Recognition Of Cattle Research Articles

Analysing Deep Learning and Machine learning Model for Cattle Recognition

In machine learning, feature extraction is a very important step in the construction of any pattern classification that extracts relevant features to identify the class from group of images. To recognizing object, accuracy depends upon the quality of features extracted from an image. Unique feature extraction has high accuracy in recognizing classes. In Deep learning image recognition is based on such as strong feature extraction ability and high recognition accuracy. In this paper we have discussed all the approaches used to recognize cattle from traditional to deep learning approaches, we have also analyzed the comparison of machine and deep learning approaches. We tried to explore few models of deep learning and its architecture that may result with high accuracy.

A novel Jinnan individual cattle recognition approach based on mutual attention learning scheme

The accurate recognition of individual Jinnan cattle is crucial for efficient breeding. However, existing ear tagging and machine learning approaches are inefficient, and deep learning models with a significant number of parameters can be computationally expensive. To address these challenges, this study proposes an innovative mutual-attention learning method for efficiently recognizing individual Jinnan cattle. This method employs two attention-enhanced student networks that collaborate and exchange knowledge on cross-dimensional attention during training, achieving improved recognition performance compared to counterparts without mutual learning. Additionally, smaller student networks can achieve improved recognition performance with much less computational cost, leveraging knowledge from larger networks. Furthermore, the proposed approach integrates attention modules such as block attention module (BAM), convolutional BAM (CBAM), and triplet attention units to capture spatial and channel attention for obtaining robust feature representations, enhancing performance. Our proposed model achieves state-of-the-art classification accuracy of 98.91%, surpassing other models in both accuracy and efficiency. Experiments also demonstrate its superiority by improving accuracy by over 2.97% and reducing loss value by over 50% compared to existing methods. Overall, the proposed approach has great potential to revolutionize the recognition of individual Jinnan cattle.

Intelligent identification of livestock, a source of Schistosoma japonicum infection, based on deep learning of unmanned aerial vehicle images

To develop an intelligent recognition model based on deep learning algorithms of unmanned aerial vehicle (UAV) images, and to preliminarily explore the value of this model for remote identification, monitoring and management of cattle, a source of Schistosoma japonicum infection. Oncomelania hupensis snail-infested marshlands around the Poyang Lake area were selected as the study area. Image datasets of the study area were captured by aerial photography with UAV and subjected to augmentation. Cattle in the sample database were annotated with the annotation software VGG Image Annotator to create the morphological recognition labels for cattle. A model was created for intelligent recognition of livestock based on deep learning-based Mask R-convolutional neural network (CNN) algorithms. The performance of the model for cattle recognition was evaluated with accuracy, precision, recall, F1 score and mean precision. A total of 200 original UAV images were obtained, and 410 images were yielded following data augmentation. A total of 2 860 training samples of cattle recognition were labeled. The created deep learning-based Mask R-CNN model converged following 200 iterations, with an accuracy of 88.01%, precision of 92.33%, recall of 94.06%, F1 score of 93.19%, and mean precision of 92.27%, and the model was effective to detect and segment the morphological features of cattle. The deep learning-based Mask R-CNN model is highly accurate for recognition of cattle based on UAV images, which is feasible for remote intelligent recognition, monitoring, and management of the source of S. japonicum infection.

Eidetic recognition of cattle using keypoint alignment

Ability to identify individual cows quickly and readily in the barn would enable real time monitoring of their behavior, health, eating habits and more, all of which could save time, money, or effort. This work focuses on creating an eidetic recognition or re-identification (ReID) algorithm that learns to recognize individual cows with just a single training example per cow and with near zero time to learn to identify a new cow, both features which the existing cattle ReID systems lack. Our algorithm is designed to improve recognition robustness to deformations in cow bodies that occur when they are walking, turning, or are seen slightly off-angle. Individual cows are first detected and localized using popular keypoint and mask detection techniques, then aligned to a fixed template, pixelated, binarized to reduce lighting effects, and serialized to obtain bit-vectors. Bit-vectors from cows at inference time are matched to those from training time using Hamming distance. To improve results, we add modules to verify the validity of detected keypoints, interpolate missing keypoints, and combine predictions from multiple frames using a majority vote. The video level accuracy is over 60% for a set of nearly 150 Holstein cows.

