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One-Shot Learning with Pseudo-Labeling for Cattle Video Segmentation in Smart Livestock Farming

Yongliang Qiao, Tengfei Xue, He Kong, Cameron Clark, Sabrina Lomax, Khalid Rafique, Salah Sukkarieh
2022 Animals  
Then, PL leverages the segmentation results of the Xception-FCN model to fine-tune the model, leading to performance boosts in cattle video segmentation.  ...  Xception-FCN utilizes depth-wise separable convolutions to learn different-level visual features and localize dense prediction based on the one single labeled frame.  ...  Acknowledgments: The authors also express their gratitude to Javier Martinez, Amanda Doughty, Ashraful Islam and Mike Reynolds for their help in experiment organization and data collection.  ... 
doi:10.3390/ani12050558 pmid:35268130 pmcid:PMC8908826 fatcat:it4mllkwp5bv5mzcxv42wf7goe

Multi-CenterAgent Loss for Visual Identification of Chinese Simmental in the Wild

Jian-Min Zhao, Qiu-Sheng Lian, Neal N. Xiong
2022 Animals  
Visual identification of cattle in the wild provides an essential way for real-time cattle monitoring applicable to precision livestock farming.  ...  We propose multi-center agent loss to jointly supervise the learning of DCNNs by SoftMax with multiple centers and the agent triplet.  ...  Then, the model is jointly supervised by multi-center agent loss to learn more center points for features to concentrate on and meanwhile enforces separation among agents of different classes.  ... 
doi:10.3390/ani12040459 pmid:35203167 pmcid:PMC8868377 fatcat:ywmeg4gevfaknhnvv236qn346i

Dealing with complexity of new phenotypes in modern dairy cattle breeding

Anita Seidel, Nina Krattenmacher, Georg Thaller
2020 Animal Frontiers  
Here, she put a strong focus on the use of machine learning methods, in particular artificial neural networks, to predict complex gene interactions in the context of genomic prediction in cattle.  ...  This newly evolved field of interdisciplinary research focuses on estimating more accurate predictive values of phenotypes by using predictive modeling methods such as machine learning (González-Camacho  ... 
doi:10.1093/af/vfaa005 pmid:32257600 pmcid:PMC7111594 fatcat:j5zv35zepvbftmk2o4jvdktiuy

Implementation of Artificial Intelligence Policy in the Field of Livestock and Dairy Farm

Venkata Naga Satya Surendra Chimakurthi
2019 American Journal of Trade and Policy  
Through this research, readers can get a better idea about applications of AI, its benefits and disadvantages in the field of dairy farming and livestock.  ...  It is greatly expected that this modern technology has the potential to bring a breakthrough in the field of livestock through combining biological information with technological advancement.  ...  Cattle Safety: Bulls rarely vent their rage on a cow, but they are naturally large creatures. The male lifts the female and puts a lot of weight on her hind legs to breed natural cows.  ... 
doi:10.18034/ajtp.v6i3.591 fatcat:ygusqqt3ung7vcbk53iptfzhne

ASAS-NANP SYMPOSIUM: Applications of machine learning for livestock body weight prediction from digital images

Zhuoyi Wang, Saeed Shadpour, Esther Chan, Vanessa Rotondo, Katharine M Wood, Dan Tulpan
2021 Journal of Animal Science  
Monitoring, recording, and predicting livestock body weight (BW) allows for timely intervention in diets and health, greater efficiency in genetic selection, and identification of optimal times to market  ...  Direct approaches include partial-weight or full-weight industrial scales placed in designated locations on large farms that measure passively or dynamically the weight of livestock.  ...  Presented at the ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: Training the Future Generation in Data and Predictive Analytics for Sustainable Development at the 2020 Virtual Annual Meeting  ... 
doi:10.1093/jas/skab022 pmid:33626149 fatcat:ucnfajo4kzgg3pzs6yz2hfcd2m

Machine Learning in Agriculture: A Review

Konstantinos Liakos, Patrizia Busato, Dimitrios Moshou, Simon Pearson, Dionysis Bochtis
2018 Sensors  
The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management  ...  , including applications on animal welfare and livestock production; (c) water management; and (d) soil management.  ...  The last article of the section [104] deals with the development of a function for the prediction of carcass weight for beef cattle of the Asturiana de los Valles breed based on SVR models and zoometric  ... 
doi:10.3390/s18082674 pmid:30110960 fatcat:mc44hp67fbfrviogramffyasla

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Beibei Xu, Wensheng Wang, Leifeng Guo, Guipeng Chen, Yaowu Wang, Wenju Zhang, Yongfeng Li
2021 Agriculture  
Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper.  ...  Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology.  ...  Acknowledgments: We are grateful to two private housing farms in Jiangxi Province in China for their kindly support with data collection.  ... 
doi:10.3390/agriculture11111062 fatcat:67p6qgmdcjhupnqg4c4vwlv7cm

