scholarly journals ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

2021 ◽  
Author(s):  
Suresh Neethirajan
Keyword(s):  
Continuous Monitoring ◽  
Computer Software ◽  
Animal Husbandry ◽  
Digital Tool ◽  
Animal Farming ◽  
Lighting Conditions ◽  
Proposed Model ◽  
On Farm ◽  
Deep Learning Model ◽  
Methodological Difficulties

<p>The automatic detection, counting and tracking of individual and flocked chickens in the poultry industry is of paramount to enhance farming productivity and animal welfare. Due to methodological difficulties, such as the complex background of images, varying lighting conditions, and occlusions from e.g., feeding stations, water nipple stations and barriers in the chicken rearing production floor, it is a challenging task to automatically recognize and track birds using computer software. Here, a deep learning model based on You Only Look Once (Yolov5) is proposed for detecting domesticated chickens from videos with varying complex backgrounds. A multiscale feature is being adapted to the Yolov5 network for mapping modules in the counting and tracking of the trajectories of the chickens. The Yolov5 network was trained and tested on our dataset which resulted in an enhanced tracking precision accuracy. Using Kalman Filter, the proposed model was able to track multiple chickens simultaneously with the focus to associate individual chickens across the frames of the video for real time and online applications. By being able to detect the chickens amid diverse background interference and counting them precisely along with tracking the movement and measuring their travelled path and direction, the proposed model provides excellent performance for on-farm applications. Artificial intelligence enabled automatic measurements of chicken behavior on-farm using cameras offers continuous monitoring of the chicken's ability to perch, walk, interact with other birds and the farm environment, as well as the assessment of dustbathing, thigmotaxis, and foraging frequency, which are important indicators for their ability to express natural behaviors. This study highlights the potential of automated monitoring of poultry through the usage of ChickTrack model as a digital tool in enabling science-based animal husbandry practices and thereby promote positive welfare for chickens in animal farming. </p>

scholarly journals ChickTrack - A Quantitative Tracking Tool for Measuring Chicken Activity

2021 ◽  
Author(s):  
Suresh Neethirajan
Keyword(s):  
Continuous Monitoring ◽  
Computer Software ◽  
Animal Husbandry ◽  
Digital Tool ◽  
Animal Farming ◽  
Lighting Conditions ◽  
Proposed Model ◽  
On Farm ◽  
Deep Learning Model ◽  
Methodological Difficulties

<p>The automatic detection, counting and tracking of individual and flocked chickens in the poultry industry is of paramount to enhance farming productivity and animal welfare. Due to methodological difficulties, such as the complex background of images, varying lighting conditions, and occlusions from e.g., feeding stations, water nipple stations and barriers in the chicken rearing production floor, it is a challenging task to automatically recognize and track birds using computer software. Here, a deep learning model based on You Only Look Once (Yolov5) is proposed for detecting domesticated chickens from videos with varying complex backgrounds. A multiscale feature is being adapted to the Yolov5 network for mapping modules in the counting and tracking of the trajectories of the chickens. The Yolov5 network was trained and tested on our dataset which resulted in an enhanced tracking precision accuracy. Using Kalman Filter, the proposed model was able to track multiple chickens simultaneously with the focus to associate individual chickens across the frames of the video for real time and online applications. By being able to detect the chickens amid diverse background interference and counting them precisely along with tracking the movement and measuring their travelled path and direction, the proposed model provides excellent performance for on-farm applications. Artificial intelligence enabled automatic measurements of chicken behavior on-farm using cameras offers continuous monitoring of the chicken's ability to perch, walk, interact with other birds and the farm environment, as well as the assessment of dustbathing, thigmotaxis, and foraging frequency, which are important indicators for their ability to express natural behaviors. This study highlights the potential of automated monitoring of poultry through the usage of ChickTrack model as a digital tool in enabling science-based animal husbandry practices and thereby promote positive welfare for chickens in animal farming. </p>

scholarly journals Llama and Alpaca Management in Germany—Results of an Online Survey among Owners on Farm Structure, Health Problems and Self-Reflection

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 102
Author(s):  
Saskia Neubert ◽  
Alexandra von Altrock ◽  
Michael Wendt ◽  
Matthias Gerhard Wagener
Keyword(s):  
Online Survey ◽  
Animal Husbandry ◽  
Health Problems ◽  
South American ◽  
Wool Production ◽  
Self Reflection ◽  
South American Camelids ◽  
Feeding Management ◽  
Management Measures ◽  
On Farm

An online survey of llama and alpaca owners was used to collect data on the population, husbandry, feeding, management measures and health problems. A total of 255 questionnaires were evaluated. In total, 55.1% of the owners had started keeping South American camelids within the last six years. The majority managed small farms with fewer than 15 animals (66.4% of 250 farms). More than half of the participants stated that they kept the camelids as hobby animals (64.3%), although they used them for wool production (55.7%) and/or for breeding (51.8%). Vaccination and deworming were carried out on more than 80% of the farms. The most common diseases occurring on the farms were endo- and ectoparasites. A total of 55.3% of the owners estimated their own knowledge of South American camelids as good, 14.5% as very good. In contrast, more than half of the owners agreed little or not at all with the statement that veterinarians generally have sufficient knowledge about South American camelids. Further research is needed to include veterinarians’ perspectives and thus optimise animal husbandry.

