Using an EfficientNet-LSTM for the recognition of single Cow’s motion behaviours in a complicated environment

Yin, Xuqiang; Wu, Dihua; Shang, Yuying; Jiang, Bo; Song, Huaibo

doi:10.1016/j.compag.2020.105707

Cited by 73 publications

(37 citation statements)

References 14 publications

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“…It is also non-contact and does not need to touch the animal for identification, so does not cause stress and can provide continuous long-term monitoring [ 10 ]. Many scholars have applied deep learning to cows [ 11 , 12 ]. Zhao et al [ 13 ] collected side-view videos of cows walking in a straight line to study and evaluate image processing techniques, including four feature extraction methods and two matching methods.…”

Section: Introductionmentioning

confidence: 99%

Individual dairy cow identification based on lightweight convolutional neural network

Sun

et al. 2021

PLoS ONE

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In actual farms, individual livestock identification technology relies on large models with slow recognition speeds, which seriously restricts its practical application. In this study, we use deep learning to recognize the features of individual cows. Alexnet is used as a skeleton network for a lightweight convolutional neural network that can recognise individual cows in images with complex backgrounds. The model is improved for multiple multiscale convolutions of Alexnet using the short-circuit connected BasicBlock to fit the desired values and avoid gradient disappearance or explosion. An improved inception module and attention mechanism are added to extract features at multiple scales to enhance the detection of feature points. In experiments, side-view images of 13 cows were collected. The proposed method achieved 97.95% accuracy in cow identification with a single training time of only 6 s, which is one-sixth that of the original Alexnet. To verify the validity of the model, the dataset and experimental parameters were kept constant and compared with the results of Vgg16, Resnet50, Mobilnet V2 and GoogLenet. The proposed model ensured high accuracy while having the smallest parameter size of 6.51 MB, which is 1.3 times less than that of the Mobilnet V2 network, which is famous for its light weight. This method overcomes the defects of traditional methods, which require artificial extraction of features, are often not robust enough, have slow recognition speeds, and require large numbers of parameters in the recognition model. The proposed method works with images with complex backgrounds, making it suitable for actual farming environments. It also provides a reference for the identification of individual cows in images with complex backgrounds.

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Section: Introductionmentioning

confidence: 99%

Individual dairy cow identification based on lightweight convolutional neural network

Sun

et al. 2021

PLoS ONE

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“…A rough rule of thumb is that a supervised deep learning algorithm generally achieves good performance with around 5000 labeled instances per category [ 147 ], while 1000–5000 labeled images were generally considered in the 105 references ( Figure 7 ). The least number was 33 [ 50 ], and the largest number was 2270250 [ 84 ]. Large numbers of labeled images were used for tracking, in which continuous frames were involved.…”

Section: Preparationsmentioning

confidence: 99%

“…The combinations had three categories. The first one was to combine multiple CNNs, such as YOLO + AlexNet [ 151 ], YOLO V2 + ResNet50 [ 98 ], and YOLO V3 + (AlexNet, VGG16, VGG19, ResNet18, ResNet34, DenseNet121) [ 126 ]; the second one was to combine CNNs with regular machine learning models, such as fully connected network (FCN) + support vector machine (SVM) [ 68 ], Mask region-based CNN (mask R-CNN) + kernel extreme learning machine (KELM) [ 90 ], Tiny YOLO V2 + SVM [ 121 ], VGG16 + SVM [ 49 ], YOLO + AlexNet + SVM [ 74 ], and YOLO V3 + [SVM, K-nearest neighbor (KNN), decision tree classifier (DTC)] [ 152 ]; and the third one was to combine CNNs with other deep learning techniques, such as [Convolutional 3 dimension (C3D), VGG16, ResNet50, DenseNet169, EfficientNet] + long short-term memory (LSTM) [ 84 ], Inception V3 + bidirectional LSTM (BiLSTM) [ 41 ], and Inception V3 + LSTM [ 153 ], YOLO V3 + LSTM [ 152 ]. In all these combinations, CNN typically played roles of feature extractors in the first stage, and then other models utilized these features to make classifications.…”

Section: Convolutional Neural Network Architecturesmentioning

confidence: 99%

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Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Huang

Chen

et al. 2021

Sensors

102

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Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.

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“…Fuentes et al [ 77 ] extracted temporal-context features (3D-CNN) and motion information (optical flow) from videos, achieving 78.8% recognition for 15 different hierarchical behaviours. Yin et al [ 115 ] proposed the EfficientNet-LSTM model to extract spatial feature for the recognition of cows’ motion behaviours, which achieved 97.87% behaviour recognition accuracy in the antagonism of environmental robustness. Wu et al [ 13 ] proposed CNN-LSTM (a fusion of convolutional neural network and long short-term memory) for recognising the basic behaviours of a single cow.…”

Section: Cattle Lameness Detection and Scoringmentioning

confidence: 99%

Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

Qiao

Kong

Clark

et al. 2021

Animals

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The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

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Using an EfficientNet-LSTM for the recognition of single Cow’s motion behaviours in a complicated environment

Cited by 73 publications

References 14 publications

Individual dairy cow identification based on lightweight convolutional neural network

Individual dairy cow identification based on lightweight convolutional neural network

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

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