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Cattle Facial Matching Recognition Algorithm Based on Multi-View Feature Fusion

In the process of collecting facial images of cattle in the field, some features of the collected images end up going missing due to the changeable posture of the cattle, which makes the recognition accuracy decrease or impossible to recognize. This paper verifies the practical effects of the classical matching algorithms ORB, SURF, and SIFT in bull face matching recognition. The experimental results show that the traditional matching algorithms perform poorly in terms of matching accuracy and matching time. In this paper, a new matching recognition model is constructed. The model inputs the target cattle facial data from different angles into the feature extraction channel, combined with GMS (grid-based motion statistics) algorithm and random sampling consistent algorithm, to achieve accurate recognition of individual cattle, and the recognition process is simple and fast. The recognition accuracy of the model was 85.56% for the Holstein cow face dataset, 82.58% for the Simmental beef cattle, and 80.73% for the mixed Holstein and Simmental beef cattle dataset. The recognition model constructed in the study can achieve individual recognition of cattle in complex environments, has good robustness to matching data, and can effectively reduce the effects of data angle changes and partial features missing in cattle facial recognition.

Facial Recognition of Cattle Based on SK-ResNet

With the gradual increase of the scale of the breeding industry in recent years, the intelligence level of livestock breeding is also improving. Intelligent breeding is of great significance to the identification of livestock individuals. In this paper, the cattle face images are obtained from different angles to generate the cow face dataset, and a cow face recognition model based on SK_ResNet is proposed. Based on ResNet-50 and using a different number of sk_Bottleneck, this model integrates multiple receptive fields of information to extract facial features at multiple scales. The shortcut connection part connects to the maximum pooling layer to reduce information loss; the ELU activation function is used to reduce the vanishing gradient, prevent overfitting, accelerate the convergence speed, and improve the generalization ability of the model. The constructed bovine face dataset was used to train the SK-ResNet-based bovine face recognition model, and the accuracy rate was 98.42%. The method was tested on the public dataset and the self-built dataset. The accuracy rate of the model was 98.57% on the self-built pig face dataset and the public sheep face dataset. The accuracy rate was 97.02%. The experimental results verify the superiority of this method in practical application, which is helpful for the practical application of animal facial recognition technology in livestock.

A Vision-Based Cattle Recognition System Using TensorFlow for Livestock Water Intake Monitoring

This letter presents a new method for identifying individual cattle for the purpose of tracking and efficiently measuring their daily water intake using a vision-based machine learning system. In addition to the current solution of using radio frequency identification (RFID), the proposed system uses TensorFlow Object Detection to detect labels on the RFID tag. The proposed system can be integrated into the current water intake monitoring system and alleviates the errors introduced by the unreliable RFID readers. The system allows users to train an object recognition model that can recognize and differentiate the labels on the ear tags of individual farm animals, so the drinking events can be recorded by the water intake monitoring system. The models are trained using custom datasets of manually annotated tag images with pretrained model architectures from TensorFlow 2 Model Zoo. The system is tested using images from event-triggered weather-proof cameras deployed in the grazing site. Experimental results of the system showed an accuracy of around 90%. In comparison to other present methods, this newly proposed system provides scalability and flexibility, making it an attractive vision-based solution for machine learning systems in agriculture.

Intelligent Cattle Detection and Recognition System Using ANN-Fourier Descriptor Techniques

Reoccurring Fulani-farmer crisis in Nigeria as a result of the destruction of farmland and arable crops by cattle during grazing has led to a high rate of loss of life and properties. To address this problem, this research work proposed a shape- based ANN-Fourier descriptor cattle recognition system. Twenty samples of each of cattle, deer, dog, elephant, horse, and camel were obtained from the UCL database. Colour space conversion and a simple image binarization were performed to obtained image segmentation. Edges of the images were extracted, and Discrete Fourier Transform was applied on the edges to obtain the Fourier descriptors. Ten Fourier descriptors were used to train feed-forward back propagation artificial neural networks for cattle recognition systems. The effect of increasing database size, the number of images used for training and testing, and threshold value were investigated. It was observed that there was no effect of increasing the size of the database on the system performance. For better accuracy, sensitivity, specificity, and precision, it was observed that a good threshold value must be carefully chosen and 75% of the image must be used for training and 25% for testing respectively. With twenty image samples, each for cattle, deer, camel, dog, horse, and elephant in the database, fifteen out of twenty samples were used for training, five for testing, and the threshold value of 0.6, 98.7% sensitivity, 98.8% specificity, 98.8% accuracy and 94.5% precision were achieved. This result provides a good method for cattle detection and recognition system.