scholarly journals Design of Cascaded CNN for Medical Image Super Resolution

2021 ◽  
Vol 7 (3) ◽  
pp. 22-29
Author(s):  
Kajol Singh ◽  
Manish Saxena
Keyword(s):  
Neural Networks ◽  
Medical Image ◽  
Research Work ◽  
Super Resolution ◽  
Primary Objective ◽  
Lighting Conditions ◽  
Proposed Model ◽  
Filter Size ◽  
Medical Image Reconstruction ◽  
Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.

scholarly journals An Enhanced Faster-RCNN Based Deep Learning Model for Crop Diseases Detection and Classification

2019 ◽  
Vol 8 (6) ◽  
pp. 4714-4719
Keyword(s):  
Neural Network ◽  
Vital Role ◽  
Convolution Neural Network ◽  
Plant Phenotyping ◽  
Early Disease ◽  
Early Disease Detection ◽  
Proposed Model ◽  
Crop Diseases ◽  
Deep Learning Model ◽  
State Of Art

Recently Plant phenotyping has gained the attention of many researchers such that it plays a vital role in the context of enhancing agricultural productivity. Indian economy highly depends on agriculture and this factor elevates the importance of early disease detection of the crops within the agricultural fields. Addressing this problem several researchers have proposed Computer Vision and Pattern recognition based mechanisms through which they have attempted to identify the infected crops in the early stages.in this scenario, CNN convolution neural network-based architecture has demonstrated exceptional performance when compared with state-of-art mechanisms. This paper introduces an enhanced RCNN recurrent convolution neural network-based architecture that enhances the prediction accuracy while detecting the crop diseases in early stages. Based on the simulative studies is observed that the proposed model outperforms when compared with CNN and other state-of-art mechanisms.

Automated monitoring and assessment of productivity in earthmoving projects

2018 ◽  
Vol 45 (11) ◽  
pp. 958-972 ◽  
Author(s):  
Ashraf Salem ◽  
Osama Moselhi
Keyword(s):  
Data Fusion ◽  
Continuous Monitoring ◽  
Research Work ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Cost Overruns ◽  
Knowledge Based ◽  
Monitoring Model ◽  
Multi Sensor Data Fusion ◽  
Proposed Model

Continuous monitoring of productivity and assessment of its variations are crucial processes that significantly contribute to success of earthmoving projects. Numerous factors may lead to productivity variations. However, these factors are subjectively identified using manual knowledge-based expert judgment. Such manual recognition process is not only subject to errors but also time-consuming. There is a lack of research work that focuses on near real-time assessment of productivity variation and its effect on cost, schedule and effective utilization of resources in earthmoving projects. This paper presents a customized multi-source automated data acquisition model that acquires data from a variety of wireless sensing technologies. The acquired multi-sensor data are transmitted to a central MySQL database. Then a newly developed data fusion algorithm is applied for truck state recognition, and hence the duration of each earthmoving state. Multi-sensor data fusion facilitates measurement of actual productivity, and consequently the assessment of productivity ratios that support continuous monitoring of productivity variation in earthmoving operations. The developed tracking and monitoring model generates an early warning that supports proactive decisions to avoid schedule delays, cost overruns, and inefficient depletion of resources. A case study is used to reveal the applicability of the proposed model in monitoring and assessing actual productivity and its deviations from planned productivity. Finally, results are discussed and conclusions are drawn highlighting the features of the proposed model.

Optimally organized GRU-deep learning model with Chi2 feature selection for heart disease prediction

2021 ◽  
pp. 1-12
Author(s):  
Irfan Javid ◽  
Ahmed Khalaf Zager Alsaedi ◽  
Rozaida Binti Ghazali ◽  
Yana Mazwin ◽  
Muhammad Zulqarnain
Keyword(s):  
Heart Disease ◽  
Search Strategy ◽  
Medical Practitioner ◽  
Training Data ◽  
Hybrid Technique ◽  
Network Configuration ◽  
Proposed Model ◽  
Selection For ◽  
Gated Recurrent Unit ◽  
Deep Learning Model

In previous studies, various machine-driven decision support systems based on recurrent neural networks (RNN) were ordinarily projected for the detection of cardiovascular disease. However, the majority of these approaches are restricted to feature preprocessing. In this paper, we concentrate on both, including, feature refinement and the removal of the predictive model’s problems, e.g., underfitting and overfitting. By evading overfitting and underfitting, the model will demonstrate good enactment on equally the training and testing datasets. Overfitting the training data is often triggered by inadequate network configuration and inappropriate features. We advocate using Chi2 statistical model to remove irrelevant features when searching for the best-configured gated recurrent unit (GRU) using an exhaustive search strategy. The suggested hybrid technique, called Chi2 GRU, is tested against traditional ANN and GRU models, as well as different progressive machine learning models and antecedently revealed strategies for cardiopathy prediction. The prediction accuracy of proposed model is 92.17% . In contrast to formerly stated approaches, the obtained outcomes are promising. The study’s results indicate that medical practitioner will use the proposed diagnostic method to reliably predict heart disease.

