deviation from the normal value) is determined, for a single cow, for a specific day, if two conditions on DRP and DR are contemporary verified. In the second method, starting from the Wood function, maybe the most famous model to fit the production of the cow in dependence of day in milk, the concept of reliability of robust statistics has been introduced in order to obtain, for each animal, a more solid and realistic lactation curve since not affected by outlier values. - Seasonal and Feeding System Effects on Qualitative Parameters of Bovine Milk Produced in the Abruzzo Region (Italy)
Marco Florio, Claudia Giannone, Andrea Ianni, Francesca Bennato, Lisa Grotta, Giuseppe Martino
Agriculture Switzerland, 2022
RECENT SCHOLAR PUBLICATIONS
- Monitoring horse behaviour with deep learning models
C Giannone, C Maccario, E Dalla Costa, E Atallah, M Bovo
Veterinary Quarterly 46 (1), 2665442 , 2026
2026 - Impact of the technology to monitor horse behaviour and health: a scoping review
C Giannone, E Atallah, E Dalla Costa, E Benetti, E Santolini, P Tassinari, ...
Journal of Equine Veterinary Science, 105734 , 2025
2025
Citations: 3 - Automated dairy cow identification and feeding behaviour analysis using a computer vision model based on YOLOv8
C Giannone, M Sahraeibelverdy, M Lamanna, D Cavallini, A Formigoni, ...
Smart Agricultural Technology, 101304 , 2025
2025
Citations: 27 - Artificial intelligence meets dairy cow research: Large language model's application in extracting daily time-activity budget data for a meta-analytical study
M Lamanna, E Muca, C Giannone, M Bovo, F Boffo, A Romanzin, ...
Journal of dairy science , 2025
2025
Citations: 24 - Meta-analytical study on feeding behavior and daily time budget management of dairy cows in free-stall housing using ChatGPT
M Lamanna, E Muca, C Giannone, M Bovo, A Romanzin, A Formigoni, ...
ITALIAN JOURNAL OF ANIMAL SCIENCE 24 (1), 142-142 , 2025
2025 - Using Solomon Coder for the ethological and feeding behaviour analysis of dairy cows in tie-stall housing: A practical guide
M Lamanna, C Giannone, M Bovo, G Bellisola, A Romanzin, D Cavallini
IEEE International Workshop on Measurements and Applications in Veterinary … , 2025
2025 - Exploitation of Renewable Sources for the Energy Needs of Farm Buildings: an Application in the Swine Sector
S Benni, C Giannone, CA Perez Garcia
BIOSYSTEMS ENGINEERING FOR THE GREEN TRANSITION, 159-159 , 2025
2025 - Analysis of dairy cow feeding behavior through computer vision
C Giannone, M Lamanna, D Cavallini, A Formigoni, P Tassinari, M Bovo
ASPA 26th Congress Book of Abstract, 261-261 , 2025
2025 - Using precision livestock farming to reduce the environmental impacts of cow milk production
C Giannone, D Lovarelli, M Bovo, E Santolini, M Guarino, D Torreggiani, ...
Book of Abstracts of the 76th Annual Meeting of the European Federation of … , 2025
2025 - Study of automated identification of dairy cows using computer vision and RFID technologies
C Giannone, M Bovo, CA Perez Garcia, D Cavallini, M Lamanna, ...
Book of Abstracts of the 76th Annual Meeting of the European Federation of … , 2025
2025 - Preliminary analyses on the identification of horse behaviors using deep learning techniques
C Giannone, E Dalla Costa, C Maccario, E Atallah, M Bovo
2025 Association for Science and Animal Production Congress , 2025
2025 - Recognition of horse behaviours using deep learning techniques
C Giannone, E Dalla Costa, C Maccario, E Atallah, M Bovo
2025 International Workshop on Measurements and Applications in Veterinary … , 2025
2025 - Reducing life cycle environmental impacts of milk production through precision livestock farming
D Lovarelli, M Bovo, C Giannone, E Santolini, P Tassinari, M Guarino
Sustainable Production and Consumption 51, 303-314 , 2024
2024
Citations: 30 - A link between PLF, animal welfare and LCA: the objectives of SUS3D project
MBM Bovo, C Capretti, M Chincarini, D Di Battista, C Giannone, L Lanzoni, ...
11th European Conference on Precision Livestock Farming-Conference … , 2024
2024 - Real time identification of individual dairy cows through computer vision
C Giannone, M Bovo, M Ceccarelli, S Benni, P Tassinari, D Torreggiani
11th European Conference on Precision Livestock Farming, 452-458 , 2024
2024
Citations: 1 - Preliminary results of environmental sustainability assessment of dairy farm equipped with PLF systems: a case study
C Giannone, M Bovo, E Santolini, D Torreggiani, P Tassinari
2024 International Workshop on Measurements and Applications in Veterinary … , 2024
2024 - Advancements and challenges in equine monitoring technologies
C Giannone, E Dalla Costa, M Bovo, D Torreggiani, P Tassinari
2024 International Workshop on Measurements and Applications in Veterinary … , 2024
2024
Citations: 1 - Review of the heat stress-induced responses in dairy cattle
C Giannone, M Bovo, M Ceccarelli, D Torreggiani, P Tassinari
Animals 13 (22), 3451 , 2023
2023
Citations: 117 - Algorithms for the identification of yield anomalies in cattle dataset collected by automatic milking systems
M Ceccarelli, M Agrusti, C Giannone, M Bovo, A Barbaresi, E Santolini, ...
