Automated sheep facial expression classification using deep transfer learning

Noor, Alam; Zhao, Yaqin; Koubâa, Anis; Wu, Longwen; Khan, Rahim; Abdalla, Fakheraldin Y. O.

doi:10.1016/j.compag.2020.105528

Cited by 59 publications

(30 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this section, the preprocessing solutions include selection of key frames, balance of datasets, adjustment of image channels, image cropping, image enhancement, image restoration, and image segmentation [ 109 ]. Although some research also defined image resizing [ 76 ] and data augmentation [ 110 ] as image preprocessing, they were included either in CNN architectures or in model training processes, thus not being discussed in this section.…”

Section: Preparationsmentioning

confidence: 99%

“…The ResNet50 [ 69 , 76 , 120 , 151 ] and VGG16 [ 49 , 76 , 101 , 107 , 120 , 151 ] were the most popular architectures used in animal farming ( Table 3 ). The former may be an optimal model balancing detection accuracy and processing speed thus being widely used.…”

Section: Convolutional Neural Network Architecturesmentioning

confidence: 99%

“…It normalizes parameters in each layer with certain means and variance, which maintains parameter distribution constant, reduces gradient dispersion, and makes training robust [ 244 ]. Other regularization methods (e.g., L1/L2 regularization), which add L1/L2 norms to loss function and regularize parameters, were also utilized in animal farming [ 76 , 111 ].…”

Section: Strategies For Algorithm Developmentmentioning

confidence: 99%

“…Abnormality detection can signal early warnings for farmers and contribute to timely intervention and mitigation of the problems. Abnormality includes pain reflected by face expression [ 76 ], lameness [ 152 ], mastitis [ 53 ], and sickness [ 78 ]. Animal recognition is to recognize animals of interest from background and generally used for animal counting, so that usage status of facilities can be understood [ 230 ].…”

Section: Applicationsmentioning

confidence: 99%

“…Because of excellent performance, the abovementioned CNNs were designed as feature extractors and embedded into architectures in the rest of computer vision tasks. [27] First proposal of modern CNN GitHub [155] (PyTorch) [156] Inception family Inception V1/GoogLeNet [35] Increasing width of networks, low computational cost GitHub [157] (PyTorch) [66,76] Inception V3 [158] Inception module, factorized convolution, aggressive regularization GitHub [159] (TensorFlow) [63,76,120] Inception ResNet V2 [160] Combination of Inception module and Residual connection GitHub [161] (TensorFlow) [69,120] Xception [162] Extreme inception module, depthwise separable convolution GitHub [163] (TensorFlow) [120] MobileNet family MobileNet [149] Depthwise separable convolution, lightweight GitHub [159] (TensorFlow) [120] MobileNet V2 [164] Inverted residual structure, bottleneck block GitHub [165] (PyTorch) [120] NASNet family NASNet Mobile [150] Convolutional cell, learning transformable architecture GitHub [166] (TensorFlow) [120] NASNet Large [150] GitHub [159] (TensorFlow)…”

Section: Architectures For Image Classificationmentioning

confidence: 99%

See 4 more Smart Citations

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

Huang

Chen

et al. 2021

Sensors

102

View full text Add to dashboard Cite

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.

show abstract

Section: Preparationsmentioning

confidence: 99%

Section: Convolutional Neural Network Architecturesmentioning

confidence: 99%

Section: Strategies For Algorithm Developmentmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Architectures For Image Classificationmentioning

confidence: 99%

See 3 more Smart Citations

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

Huang

Chen

et al. 2021

Sensors

102

View full text Add to dashboard Cite

show abstract

Contemporary machine learning applications in agriculture: Quo Vadis?

Mahmood

Tiwari

Singh

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Agricultural automation is an emerging subject today to accomplish the food demands of individuals across the globe. Machine learning is one such agricultural automation tool that has been adopted briskly in the recent decade due to its ability to process countless input data and handle non-linear tasks. Availability and continuous development of agricultural data led the machine learning pervasive in multiple aspects of agriculture. This paper systematically analyses and summarizes the 81 quality research efforts published in the past decade dedicated to the various contemporary machine learning applications in agriculture and food production systems. We examined and categorized each agricultural problem under study into four categories and each category into its subcategories. The finding demonstrates the contemporary applications of machine learning in broad agricultural subcategories and determines where it is heading shortly; based upon contributions of researchers, utilization of machine learning models/algorithms, and the availability of agricultural datasets.Through the analysis, it is discovered that the current innovation can help the improvement of agricultural automation to accomplish the advantages of minimal cost, high efficiency, and better precision. This paper can serve as an investigatory guide for researchers, academicians, engineers, and manufacturers to understand and apply modern and upgraded cognitive technologies to each subcategory of the agricultural sector.

show abstract

Convolutional neural networks to classify human stress that occurs during in‐field sugarcane harvesting: A case study

Modi,

Singh,

Singh

et al. 2024

Journal of Field Robotics

View full text Add to dashboard Cite

Assessing human stress in agriculture proves to be a complex and time‐intensive endeavor within the field of ergonomics, particularly for the development of agricultural systems. This methodology involves the utilization of instrumentation and the establishment of a dedicated laboratory setup. The complexity arises from the need to capture and analyze various physiological and psychological indicators, such as heart rate (HR), muscle activity, and subjective feedback to comprehensively assess the impact of farm operations on subjects. The instrumentation typically includes wearable devices, sensors, and monitoring equipment to gather real‐time data of subject during the performance of farm operations. Deep learning (DL) models currently achieve human performance levels on real‐world face recognition tasks. In this study, we went beyond face recognition and experimented with the recognition of human stress based on facial features during the drudgery‐prone agricultural operation of sugarcane harvesting. This is the first research study for deploying artificial intelligence‐driven DL techniques to identify human stress in agriculture instead of monitoring several ergonomic characteristics. A total of 20 (10 each for male and female) subjects comprising 4300 augmented RGB images (215 per subject) were acquired during sugarcane harvesting seasons and then these images were deployed for training (80%) and validation (20%). Human stress and nonstress states were determined based on four ergonomic physiological parameters: heart rate (ΔHR), oxygen consumption rate (OCR), energy expenditure rate (EER), and acceptable workload (AWL). Stress was defined when ΔHR, OCR, EER, and AWL reached or exceeded certain standard threshold values. Four convolutional neural network‐based DL models (1) DarkNet53, (2) InceptionV3, (3) MobileNetV2 and (4) ResNet50 were selected due to their remarkable feature extraction abilities, simple and effective implementation to edge computation devices. In all four DL models, training performance results delivered training accuracy ranging from 73.8% to 99.1% at combinations of two mini‐batch sizes and four levels of epochs. The maximum training accuracies were 99.1%, 99.0%, 97.7%, and 95.4% at the combination of mini‐batch size 16 and 25 epochs for DarkNet53, InceptionV3, ResNet50, and MobileNetV2, respectively. Due to the best performance, DarkNet53 was tested further on an independent data set of 100 images and found 89.8%–93.3% confident to classify stressed images for female subjects while 92.2%–94.5% for male subjects, though it was trained on the integrated data set. The comparative classification of the developed model and ergonomic measurements for stress classification was carried out with a net accuracy of 88% where there were few instances of wrong classifications.

show abstract

Automated sheep facial expression classification using deep transfer learning

Cited by 59 publications

References 16 publications

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

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

Contemporary machine learning applications in agriculture: Quo Vadis?

Convolutional neural networks to classify human stress that occurs during in‐field sugarcane harvesting: A case study

Contact Info

Product

Resources

About