A Method to Predict CO2 Mass Concentration in Sheep Barns Based on the RF-PSO-LSTM Model
Author:
Cen Honglei123, Yu Longhui1234ORCID, Pu Yuhai123, Li Jingbin123, Liu Zichen123, Cai Qiang123, Liu Shuangyin14, Nie Jing123ORCID, Ge Jianbing123, Guo Jianjun4, Yang Shuo123, Zhao Hangxing123ORCID, Wang Kang123
Affiliation:
1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China 2. Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China 3. Industrial Technology Research Institute of Xinjiang Production and Construction Corps, Shihezi 832000, China 4. College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Abstract
In large-scale meat sheep farming, high CO2 concentrations in sheep sheds can lead to stress and harm the healthy growth of meat sheep, so a timely and accurate understanding of the trend of CO2 concentration and early regulation are essential to ensure the environmental safety of sheep sheds and the welfare of meat sheep. In order to accurately understand and regulate CO2 concentrations in sheep barns, we propose a prediction method based on the RF-PSO-LSTM model. The approach we propose has four main parts. First, to address the problems of data packet loss, distortion, singular values, and differences in the magnitude of the ambient air quality data collected from sheep sheds, we performed data preprocessing using mean smoothing, linear interpolation, and data normalization. Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO2 mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. Then, to address the problem of manually debugging the hyperparameters of the long short-term memory model (LSTM), which is time consuming and labor intensive, as well as potentially subjective, we used a particle swarm optimization (PSO) algorithm to obtain the optimal combination of parameters, avoiding the disadvantages of selecting hyperparameters based on subjective experience. Finally, we trained the LSTM model using the optimized parameters obtained by the PSO algorithm to obtain the proposed model in this paper. The experimental results show that our proposed model has a root mean square error (RMSE) of 75.422 μg·m−3, a mean absolute error (MAE) of 51.839 μg·m−3, and a coefficient of determination (R2) of 0.992. The model prediction curve is close to the real curve and has a good prediction effect, which can be useful for the accurate prediction and regulation of CO2 concentration in sheep barns in large-scale meat sheep farming.
Funder
Shihezi University Innovation and Development Special Project, recipient: Jing Nie Shihezi University Achievement Transformation and Technology Promotion Project, recipient: Honglei Cen Post Expert Task of Meat and Sheep System in Agricultural Area of Autonomous Region, recipient: Jie Zhang National Natural Science Foundation of China; Guangzhou Key Research and Development Project; Characteristic Innovation Project of Universities in Guangdong Province; recipient: Shuangyin Liu Innovation Team Project of Universities in Guangdong Province, recipient: Jianjun Guo
Subject
General Veterinary,Animal Science and Zoology
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