Cow Rump Identification Based on Lightweight Convolutional Neural Networks

Hou, Handan; Shi, Wei; Guo, Jinyan; Zhang, Zhe; Shen, Weizheng; Kou, Shengli

doi:10.3390/info12090361

Cited by 5 publications

(4 citation statements)

References 27 publications

(29 reference statements)

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“…The method of identifying cows in this study was compared with the methods proposed by other researchers studying cows and the results are shown in Table 4 . The literature [ 27 ] achieved recognition of cow rump by fine-tuning mobilenet, and the final recognition accuracy was 99.76%, but the mod size was 9.25 MB, which was more than two times larger than our model. The literature [ 28 ] uses ReXNet 3D for cow behavior recognition and its model size is 14.3 MB with low accuracy, which is 10.69 MB larger than our model, in addition, its FLOPs are 15.8 G, which is also much larger than our model.…”

Section: Resultsmentioning

confidence: 77%

Lightweight individual cow identification based on Ghost combined with attention mechanism

Kong

et al. 2022

PLoS ONE

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Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and practical advantages. In this study, a method based on the combination of Ghost and attention mechanism is proposed to improve ReNet50 to achieve non-contact individual recognition of cows. In the model, coarse-grained features of cows are extracted using a large sensory field of cavity convolution, while reducing the number of model parameters to some extent. ResNet50 consists of two Bottlenecks with different structures, and a plug-and-play Ghost module is inserted between the two Bottlenecks to reduce the number of parameters and computation of the model using common linear operations without reducing the feature map. In addition, the convolutional block attention module (CBAM) is introduced after each stage of the model to help the model to give different weights to each part of the input and extract the more critical and important information. In our experiments, a total of 13 cows’ side view images were collected to train the model, and the final recognition accuracy of the model was 98.58%, which was 4.8 percentage points better than the recognition accuracy of the original ResNet50, the number of model parameters was reduced by 24.85 times, and the model size was only 3.61 MB. In addition, to verify the validity of the model, it is compared with other networks and the results show that our model has good robustness. This research overcomes the shortcomings of traditional recognition methods that require human extraction of features, and provides theoretical references for further animal recognition.

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Section: Resultsmentioning

confidence: 77%

Lightweight individual cow identification based on Ghost combined with attention mechanism

Kong

et al. 2022

PLoS ONE

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“…Currently, individual livestock identification is mainly divided into two categories: closed-set identification and open-set identification. In the realm of closed-set identification, existing research primarily focuses on individual identification based on parts of the cow such as the face [17], back [13], side [12,14], and rear [20] using various models including EfficientNet-B1, ResNet50, RetinaFaceNet, VGG-16+SVM, and MobileNet V2. These methods have achieved high recognition accuracy within fixed, predefined sets of individual cows.…”

Section: Comparison With Existing Studiesmentioning

confidence: 99%

“…Other researchers have also proposed various methods for cattle facial recognition [17,18]. Additionally, some studies focus on identifying individuals based on other body parts of cattle [19,20] and on model lightweighting [21][22][23]. However, whether based on the cow's back, face, or other body parts, most studies on individual dairy cow recognition focus on closed-set identification, which only recognizes individuals within the training set.…”

Section: Introductionmentioning

confidence: 99%

Open-Set Recognition of Individual Cows Based on Spatial Feature Transformation and Metric Learning

Wang,

Li,

et al. 2024

Animals

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The automated recognition of individual cows is foundational for implementing intelligent farming. Traditional methods of individual cow recognition from an overhead perspective primarily rely on singular back features and perform poorly for cows with diverse orientation distributions and partial body visibility in the frame. This study proposes an open-set method for individual cow recognition based on spatial feature transformation and metric learning to address these issues. Initially, a spatial transformation deep feature extraction module, ResSTN, which incorporates preprocessing techniques, was designed to effectively address the low recognition rate caused by the diverse orientation distribution of individual cows. Subsequently, by constructing an open-set recognition framework that integrates three attention mechanisms, four loss functions, and four distance metric methods and exploring the impact of each component on recognition performance, this study achieves refined and optimized model configurations. Lastly, introducing moderate cropping and random occlusion strategies during the data-loading phase enhances the model’s ability to recognize partially visible individuals. The method proposed in this study achieves a recognition accuracy of 94.58% in open-set scenarios for individual cows in overhead images, with an average accuracy improvement of 2.98 percentage points for cows with diverse orientation distributions, and also demonstrates an improved recognition performance for partially visible and randomly occluded individual cows. This validates the effectiveness of the proposed method in open-set recognition, showing significant potential for application in precision cattle farming management.

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“…Over the past two decades, there has been notable progress in the development of modern data collection methods and advanced Internet of Things (IoT) technologies within the global livestock industry. However, challenges remain, particularly in the areas of intrinsic data analysis capabilities and animal welfare concerns [6,[8][9][10][11]. Dynamically perceiving the body measurement data of pigs is of utmost importance.…”

Section: Introductionmentioning

confidence: 99%

Research and Preliminary Evaluation of Key Technologies for 3D Reconstruction of Pig Bodies Based on 3D Point Clouds

Lei,

Tang,

et al. 2024

Agriculture

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In precision livestock farming, the non-contact perception of live pig body measurement data is a critical technological branch that can significantly enhance breeding efficiency, improve animal welfare, and effectively prevent and control diseases. Monitoring pig body measurements allows for accurate assessment of their growth and production performance. Currently, traditional sensing methods rely heavily on manual measurements, which not only have large errors and high workloads but also may cause stress responses in pigs, increasing the risk of African swine fever, and its costs of prevention and control. Therefore, we integrated and developed a system based on a 3D reconstruction model that includes the following contributions: 1. We developed a non-contact system for perceiving pig body measurements using a depth camera. This system, tailored to the specific needs of laboratory and on-site pig farming processes, can accurately acquire pig body data while avoiding stress and considering animal welfare. 2. Data preprocessing was performed using Gaussian filtering, mean filtering, and median filtering, followed by effective estimation of normals using methods such as least squares, principal component analysis (PCA), and random sample consensus (RANSAC). These steps enhance the quality and efficiency of point cloud processing, ensuring the reliability of 3D reconstruction tasks. 3. Experimental evidence showed that the use of the RANSAC method can significantly speed up 3D reconstruction, effectively reconstructing smooth surfaces of pigs. 4. For the acquisition of smooth surfaces in 3D reconstruction, experimental evidence demonstrated that the RANSAC method significantly improves the speed of reconstruction. 5. Experimental results indicated that the relative errors for chest girth and hip width were 3.55% and 2.83%, respectively. Faced with complex pigsty application scenarios, the technology we provided can effectively perceive pig body measurement data, meeting the needs of modern production.

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Cow Rump Identification Based on Lightweight Convolutional Neural Networks

Cited by 5 publications

References 27 publications

Lightweight individual cow identification based on Ghost combined with attention mechanism

Lightweight individual cow identification based on Ghost combined with attention mechanism

Open-Set Recognition of Individual Cows Based on Spatial Feature Transformation and Metric Learning

Research and Preliminary Evaluation of Key Technologies for 3D Reconstruction of Pig Bodies Based on 3D Point Clouds

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