Dairy Cow Rumination Detection: A Deep Learning Approach

Ayadi, Safa; Said, Ahmed Ben; Jabbar, Rateb; Aloulou, Chafik; Chabbouh, Achraf; Achballah, Ahmed Ben

doi:10.1007/978-3-030-65810-6_7

Cited by 15 publications

(8 citation statements)

References 45 publications

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“…Several examples of applications of machine learning in precision agriculture [51] are reported, i.e., soil properties detection [52][53][54], crop yield predictions [55][56][57][58][59], disease [60][61][62][63] and weed detection [64][65][66], site-specific irrigation [67][68][69], and livestock production and management [70][71][72]. One of the most in-depth topics is the analysis of plant health with hyperspectral data [73].…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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“…For example, Alameer et al [21] adopted you-only-look-once (YOLO) and faster regions with CNN features (Faster R-CNN) with deep residual network (ResNet-50) to recognize the postures of pigs, i.e., standing, sitting, lying lateral, and lying sternal. Ayadi et al [22] used a standard VGG16 model to recognize cow postures, specifically rumination behaviour, from video recordings. Brünger et al [23] used a U-Net network with different encoder architectures for panoptic pig segmentation, which is a combination of semantic segmentation (assigning a class label to each pixel) and instance segmentation (detecting and segmenting each object instance), while the results are used for posture detection.…”

Section: Related Workmentioning

confidence: 99%

Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

2021

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The analysis and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem of recognition of animal poses, which has numerous applications in zoology, ecology, biology, and entertainment. We propose a methodology to recognize dog poses. The methodology includes the extraction of frames for labeling from videos and deep convolutional neural network (CNN) training for pose recognition. We employ a semi-supervised deep learning model of reinforcement. During training, we used a combination of restricted labeled data and a large amount of unlabeled data. Sequential CNN is also used for feature localization and to find the canine’s motions and posture for spatio-temporal analysis. To detect the canine’s features, we employ image frames to locate the annotations and estimate the dog posture. As a result of this process, we avoid starting from scratch with the feature model and reduce the need for a large dataset. We present the results of experiments on a dataset of more than 5000 images of dogs in different poses. We demonstrated the effectiveness of the proposed methodology for images of canine animals in various poses and behavior. The methodology implemented as a mobile app that can be used for animal tracking.

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“…Machine Learning can be applied to solve complex problems efficiently (Said and Erradi, 2019), (Abdelhedi et al, 2020), (Ayadi et al, 2020), (Jabbar et al, 2018). In this study, random forest (RF), a widely used machine learning technique (Wainberg, Alipanahi and Frey, 2016) is used.…”

Section: Feature Importance Analysismentioning

confidence: 99%

Transition to Sustainable Smart Energy: An analysis of human- building interactions in Qatar Using Machine Learning Techniques

Jabbar¹,

Zaidan²,

Said³

et al. 2021

Proceedings of the 57th ISOCARP World Planning Congress

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Policy Planning have the potential to contribute to the strategic development and economic diversification of developing countries even without considerable structural changes. In this study, we analysed a set of humanoriented dimensions aimed at improving energy policies related to the building sector in Qatar. Considering the high percentage of expatriate and migrant communities with different financial and cultural backgrounds and behavioral patterns compared with local communities in the GCC Union, it is required to investigate human dimensions to propose adequate energy policies. This study explored the correlations of socioeconomic, behavioral, and demographic dimensions to determine the main factors behind discrepancies in energy use, responsibilities, motivations, habits, and overall well-being. The sample included 2,200 people in Qatar, and it was clustered into two consumer categories: high and low. In particular, the study focused on exploring human indoor comfort perception dependencies with building features. Financial drivers, such as demand programs and energy subsidies, were explored in relation to behavioral patterns. Subsequently, the data analysis resulted in implications for energy policies regarding interventions, social well-being, and awareness. Machine learning methods were used to perform a feature importance analysis to determine the main factors of human behavior. The findings of this study demonstrated how human factors impact comfort perception in residential and work environments, norms, habits, self-responsibility, consequence awareness, and consumption. The study has important implications for developing targeted strategies aimed at improving the efficacy of energy policies and sustainability performance indicators.

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Dairy Cow Rumination Detection: A Deep Learning Approach

Cited by 15 publications

References 45 publications

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

Transition to Sustainable Smart Energy: An analysis of human- building interactions in Qatar Using Machine Learning Techniques

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