Assessment of dairy cow feed intake based on BP neural network with polynomial decay learning rate

Shen, Weizheng; Li, Gen; Wei, Xiaoli; Fu, Qiang; Zhang, Yonggen; Qu, Tengyu; Chen, Congcong; Wang, Runtao

doi:10.1016/j.inpa.2021.04.008

Cited by 11 publications

(14 citation statements)

References 23 publications

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“…At present, the methods adopted by many scholars to evaluate brand competitiveness mainly include the analytic hierarchy process, multiple regression analysis methods, fuzzy comprehensive evaluation method, etc. ese methods have their insurmountable shortcomings [25][26][27][28][29]. In recent years, the emergence of neural networks has provided new ideas for multi-index systematic evaluation.…”

Section: Application Of Bp Neural Network Methods In Rural Tourism Br...mentioning

confidence: 99%

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Construction of Rural Tourism Brand Value Management Model from the Perspective of Big Data

Wang

2022

Computational Intelligence and Neuroscience

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Rural tourism has become an important branch of tourism management. Big data technology provides tools for rural tourism brand value management. This study aims to build a brand value management model for rural tourism from the perspective of big data. The rural tourism brand value management model under the big data perspective takes the rural tourism brand competitiveness as the starting point to analyze the relationship between brand value and brand competitiveness, so that the brand competitiveness under the perspective of rural tourism brand value has a more specific and quantifiable index system. From the two aspects of enterprise value advantage and customer value advantage, this article looks for the factors that comprehensively reflect the brand competitiveness of rural tourism. After the establishment of the index system, the BP neural network model is used to make the multiple factor evaluation more objective and feasible. Finally, it is proposed to enhance the competitiveness of rural tourism brands from the perspective of the reconstruction of competitive advantage based on enterprise value and the reconstruction of competitive advantage based on customers.

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Section: Application Of Bp Neural Network Methods In Rural Tourism Br...mentioning

confidence: 99%

“…e BP neural network [25][26][27][28][29] is developed based on the backpropagation algorithm. It is a multi-level feedback network with error back-propagation.…”

Section: E Basic Principle Of Artificial Neural Networkmentioning

confidence: 99%

Construction of Rural Tourism Brand Value Management Model from the Perspective of Big Data

Wang

2022

Computational Intelligence and Neuroscience

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“…In forward transmission, the input signal is processed from the input layer through the implicit layer to the output layer. The neuron state in each layer only affects the neuron state in the next layer, and if the desired output is not obtained in the output layer, it is transferred to backpropagation, and the weights and thresholds of the network are adjusted according to the prediction error so that the predicted output of the BP neural network continuously approximates the desired output [ 12 , 13 , 14 ].…”

Section: Csapso-bp Neural Network Algorithmmentioning

confidence: 99%

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

Fang

Deng

et al. 2023

Sensors

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To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

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“…Furthermore, Shen et al ( 2022) proposed a method of optimizing BP neural networks to establish a dairy cow feed intake assessment model, taking the cow's body weight, lying duration, lying times, walking steps, foraging duration, and concentrate-roughage ratio as input variables and taking the actual feed intake as the output variable, and the model was trained and verified using experimental data collected on site. They concluded that the established BP model using the polynomial decay learning rate has the highest assessment accuracy for the assessment of feed intake, and R 2 was 0.94 [112]. In addition, Ding et al ( 2022) evaluated an integrated machine-learning algorithm framework to identify jaw movements during feeding using a triaxial accelerometer at a relatively low sampling frequency, and it was also used to predict feed intake on the basis of the acceleration variables of ingesting and chewing activities.…”

Section: Precision Feed Intakementioning

confidence: 99%

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

Liu,

Qi,

et al. 2023

Agriculture

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Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers.

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Assessment of dairy cow feed intake based on BP neural network with polynomial decay learning rate

Cited by 11 publications

References 23 publications

Construction of Rural Tourism Brand Value Management Model from the Perspective of Big Data

Construction of Rural Tourism Brand Value Management Model from the Perspective of Big Data

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows

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