Security enabled UAVs for Tech-Agriculture monitoring rice crops using FIBOR architecture

2021 ◽  
pp. 119-126
Author(s):  
J V N Lakshmi
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Continuous Monitoring ◽  
Security And Privacy ◽  
Security Model ◽  
Indian Agriculture ◽  
Collaborative Environment ◽  
Design Flexibility ◽  
Proposed Model ◽  
Farming Industry ◽  
Technical Collaboration

Unmanned Aerial Vehicles usage has significantly improved in all the sectors. Various industries are using drones as a platform for development with eco- nomic investment. Drastic advancement in design, flexibility, equipment and technical improvements has a great impact in creating airborne domain of IoT. Hence, drones have become a part of farming industry. Indian agriculture economy concentrates more on producing rice as this is considered as a staple food in various states. For increasing the production of rice sensors are equipped in the fields to track the water supply and humidity components. Whereas, identifying weeds, early stages of disease detection, recognizing failed crops, spraying fertilizers and continuous monitoring from bleats, locust and other dangerous insects are some of the technical collaboration with UAVs with respect farming sector. However, use of UAVs in real time environment involves many security and privacy challenges. In order to preserve UAVs from external vulnerabilities and hacking the collaborative environment requires a tough security model. In this proposed article a framework is implemented applying FIBOR security model on UAVs to suppress the threats from data hackers and protect the data in cloud from attackers. This proposed model enabled with drone technology provides a secured framework and also improves the crop yield by 15% by adapting a controlled network environment.

3D U-Net: A voxel-based method in binding site prediction of protein structure

2021 ◽  
Vol 19 (02) ◽  
pp. 2150006
Author(s):  
Fatemeh Nazem ◽  
Fahimeh Ghasemi ◽  
Afshin Fassihi ◽  
Alireza Mehri Dehnavi
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Binding Sites ◽  
Semantic Segmentation ◽  
Structure Based Drug Design ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Novel Proteins ◽  
Proposed Model ◽  
Deep Learning Model

Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.

Multistep Flow Prediction on Car-Sharing Systems: A Multi-Graph Convolutional Neural Network with Attention Mechanism

2019 ◽  
Vol 29 (11n12) ◽  
pp. 1727-1740 ◽  
Author(s):  
Hongming Zhu ◽  
Yi Luo ◽  
Qin Liu ◽  
Hongfei Fan ◽  
Tianyou Song ◽  
...  
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Spatial Relations ◽  
Attention Mechanism ◽  
Temporal Relations ◽  
Car Sharing ◽  
Flow Prediction ◽  
Proposed Model ◽  
Decoder Architecture ◽  
Deep Learning Model

Multistep flow prediction is an essential task for the car-sharing systems. An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users. However, this task is challenging due to the complex spatial and temporal relations among stations. Existing works only considered temporal relations (e.g. using LSTM) or spatial relations (e.g. using CNN) independently. In this paper, we propose an attention to multi-graph convolutional sequence-to-sequence model (AMGC-Seq2Seq), which is a novel deep learning model for multistep flow prediction. The proposed model uses the encoder–decoder architecture, wherein the encoder part, spatial and temporal relations are encoded simultaneously. Then the encoded information is passed to the decoder to generate multistep outputs. In this work, specific multiple graphs are constructed to reflect spatial relations from different aspects, and we model them by using the proposed multi-graph convolution. Attention mechanism is also used to capture the important relations from previous information. Experiments on a large-scale real-world car-sharing dataset demonstrate the effectiveness of our approach over state-of-the-art methods.

scholarly journals On-farm welfare monitoring system for goats based on Internet of Things and machine learning

2020 ◽  
Vol 16 (7) ◽  
pp. 155014772094403
Author(s):  
Yuan Rao ◽  
Min Jiang ◽  
Wen Wang ◽  
Wu Zhang ◽  
Ruchuan Wang
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Monitoring System ◽  
Animal Husbandry ◽  
Environmental Data ◽  
Software Systems ◽  
Data Detection ◽  
Livestock Farming ◽  
On Farm

Intensive animal husbandry is becoming more and more popular with the adoption of modern livestock farming technologies. In such circumstances, it is required that the welfare of animals be continuously monitored in a real-time way. To this end, this study describes one on-farm welfare monitoring system for goats, with a combination of Internet of Things and machine learning. First, the system was designed for uninterruptedly monitoring goat growth in a multifaceted and multilevel manner, by means of collecting on-farm videos and representative environmental data. Second, the monitoring hardware and software systems were presented in detail, aiming at supporting remote operation and maintenance, and convenience for further development. Third, several key approaches were put forward, including goat behavior analysis, anomaly data detection, and processing based on machine learning. Through practical deployment in the real situation, it was demonstrated that the developed system performed well and had good potential for offering real-time monitoring service for goats’ welfare, with the help of accurate environmental data and analysis of goat behavior.

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