A Lightweight Neural Network-Based Method for Detecting Estrus Behavior in Ewes

Yu, Longhui; Pu, Yuhai; Cen, Honglei; Li, Jingbin; Liu, Shuangyin; Nie, Jing; Ge, Jianbing; Lv, Linze; Li, Yali; Xu, Yalei; Guo, Jianjun; Zhao, Hangxing; Wang, Kang

doi:10.3390/agriculture12081207

Cited by 10 publications

(6 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MobileNetV2 had the highest FPS of 54.39, which improved by 46.29% compared to CSPDarknet53. Although the model size of EfficientNetV2 was found to be significantly lower than that of CSPDarknet53, FPS decreased rather than increased, unlike the results published in other studies [42,43], which may be caused by the compatibility of the experimental hardware platform with the model inference process. The training loss value of different models all plummeted at the 70th epoch, which may be caused by the change of learning rate during model training.…”

Section: Lightweight Convolutional Neural Networkcontrasting

confidence: 70%

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

Zhang

Zhao

et al. 2023

Agronomy

View full text Add to dashboard Cite

Accurate and rapid detection of tea shoots within the tea canopy is essential for achieving the automatic picking of famous tea. The current detection models suffer from two main issues: low inference speed and difficulty in deployment on movable platforms, which constrain the development of intelligent tea picking equipment. Furthermore, the detection of tea canopy shoots is currently limited to natural daylight conditions, with no reported studies on detecting tea shoots under artificial light during the nighttime. Developing an all-day tea picking platform would significantly improve the efficiency of tea picking. In view of these problems, the research objective was to propose an all-day lightweight detection model for tea canopy shoots (TS-YOLO) based on YOLOv4. Firstly, image datasets of tea canopy shoots sample were collected under low light (6:30–7:30 and 18:30–19:30), medium light (8:00–9:00 and 17:00–18:00), high light (11:00–15:00), and artificial light at night. Then, the feature extraction network of YOLOv4 and the standard convolution of the entire network were replaced with the lightweight neural network MobilenetV3 and the depth-wise separable convolution. Finally, to compensate for the lack of feature extraction ability in the lightweight neural network, a deformable convolutional layer and coordinate attention modules were added to the network. The results showed that the improved model size was 11.78 M, 18.30% of that of YOLOv4, and the detection speed was improved by 11.68 FPS. The detection accuracy, recall, and AP of tea canopy shoots under different light conditions were 85.35%, 78.42%, and 82.12%, respectively, which were 1.08%, 12.52%, and 8.20% higher than MobileNetV3-YOLOv4, respectively. The developed lightweight model could effectively and rapidly detect tea canopy shoots under all-day light conditions, which provides the potential to develop an all-day intelligent tea picking platform.

show abstract

Section: Lightweight Convolutional Neural Networkcontrasting

confidence: 70%

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

Zhang

Zhao

et al. 2023

Agronomy

View full text Add to dashboard Cite

show abstract

“…Automated estrus detection using sensors, infrared thermography and machine learning models is not limited to sows only. Considerable progress is evident in cow [141][142][143][144], buffalo [23,145], and ewe [146,147] estrus detection as well. Though installing and implementing real-time estrus detection systems may require an initial investment, in the long run, will result in benefits and significant cost savings for livestock farmers and industries.…”

Section: Benefits Of Technology and Ai-based Estrus Detectionmentioning

confidence: 99%

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

Sharifuzzaman,

Mun,

Ampode

et al. 2024

Animals

View full text Add to dashboard Cite

In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

show abstract

“…The experimental results showed that the accuracy of this model for pig gesture recognition was above 93% with FPS between 10 and 12, which still has the disadvantage of slow recognition speed. 2022 Yu L et al proposed using E cientNet, a lightweight neural network, as a feature extraction network for YOLOv3 and added a SENet attention mechanism to detect the oestrus behavior of female sheep, achieving 99.44% accuracy 7 . In addition, there are some object detection studies on sh and ducks that suggest the feasibility of applying deep learning to animals [8][9][10][11][12] .…”

Section: Introductionmentioning

confidence: 99%

GAM-YOLOv7-tiny and Soft-NMS-AlexNet: Improved lightweight sheep body object detection and pose estimation network

Gong,

Liu,

et al. 2024

Preprint

View full text Add to dashboard Cite

Intelligent livestock farming has been a major focus of attention in recent years. Using deep learning to assist livestock management can effectively reduce labor loss and improve management efficiency. Lightweighting plays a key role in the deployment and practical use of deep learning models, and most existing sheep-based deep learning models do not focus on this, which has become a major factor limiting the development of smart sheep farming. Therefore, in this paper, first, a GAM-YOLOv7-tiny neural network model for object detection of sheep was investigated. The size of the model reached 3.5G, which was a reduction to 26.3% of the original size, the FLOPS was reduced by 74.1%, the experimental result reached 96.4% of mAP and the FPS reached 88.232 on an RTX 1650Ti. Second, a Soft-NMS-AlexNet neural network model for key point recognition of sheep bodies was investigated with a model size of 1.97G, and the final experimental results achieved 82% AP and 190.86 ± 23.97 FPS. Finally, we completed the behavior recognition of the standing and lying posture of sheep using the pose estimation model, which provides a research solution for performing behavioral monitoring and giving early warnings for diseases for sheep.

show abstract

A Lightweight Neural Network-Based Method for Detecting Estrus Behavior in Ewes

Cited by 10 publications

References 56 publications

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

Technological Tools and Artificial Intelligence in Estrus Detection of Sows—A Comprehensive Review

GAM-YOLOv7-tiny and Soft-NMS-AlexNet: Improved lightweight sheep body object detection and pose estimation network

Contact Info

Product

Resources

About