2023 IEEE International Workshop on Metrology for Agriculture and Forestry … , 2023
2023 - Dietary grape pomace supplementation in lambs affects the meat fatty acid composition, volatile profiles and oxidative stability
F Bennato, C Martino, A Ianni, C Giannone, G Martino
Foods 12 (6), 1257 , 2023
2023
Citations: 15
MOST CITED SCHOLAR PUBLICATIONS
- Review of the heat stress-induced responses in dairy cattle
C Giannone, M Bovo, M Ceccarelli, D Torreggiani, P Tassinari
Animals 13 (22), 3451 , 2023
2023
Citations: 117 - Reducing life cycle environmental impacts of milk production through precision livestock farming
D Lovarelli, M Bovo, C Giannone, E Santolini, P Tassinari, M Guarino
Sustainable Production and Consumption 51, 303-314 , 2024
2024
Citations: 30 - Automated dairy cow identification and feeding behaviour analysis using a computer vision model based on YOLOv8
C Giannone, M Sahraeibelverdy, M Lamanna, D Cavallini, A Formigoni, ...
Smart Agricultural Technology, 101304 , 2025
2025
Citations: 27 - Artificial intelligence meets dairy cow research: Large language model's application in extracting daily time-activity budget data for a meta-analytical study
M Lamanna, E Muca, C Giannone, M Bovo, F Boffo, A Romanzin, ...
Journal of dairy science , 2025
2025
Citations: 24 - Dietary grape pomace supplementation in lambs affects the meat fatty acid composition, volatile profiles and oxidative stability
F Bennato, C Martino, A Ianni, C Giannone, G Martino
Foods 12 (6), 1257 , 2023
2023
Citations: 15 - Seasonal and feeding system effects on qualitative parameters of bovine milk produced in the Abruzzo region (Italy)
M Florio, C Giannone, A Ianni, F Bennato, L Grotta, G Martino
Agriculture 12 (7), 917 , 2022
2022
Citations: 14 - Impact of the technology to monitor horse behaviour and health: a scoping review
C Giannone, E Atallah, E Dalla Costa, E Benetti, E Santolini, P Tassinari, ...
Journal of Equine Veterinary Science, 105734 , 2025
2025
Citations: 3 - Real time identification of individual dairy cows through computer vision
C Giannone, M Bovo, M Ceccarelli, S Benni, P Tassinari, D Torreggiani
11th European Conference on Precision Livestock Farming, 452-458 , 2024
2024
Citations: 1 - Advancements and challenges in equine monitoring technologies
C Giannone, E Dalla Costa, M Bovo, D Torreggiani, P Tassinari
2024 International Workshop on Measurements and Applications in Veterinary … , 2024
2024
Citations: 1 - Monitoring horse behaviour with deep learning models
C Giannone, C Maccario, E Dalla Costa, E Atallah, M Bovo
Veterinary Quarterly 46 (1), 2665442 , 2026
2026 - Meta-analytical study on feeding behavior and daily time budget management of dairy cows in free-stall housing using ChatGPT
M Lamanna, E Muca, C Giannone, M Bovo, A Romanzin, A Formigoni, ...
ITALIAN JOURNAL OF ANIMAL SCIENCE 24 (1), 142-142 , 2025
2025 - Using Solomon Coder for the ethological and feeding behaviour analysis of dairy cows in tie-stall housing: A practical guide
M Lamanna, C Giannone, M Bovo, G Bellisola, A Romanzin, D Cavallini
IEEE International Workshop on Measurements and Applications in Veterinary … , 2025
2025 - Exploitation of Renewable Sources for the Energy Needs of Farm Buildings: an Application in the Swine Sector
S Benni, C Giannone, CA Perez Garcia
BIOSYSTEMS ENGINEERING FOR THE GREEN TRANSITION, 159-159 , 2025
2025 - Analysis of dairy cow feeding behavior through computer vision
C Giannone, M Lamanna, D Cavallini, A Formigoni, P Tassinari, M Bovo
ASPA 26th Congress Book of Abstract, 261-261 , 2025
2025 - Using precision livestock farming to reduce the environmental impacts of cow milk production
C Giannone, D Lovarelli, M Bovo, E Santolini, M Guarino, D Torreggiani, ...
Book of Abstracts of the 76th Annual Meeting of the European Federation of … , 2025
2025 - Study of automated identification of dairy cows using computer vision and RFID technologies
C Giannone, M Bovo, CA Perez Garcia, D Cavallini, M Lamanna, ...
Book of Abstracts of the 76th Annual Meeting of the European Federation of … , 2025
2025 - Preliminary analyses on the identification of horse behaviors using deep learning techniques
C Giannone, E Dalla Costa, C Maccario, E Atallah, M Bovo
2025 Association for Science and Animal Production Congress , 2025
2025 - Recognition of horse behaviours using deep learning techniques
C Giannone, E Dalla Costa, C Maccario, E Atallah, M Bovo
2025 International Workshop on Measurements and Applications in Veterinary … , 2025
2025 - A link between PLF, animal welfare and LCA: the objectives of SUS3D project
MBM Bovo, C Capretti, M Chincarini, D Di Battista, C Giannone, L Lanzoni, ...
11th European Conference on Precision Livestock Farming-Conference … , 2024
2024 - Preliminary results of environmental sustainability assessment of dairy farm equipped with PLF systems: a case study
C Giannone, M Bovo, E Santolini, D Torreggiani, P Tassinari
2024 International Workshop on Measurements and Applications in Veterinary … , 2024
2024
