Pig Identification Using Deep Convolutional Neural Network Based on Different Age Range

Sihalath, Thavisack; Basak, Jayanta Kumar; Bhujel, Anil; Arulmozhi, Elanchezhıan; Moon, Byeong Eun; Kim, Hyeon Tae

doi:10.1007/s42853-021-00098-7

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…The information about the supply and demand of agricultural products has become more complicated. Effective forecasting of agricultural production can better enable agrarian producers to understand information about agricultural production, take appropriate measures promptly for possible problems in the process of the farm output and provide solutions suitable for the development of the farm economy regarding agricultural production inputs, thus promoting the smooth operation of the farm market [ 2 ]. In rational planning of agricultural production, it is necessary to quantitatively analyze the main factors affecting agricultural production and predict the trend of the farm output.…”

Section: Introductionmentioning

confidence: 99%

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Ji-yu

2022

Computational Intelligence and Neuroscience

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In the vision of smart cities, everything is highly connected with the aid of computational intelligence. Therefore, the cyber-physical society has been named a computational social system for a long time. Due to the high relation with vast populations’ national livelihood, agriculture will still serve as a core industry in the national economy. As a result, this study focused on an efficient forecasting method for the agriculture economy. In recent years, the conception of deep intelligence has received overall prevalence in academia because of its excellent performance in implementing intelligent information processing tasks. Hence, this paper utilized deep intelligence driven by neural networks and managed to investigate an efficient prediction method for the agriculture economy of computational social systems. To fit the time-series forecasting scene of the long-term development of the agriculture economy, the convolutional neural network model is slightly improved by revising its parallel structure into the recurrent format. Finally, simulations on realistic datasets are carried out to evaluate the proposed forecasting method.

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Section: Introductionmentioning

confidence: 99%

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Ji-yu

2022

Computational Intelligence and Neuroscience

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“…high performance in estimating the outcome of ANN modelling compared to the MLR methods for capturing the highly nonlinear and complex relationship between output and input variables has been reported in different studies (Jaihuni et al, 2022;Sihalath et al, 2021).…”

Section: Applications Of Artificial Neural Network and Multiple Linea...mentioning

confidence: 90%

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Basak¹,

Paudel²,

Deb³

et al. 2023

IJAR

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Background: Experiments were conducted to analyze environmental parameters and growth-related factors to identify the most influential factors in estimating pig’s body weight (PBW) using artificial neural networks (ANNs) and multiple linear regression (MLR)-based models. Methods: Four different ANNs, including Cascade Forward Back-propagation (CFBP), Layer Recurrent (LR), Feed Forward Back-propagation (FFBP) and Elman (EL), and MLR models were developed to estimate the body weight of pigs. The current research was conducted for 92 days during the two experimental periods (2021–2022) in a pig barn. Result: The Levenberg-Marquardt training function, gradient descent weight and bias learning function, tan-sigmoid transfer function, and two hidden layers with 16 neurons in each layer were shown to be the most effective architecture of the FFBP model in predicting PBW. According to the sensitivity analysis, length of pig (LP) was the most influential factor in estimating the PBW for MLR/ANN models. However, the environmental parameters along with growth-related factors could not always be the same association with PBW. Therefore, further research on viable alternative breeds with different management conditions may be considered to evaluate MLR and ANN model’s performance.

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“…Frontal pig face images were collected and a convolutional neural network (CNN) pig face recognition model with higher accuracy was established in comparison with Fisherfaces and VGG-Face algorithms [16]. A method for automatic screening of pig face images was explored [17], and a pig face recognition model for different growth stages was established [18]. To further improve the performance of pig face recognition models based on deep learning, most of the current research has focused on optimizing deep learning algorithms to improve the accuracy of pig face recognition and reducing the number of parameters of the models [19,20].…”

Section: Introductionmentioning

confidence: 99%

Individual Pig Identification Using Back Surface Point Clouds in 3D Vision

Zhou

Xie

2023

Sensors

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The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig’s back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig’s back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.

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Pig Identification Using Deep Convolutional Neural Network Based on Different Age Range

Cited by 9 publications

References 24 publications

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Deep Intelligence-Driven Efficient Forecasting for the Agriculture Economy of Computational Social Systems

Applications of artificial neural networks and multiple linear regression algorithms in modelling of pig’s body weight

Individual Pig Identification Using Back Surface Point Clouds in 3D Vision

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