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture1

Gota Morota, Ricardo V Ventura, Fabyano F Silva, Masanori Koyama, Samodha C Fernando
2018 Journal of Animal Science  
Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint  ...  To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.  ...  Livestock body weight is critical for nutritional and breeding management because it is a direct indicator of animal growth, health status, and readiness for market.  ... 
doi:10.1093/jas/sky014 pmid:29385611 fatcat:xrnekxfbqbea7gq5zejupxhdry

Automated Individual Cattle Identification Using Video Data: A Unified Deep Learning Architecture Approach

Yongliang Qiao, Cameron Clark, Sabrina Lomax, He Kong, Daobilige Su, Salah Sukkarieh
2021 Frontiers in Animal Science  
Individual cattle identification is a prerequisite and foundation for precision livestock farming.  ...  Here, we propose and implement a new unified deep learning approach to cattle identification using video analysis.  ...  MLP can model the correlation between those inputs and outputs, therefore it is often applied for supervised learning tasks.  ... 
doi:10.3389/fanim.2021.759147 fatcat:hoko74h4sfa7fcss75m5yi37jq

The Future of Phenomics

2020 Animal Frontiers  
Here, she put a strong focus on the use of machine learning methods, in particular artificial neural networks, to predict complex gene interactions in the context of genomic prediction in cattle.  ...  Based on the sensor data, a predictive model predicted 43.5% of the calving events with 1% false positive alerts. A range of precision dairy monitoring technologies were tested (Gresse, 2018) .  ...  The program will cover nutrition, genetics, physiology, animal health and welfare, livestock farming systems, precision livestock farming, insect production and use, as well as cattle, horse pig, sheep  ... 
doi:10.1093/af/vfaa015 pmid:32724711 pmcid:PMC7377506 fatcat:zmpfwyxmz5afxmwdelqpe7fnvi

Machine Learning in Agriculture: A Comprehensive Updated Review

Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, Dionysis Bochtis
2021 Sensors  
In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively.  ...  management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines.  ...  As for Regression, it is used for supervised learning models intending to model a target value on the basis of independent predictors.  ... 
doi:10.3390/s21113758 pmid:34071553 fatcat:moehdvs6efdpxpklidutmw2ary

Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed

Hamidreza Ghotbaldini, Mohammadreza Mohammadabadi, Hossein Nezamabadi-pour, Olena Ivanivna Babenko, Maryna Vitaliivna Bushtruk, Serhii Vasyliovych Tkachenko
2019 Acta Scientiarum: Animal Sciences  
The present study aimed to apply artificial neural networks to predict the breeding values of body weight in 6-month age of Kermani sheep.  ...  Results showed that the both networks capable to predict breeding values for body weight at 6 month-age in Kermani sheep.  ...  MLP utilizes a supervised learning technique called back propagation for training, and its learning rule is generalized delta learning rule.  ... 
doi:10.4025/actascianimsci.v41i1.45282 fatcat:rcbmsp54hbesbad4juovjncbkq

Research on Chengdu Ma Goat Recognition Based on Computer Vison

Jingyu Pu, Chengjun Yu, Xiaoyan Chen, Yu Zhang, Xiao Yang, Jun Li
2022 Animals  
Most livestock farmers still conduct small-scale breeding in primitive ways, which is not conducive to the breeding and protection of Chengdu ma goats.  ...  Experiments show that our method is able to accurately recognize Chengdu ma goats in the actual indoor barn breeding environment, which lays the foundation for precision feeding based on sex and age.  ...  Acknowledgments: Thanks to Xiaoli Yao for the help in data annotation. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ani12141746 pmid:35883293 pmcid:PMC9312181 fatcat:57t7a5yelva4dbpwuudmsc7nla

Advancements in Sensor Technology and Decision Support Intelligent Tools to Assist Smart Livestock Farming

Luis O Tedeschi, Paul L Greenwood, Ilan Halachmi
2021 Journal of Animal Science  
PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture's forage quantity and quality; body weight and composition and physiological assessments  ...  Remote-monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20  ...  Furthermore, with the development of unsupervised learning (more recently referred to as self-supervised learning) (LeCun et al., 2015) , the search for predictive reasoning may become even more complicated  ... 
doi:10.1093/jas/skab038 pmid:33550395 pmcid:PMC7896629 fatcat:2emxdocbyjdd7pejbargtp2qc4

Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning [article]

William Andrew, Jing Gao, Siobhan Mullan, Neill Campbell, Andrew W Dowsey, Tilo Burghardt
2020 arXiv   pre-print
We find that deep metric learning systems show strong performance even when many cattle unseen during system training are to be identified and re-identified -- achieving 93.8% accuracy when trained on  ...  This work paves the way for facilitating the non-intrusive monitoring of cattle applicable to precision farming and surveillance for automated productivity, health and welfare monitoring, and to veterinary  ...  In doing so, this paves the way for the model to generalise to new farms and new herds prior to deployment without any training, with significant implications for the precision livestock farming sector  ... 
arXiv:2006.09205v3 fatcat:zjdnacxudzhnjoamaazhkdlrly
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