Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units

Peng, Yingqi; Kondo, Naoshi; Fujiura, Tateshi; Suzuki, Tetsuhito; Wulandari, _; Yoshioka, Hidetsugu; Itoyama, Erina

doi:10.1016/j.compag.2018.12.023

Cited by 89 publications

(31 citation statements)

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“…Furthermore, false positive (FP) means that the predictive result is positive, but the actual value is negative, and false negative (FN) means that the predicted result is negative, but the actual value is positive. The Formulas (7)–(11) are based on the work presented in [ 50 , 51 , 52 ]. The accuracy, regardless of whether it is actually a positive sample or a negative sample, calculates the ratio of predicted to actual values.…”

Section: Materials and Methodologymentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.

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Section: Materials and Methodologymentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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“…TP indicates true positive (True Positive) (i.e., predicted to suffer from lung disease and actually suffering from lung disease), while TN is true negative as predicted to suffer from lung disease and actually suffering from lung disease, while TN is true negative (True Negative) (i.e., the predicted absence of lung disease and no recorded presence of lung disease). FP is a false positive, which predicts the development of lung disease that is not actually present, while FN is a false negative which predicts no development of lung disease despite the real presence of lung disease, the Formulas (4)-(7) based on the work in [36][37][38][39].…”

Section: Evaluating Cnn Model Performance For Lung Disease Predictionmentioning

confidence: 99%

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

2020

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Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.

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“…CNNs also came to use in agricultural sciences, as Ref. [ 40 ] compared several neural network models trained on motion data of steers to distinguish between behaviours like feeding, lying, ruminating, licking salt, moving, social licking and head butt. However, mostly CNNs were used in the context of image processing.…”

Section: Introductionmentioning

confidence: 99%

Instance Segmentation with Mask R-CNN Applied to Loose-Housed Dairy Cows in a Multi-Camera Setting

Salau

Krieter

2020

Animals

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With increasing herd sizes came an enhanced requirement for automated systems to support the farmers in the monitoring of the health and welfare status of their livestock. Cattle are a highly sociable species, and the herd structure has important impact on the animal welfare. As the behaviour of the animals and their social interactions can be influenced by the presence of a human observer, a camera based system that automatically detects the animals would be beneficial to analyse dairy cattle herd activity. In the present study, eight surveillance cameras were mounted above the barn area of a group of thirty-six lactating Holstein Friesian dairy cows at the Chamber of Agriculture in Futterkamp in Northern Germany. With Mask R-CNN, a state-of-the-art model of convolutional neural networks was trained to determine pixel level segmentation masks for the cows in the video material. The model was pre-trained on the Microsoft common objects in the context data set, and transfer learning was carried out on annotated image material from the recordings as training data set. In addition, the relationship between the size of the used training data set and the performance on the model after transfer learning was analysed. The trained model achieved averaged precision (Intersection over union, IOU = 0.5) 91% and 85% for the detection of bounding boxes and segmentation masks of the cows, respectively, thereby laying a solid technical basis for an automated analysis of herd activity and the use of resources in loose-housing.

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Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units

Cited by 89 publications

References 10 publications

Analyzing Malaria Disease Using Effective Deep Learning Approach

Analyzing Malaria Disease Using Effective Deep Learning Approach

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

Instance Segmentation with Mask R-CNN Applied to Loose-Housed Dairy Cows in a Multi-Camera Setting

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