Secure and Sustainable Framework for Cattle Recognition Using Wireless Multimedia Networks and Machine Learning Techniques

Towards the successful operation of any farm, effective livestock management is necessary. The efficiency, affordability, and scalability of management solutions play a crucial role in modern farming. In this paper, a novel low-cost framework is proposed to recognize for health monitoring of individual cattle based on their sensed behavioral activity data and muzzle (nose) pattern image database using incremental decision tree classification techniques and accelerometer-based activity monitoring method. The proposed system performs image prepossessing on the captured muzzle point images of cattle to mitigate the noise, enhance the contrast, and increase quality. We extract the minutiae features to improve the system’s accuracy for recognizing the cattle for accurate and early classification of behavioral activity for health monitoring. The proposed system uses a support vector machine and incremental decision tree classifiers to classify the extracted feature of cattle’s muzzle images and health database. The server has encrypted databases that consist of captured muzzle images, owner database, health sensor database provided by the owners and systems. We use a similarity score measurement using incremental decision tree classification for matching and classify the muzzle point featutes with the database for cattle identification and health monitoring. We also developed a prototype for evaluating the accuracy of the proposed system with 97.99% accuarcy for unique identification of individual cattle.

Recognition of Cattle's Feeding Behaviors Using Noseband Pressure Sensor With Machine Learning.

Automatic monitoring of feeding behavior especially rumination and eating in cattle is important to keep track of animal health and growth condition and disease warnings. The noseband pressure sensor is not only able to accurately sense the pressure change of the cattle's jaw movements, which can directly reflect the cattle's chewing behavior, but also has strong resistance to interference. However, it is difficult to keep the same initial pressure while wearing the pressure sensor, and this will pose a challenge to process the feeding behavior data. This article proposed a machine learning approach aiming at eliminating the influence of initial pressure on the identification of rumination and eating behaviors. The method mainly used the local slope to obtain the local data variation and combined Fast Fourier Transform (FFT) to extract the frequency-domain features. Extreme Gradient Boosting Algorithm (XGB) was performed to classify the features of rumination and eating behaviors. Experimental results showed that the local slope in combination with frequency-domain features achieved an F1 score of 0.96, and recognition accuracy of 0.966 in both rumination and eating behaviors. Combined with the commonly used data processing algorithms and time-domain feature extraction method, the proposed approach improved the behavior recognition accuracy. This work will contribute to the standardized application and promotion of the noseband pressure sensors.

Detection, identification and posture recognition of cattle with satellites, aerial photography and UAVs using deep learning techniques

ABSTRACT To obtain specific information about cattle in extensive production systems, the usual labor intensive work done by the farmer to find and visit cattle herds in large pastures can be replaced by using UAVs. UAVs are capable of assessing traits in cows, like distinguishing individuals and postures. Although these traits and the detection of cattle, do not represent resilience and efficiency directly, these may contain information associated to resilience. We performed a feasibility study of remotely sensed imagery (using datasets from satellites, manned aircrafts, and UAVs), and deep learning techniques to detect, count, identify and characterize posture of individual cows in grassland production systems. With these techniques, we focused on : (1) automatic detection of cattle locations and animal counting; (2) cow postures like standing, grazing or lying; and (3) individual cow identification. Data were collected during three field trials in the Netherlands and Poland. Artificial Intelligence was used to classify the objects (cattle) in the drone imagery. Classification accuracies of >95% were obtained for detecting cows. Accuracies of ~91% were obtained for identifying individual cows, and accuracies of ~88% were obtained for cow postures. These results make camera-mounted drones a promising new technology for monitoring extensive beef production systems.

Toward an Intelligent System for Taurine Cattle Recognition

Unlike zebus, taurine cattle have the natural ability to resist trypanosomosis, a parasitic disease endemic to the humid areas of West Africa. However, repeated crossbreeding between zebus and taurine cattle is jeopardizing the genetic heritage of the Taurines and their ability to resist trypanosomosis. To strengthen protection and conservation efforts, it is essential to accurately distinguish purebred taurines from crossbreds. In this study, five Machine Learning models were built using morphological data collected from 1968 cattle. These models were trained to determine whether a given individual is purebred taurine or not. The classifiers yielded promising results. The random forest model and RBF Kernel SVM performed the best with up to 86% and 85% accuracy respectively. Moreover, the study of the correlation coefficients and the feature importance scores allowed us to define the most discriminating morphological traits.

