Identification of aflatoxin-poisoned broilers based on accelerometer and machine learning

Mei, Weida; Yang, Xiao; Zhao, Yang; Wang, Xiaoshuai; Dai, Xiaorong; Wang, Kaiying

doi:10.1016/j.biosystemseng.2023.01.021

Cited by 15 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al utilized two machine learning models, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), to analyze the data collected from accelerometers on broilers, achieving the classification of specific broiler behaviors [ 68 ]. Mei et al validated the usefulness of utilizing 3D accelerometers and machine learning models for the identification of aflatoxicosis in broiler chickens [ 70 ]. Williams and Zhan found that the data from tail-mounted accelerometers showed high classification performance for standing and lying postures of dairy cows but performed poorly in classifying excretory events [ 71 ].…”

Section: Resultsmentioning

confidence: 99%

Precision Livestock Farming Research: A Global Scientometric Review

Jiang

Tang

Cui

et al. 2023

Animals

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Precision Livestock Farming Research: A Global Scientometric Review

Jiang

Tang

Cui

et al. 2023

Animals

View full text Add to dashboard Cite

show abstract

“…The update of sparse coefficients is generally achieved by using an iterative thresholding function to realize sparsification, such as the ISTA algorithm shown in Eqs. ( 6) and (7). It can be observed that the sparse coding algorithm in deep sparse representation theory and the structure of neurons (convolution operation) in neural networks have equivalence.…”

Section: Sparse Representation and Deep Neural Network 31 Connection ...mentioning

confidence: 99%

“…However, deep learning relies on statistical information and only focuses on visual plausibility, and lacks interpretability. The reliability of the image reconstruction results cannot be guaranteed, casting doubts on the application of deep learning in biological slice image inpainting [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting

Hu,

Ma,

Mei

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Biological slices are an effective tool for studying the physiological structure and evolution mechanism of biological systems. However, due to the complexity of preparation technology and the presence of many uncontrollable factors during the preparation processing, leads to problems such as difficulty in preparing slice images and breakage of slice images. Therefore, we proposed a biological slice image small-scale corruption inpainting algorithm with interpretability based on multi-layer deep sparse representation, achieving the high-fidelity reconstruction of slice images. We further discussed the relationship between deep convolutional neural networks and sparse representation, ensuring the high-fidelity characteristic of the algorithm first. A novel deep wavelet dictionary is proposed that can better obtain image prior and possess learnable feature. And multi-layer deep sparse representation is used to implement dictionary learning, acquiring better signal expression. Compared with methods such as NLABH, Shearlet, Partial Differential Equation (PDE), K-Singular Value Decomposition (K-SVD), Convolutional Sparse Coding, and Deep Image Prior, the proposed algorithm has better subjective reconstruction and objective evaluation with small-scale image data, which realized high-fidelity inpainting, under the condition of small-scale image data. And the O n 2 -level time complexity makes the proposed algorithm practical. The proposed algorithm can be effectively extended to other cross-sectional image inpainting problems, such as magnetic resonance images, and computed tomography images.

show abstract

“…Machine learning and accelerometers have also been used to analyze the activity amount of chickens, such as the Random Forest has been used to identify the low, medium and high intensity activity of laying hens at different weeks of age [28], and the Bagged Tree has been used to classify the static, semi-dynamic and highly dynamic behavior of laying hens [29]. In monitoring chicken health and welfare, Mei et al [30] used sensors to monitor broilers and used machine learning algorithms such as KNN and Decision Tree to identify aflatoxin-poisoned broilers based on behavioral differences, and He et al [31]wore sensors on the broilers' legs to detect the lameness using the Logistic Regression . These studies suggest that the use of inertial sensors to monitor chicken behavior can help to automate the quantification of specific behaviors.…”

Section: Introductionmentioning

confidence: 99%

A Recognition Method for Aggressive Chicken Behavior Based on Machine Learning

Li,

Wang,

Hou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Aggressive behavior is an important indicator of chicken welfare assessment. At present, the aggressive behavior of chickens typically requires human observation for welfare assessment, and the assessment results are influenced by the subjective judgment of humans. This paper proposes an aggressive chicken behavior identification method based on a hybrid strategy improved Sparrow Search Algorithm combined with Support Vector Machine (ISSA-SVM). Nine-axis inertial sensors were used to collect the behavioral data of chickens. A total of 231-dimensional feature data in the time and frequency domains of the behavioral data were extracted through a 1 s sliding window. To reduce feature redundancy, the initial population is initialized using circle chaotic mapping instead of random initialization of the original sparrow algorithm to increase the uniformity of the initial population distribution in the feature space; adaptive weights are introduced to increase the search range of the early iteration, and the global optimal solution of the previous generation is introduced to improve the global search capability of the algorithm; the optimal solution is perturbed using the dimension-by-dimension mutation strategy of adaptive t-distribution to increase the diversity of the feature distribution. ISSA-SVM reduced the feature dimensionality from 231 to 17, indicating a reduction of 92.6%. The recognition overall accuracy of ISSA-SVM for aggressive chicken behavior was 94.27%, which improved by 1.33% compared to SVM. The results of the experiment show that of all the aggressive chicken behaviors during the 5-day experiment, fighting behavior occurred most frequently from 11: 00 to 13: 00 and from 17: 00 to 18: 00. This study provides a method for the automatic identification of aggressive chicken behavior and can serve as an informational tool for poultry welfare assessment.INDEX TERMS Aggressive behavior, chicken, ISSA-SVM, feature selection, inertial sensors.

show abstract

Identification of aflatoxin-poisoned broilers based on accelerometer and machine learning

Cited by 15 publications

References 35 publications

Precision Livestock Farming Research: A Global Scientometric Review

Precision Livestock Farming Research: A Global Scientometric Review

Multi-Layer Deep Sparse Representation for Biological Slice Image Inpainting

A Recognition Method for Aggressive Chicken Behavior Based on Machine Learning

Contact Info

Product

Resources

About