A lightweight CNN-based model for early warning in sow oestrus sound monitoring

Wang, Yuan; Li, Shouxiao; Zhang, Hang; Liu, Tonghai

doi:10.1016/j.ecoinf.2022.101863

Cited by 14 publications

(7 citation statements)

References 28 publications

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“…Higaki et al employed sensors attached to the tail to collect surface temperature data of the ventral tail base of cattle during the estrous cycle and constructed an estrus detection model using machine learning technology for training data, which was tested for estrus detection ability [ 84 ]. Wang et al developed a lightweight sow estrus detection approach based on acoustic data and a deep convolution neural network algorithm, which analyzed short- and long-frequency sow estrus sounds, providing an effective and accurate estrus monitoring and early warning system for pig farms [ 85 ]. Yu et al proposed an ewe estrus recognition method based on a multi-target detection layer neural network, which can accurately and promptly recognize ewe estrus behavior in large-scale mutton sheep breeding, avoiding the stress caused by contact sensor detection [ 86 ].…”

Section: Resultsmentioning

confidence: 99%

Precision Livestock Farming Research: A Global Scientometric Review

Jiang

Tang

Cui

et al. 2023

Animals

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Precision livestock farming (PLF) utilises information technology to continuously monitor and manage livestock in real-time, which can improve individual animal health, welfare, productivity and the environmental impact of animal husbandry, contributing to the economic, social and environmental sustainability of livestock farming. PLF has emerged as a pivotal area of multidisciplinary interest. In order to clarify the knowledge evolution and hotspot replacement of PLF research, based on the relevant data from the Web of Science database from 1973 to 2023, this study analyzed the main characteristics, research cores and hot topics of PLF research via CiteSpace. The results point to a significant increase in studies on PLF, with countries having advanced livestock farming systems in Europe and America publishing frequently and collaborating closely across borders. Universities in various countries have been leading the research, with Daniel Berckmans serving as the academic leader. Research primarily focuses on animal science, veterinary science, computer science, agricultural engineering, and environmental science. Current research hotspots center around precision dairy and cattle technology, intelligent systems, and animal behavior, with deep learning, accelerometer, automatic milking systems, lameness, estrus detection, and electronic identification being the main research directions, and deep learning and machine learning represent the forefront of current research. Research hot topics mainly include social science in PLF, the environmental impact of PLF, information technology in PLF, and animal welfare in PLF. Future research in PLF should prioritize inter-institutional and inter-scholar communication and cooperation, integration of multidisciplinary and multimethod research approaches, and utilization of deep learning and machine learning. Furthermore, social science issues should be given due attention in PLF, and the integration of intelligent technologies in animal management should be strengthened, with a focus on animal welfare and the environmental impact of animal husbandry, to promote its sustainable development.

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Section: Resultsmentioning

confidence: 99%

Precision Livestock Farming Research: A Global Scientometric Review

Jiang

Tang

Cui

et al. 2023

Animals

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“…Wang et al ( 2022) developed an estrus identification model relying on auditory input and deep CNN to detect estrus by accumulating and analyzing short and long recurrence of sounds. They found the model to be effective with 97.52% accuracy [139]. Increased accuracy, early detection, reduced labor requirement, and remote monitoring accessibility are the advantages offered by artificial intelligence.…”

Section: Artificial Intelligence and Machine Learning Models In Estru...mentioning

confidence: 99%

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

Sharifuzzaman,

Mun,

Ampode

et al. 2024

Animals

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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.

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“…Lightweight CNN architecture is one of a deep learning commonly used in image data processing in a small form factor neural net [86][87][88][89][90]. In this research, can be seen in Fig.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Development of Speech Command Control Based TinyML System for Post-Stroke Dysarthria Therapy Device

Riyanta,

Irianta,

Kamiel

2023

Journal of Robotics and Control

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Post-stroke dysarthria (PSD) is a widespread outcome of a stroke. To help in the objective evaluation of dysarthria, the development of pathological voice recognition and technology has a lot of attention. Soft robotics therapy devices have been received as an alternative rehabilitation and hand grasp assistance for improving activity daily living (ADL). Despite the significant progress in this field, most soft robotic therapy devices use a complex, bulky, lack of pathological voice recognition model, large computational power, and stationary controller. This study aims to develop a portable wirelessly multi-controller with a simulated dysarthric vowel speech in Bahasa Indonesia and non-dysarthric micro speech recognition, using tiny machine learning (TinyMl) system for hardware efficiency. The speech interface using INMP441, compute with a lightweight Deep Convolutional Neural network (DCNN) design and embedded into ESP-32. Feature model using Short Time Fourier Transform (STFT) and fed into CNN. This method has proven useful in micro-speech recognition with low computational power in both speech scenarios with a level of accuracy above 90%. Realtime inference performance on ESP-32 using hand prosthetics, with 3-level household noise intensity respectively 24db,42db, and 62db, and has respectively resulted from 95%, 85%, and 50% Accuracy. Wireless connectivity success rate with both controllers is around 0.2 - 0.5 ms.

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A lightweight CNN-based model for early warning in sow oestrus sound monitoring

Cited by 14 publications

References 28 publications

Precision Livestock Farming Research: A Global Scientometric Review

Precision Livestock Farming Research: A Global Scientometric Review

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

Development of Speech Command Control Based TinyML System for Post-Stroke Dysarthria Therapy Device

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