CORF3D contour maps with application to Holstein cattle recognition from RGB and thermal images

Livestock management involves the monitoring of farm animals by tracking certain physiological and phenotypical characteristics over time. In the dairy industry, for instance, cattle are typically equipped with RFID ear tags. The corresponding data (e.g. milk properties) can then be automatically assigned to the respective cow when they enter the milking station. In order to move towards a more scalable, affordable, and welfare-friendly approach, automatic non-invasive solutions are more desirable. Thus, a non-invasive approach is proposed in this paper for the automatic identification of individual Holstein cattle from the side view while exiting a milking station. It considers input images from a thermal-RGB camera. The thermal images are used to delineate the cow from the background. Subsequently, any occluding rods from the milking station are removed and inpainted with the fast marching algorithm. Then, it extracts the RGB map of the segmented cattle along with a novel CORF3D contour map. The latter contains three contour maps extracted by the Combination of Receptive Fields (CORF) model with different strengths of push–pull inhibition. This mechanism suppresses noise in the form of grain type texture. The effectiveness of the proposed approach is demonstrated by means of experiments using a 5-fold and a leave-one day-out cross-validation on a new data set of 3694 images of 383 cows collected from the Dairy Campus in Leeuwarden (the Netherlands) over 9 days. In particular, when combining RGB and CORF3D maps by late fusion, an average accuracy of 99.64%(±0.13) was obtained for the 5-fold cross validation and 99.71%(±0.31) for the leave-one day-out experiment. The two maps were combined by first learning two ConvNet classification models, one for each type of map. The feature vectors in the two FC layers obtained from training images were then concatenated and used to learn a linear SVM classification model. In principle, the proposed approach with the novel CORF3D contour maps is suitable for various image classification applications, especially where grain type texture is a confounding variable.

Cattle Identification and Activity Recognition by Surveillance Camera

In cattle farm, it is important to monitor activity of cattle to know their health condition and prevent accidents. Sensors were used by conventional methods to recognize activity of cattle, but attachment of sensors to the animal may cause stress. Camera was used to recognize activity of cattle, but it is difficult to identify cattle because cattle have similar appearance, especially for black or brown cattle. We propose a new method to identify cattle and recognize their activity by surveillance camera. The cattle are recognized at first by CNN deep learning method. Face and body areas of cattle, sitting and standing state are recognized separately at same time. Image samples of day and night were collected for learning model to recognize cattle for 24-hours. Among the recognized cattle, initial ID numbers are set at first frame of the video to identify the animal. Then particle filter object tracking is used to track the cattle. Combing cattle recognition and tracking results, ID numbers of the cattle are kept to the following frames of the video. Cattle activity is recognized by using multi-frame of the video. In areas of face and body of cattle, active or static activities are recognized. Activity times for the areas are outputted as cattle activity recognition results. Cattle identification and activity recognition experiments were made in a cattle farm by wide angle surveillance cameras. Evaluation results demonstrate effectiveness of our proposed method.

Cattle Recognition: A New Frontier in Visual Animal Biometrics Research

Visual animal biometrics is an emerging research field of computer vision, pattern recognition, and cognitive science. Recently, cattle recognition has played a significant role in understanding, controlling and the outbreak of critical diseases, vaccination, production management, traceability, and ownership assignment of a livestock animal. The traditional animal recognition methodologies, such as ear-tagging, freeze-branding, ear-tattoos, embedded microchips, ear tips or notches-based, and electrical-based marking approaches, have been applied to recognize individual livestock animal. However, standard animal recognition procedures are invasive. The performance of conventional methods is not good due to their vulnerability to losses, easy duplication, and fraud of embedded tag number. These are major security issues and challenges for the identification of cattle throughout the world. Visual animal biometric systems are gaining more proliferations due to widespread applications to recognize individual cattle based on their primary biometric muzzle point image characteristics. This paper aims to provide a comprehensive review of cattle recognition and tracking from non-biometric recognition approaches (classical animal recognition methods) to visual animal biometric systems using muzzle point image pattern along with measurements and interpretations based on current state-of-the-art methods. Moreover, this paper demonstrates the basic deployment of the animal biometric system to uniquely identify the animals using their biometric characteristics. This study can hopefully encourage new multidisciplinary researchers and scientists to provide excellent efforts for the designing and development of adequate algorithms for solving the classification and recognition problems. The literature review is followed by the presentation of references for more details, incorporating applications and current trends.

Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images

The usage of computer vision adds a new paradigm in the field of animal biometric, and has recently received more attention due to the growing importance of identification and tracking of animal species or individual animals. Biometric characteristics help to develop a better representation and a better identification of different animal species and individual animals. In this work, we propose an effective approach for automatic cattle recognition based on the multiple features of muzzle points and the cattle face images. The proposed method deals the cattle recognition problem as a classification problem among the multiple linear regression models and provides a new theory for the recognition of individual cattle. The group sparse signal representation based classification offers the key to addressing this problem using L2-minimization. In this paper, a comparative study among the well-established handcrafted texture feature extraction techniques and the appearance-based feature extraction techniques is also presented. A detailed set of experimental results on muzzle point image database is also carried to prove the theory. Our method has achieved 93.87% identification accuracy which demonstrates the superiority of the proposed method than the other existing machine learning based recognition algorithms.

Deep learning framework for recognition of cattle using muzzle point image pattern

Animal biometrics is a frontier area of computer vision, pattern recognition and cognitive science to plays the vital role for the registration, unique identification, and verification of livestock (cattle). The existing handcrafted texture feature extraction and appearance based feature representation techniques are unable to perform the animal recognition in the unconstrained environment. Recently deep learning approaches have achieved more attention for recognition of species or individual animal using visual features. In this research, we propose the deep learning based approach for identification of individual cattle based on their primary muzzle point (nose pattern) image pattern characteristics to addressing the problem of missed or swapped animals and false insurance claims. The major contributions of the work as follows: (1) preparation of muzzle point image database, which are not publically available, (2) extraction of the salient set of texture features and representation of muzzle point image of cattle using the deep learning based convolution neural network, deep belief neural network proposed approaches. The stacked denoising auto-encoder technique is applied to encode the extracted feature of muzzle point images and (3) experimental results and analysis of proposed approach. Extensive experimental results illustrate that the proposed deep learning approach outperforms state-of-the-art methods for recognition of cattle on muzzle point image database. The efficacy of the proposed deep learning approach is computed under different identification settings. With multiple test galleries, rank-1 identification accuracy of 98.99% is achieved.

Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm

Animal biometrics is an emerging research field that develops quantified methodologies for representing and detecting the visual appearances of animal based on generic features and primary biometric characteristics. The identification of individual cattle is an important issue for classification of different breads, their registration, traceability, health management, and verification of false insurance claim throughout the world. To solve these major problems, the muzzle (nose) point image pattern of cattle is a suitable and primary biometric characteristic for the recognition of cattle. The recognition of muzzle point images is similar to the recognition of minutiae points in the human fingerprints. In this paper, we propose a hybrid feature extraction approach for the automatic recognition and classification of cattle breeds based on captured muzzle point image pattern features using low- cost camera. The major contributions of this research is based on following aspects: (1) preparation of muzzle point image dataset, (2) extraction of salient set of features and (3) K-nearest neighbour (K-NN), Fuzzy-KNN, Decision Tree (DT), Gaussian Mixture Model (GMM), Probabilistic Neural Network (PNN), Multilayer Perceptron (MLP) and Naive Bayes classification models are applied to recognition, and classification of individual cattle using their muzzle point pattern. This paper, therefore, demonstrates the automatic recognition and classification of livestock using the set of extracted muzzle point image features. The experimental results show that proposed hybrid feature extraction and recognition approach outperforms the current state-of- the art method for the identification of individual cattle using their muzzle point image pattern.

Real-time recognition of cattle using animal biometrics

With the advent of efficient recognition techniques, animal biometric systems have gained more proliferation for the identification and monitoring of cattle. A cattle biometric system is a pattern recognition-based system for the identification of livestock. In this paper, we propose a novel muzzle point recognition based on Fisher locality preserving projection algorithm for the recognition of cattle in real time. We have captured images of animals using a surveillance camera and transferred them to the server by wireless network technology. The major contributions are as follows: (1) preparation of muzzle point database, (2) extraction of the salient set of features using proposed muzzle point recognition approach, and (3) evaluation and comparison analysis of the introduced method and several existing recognition algorithms on a standard benchmark protocol. The efficacy of proposed muzzle point recognition approach for cattle evaluates under identification settings and yields \(96.87\,\%\) recognition accuracy for identifying individual cattle. The proposed approach also valued the 10.25 sec recognition time for enrollment and identified individual cattle on different sizes of muzzle point images.