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Carbon dioxide (CO2) emissions from the livestock industry are expected to increase. A response strategy for CO2 emission regulations is required for pig production as this industry comprises a large proportion of the livestock industry and it is projected that per capita pork consumption will rise. A CO2 emission response strategy can be established by accurately measuring the CO2 concentrations in pig facilities. Here, we compared and evaluated the performance of three different machine learning (ML) models (ElasticNet, random forest regression (RFR), and support vector regression (SVR)) designed to predict CO2 concentration and internal air temperature (Ti) values in the pig house used to regulate a heating, ventilation, and air conditioning (HVAC) control system. For each ML model, the hyperparameter was optimised and the predictive accuracy was evaluated. The order of predictive accuracy for the ML models was ElasticNet < SVR < RFR. Hence, random forest regression provided superior prediction performance. Based on the test dataset, for Ti prediction by RFR, R2 ≥ 0.848 and the root mean square error (RMSE) and mean absolute error (MAE) were 0.235 °C and 0.160 °C, respectively, whilst for CO2 concentration prediction by RFR, R2 ≥ 0.885 and the RMSE and MAE were 64.39 ppm and ≤ 46.17 ppm, respectively.

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To implement smart livestock operations based on information and communication technology, a field monitoring environment based on measured data must be established. Ammonia should be managed more carefully than other environmental monitoring items because it is related to the comfort and production of livestock. Moreover, it is related to odor complaints and environmental protection issues. However, existing studies that propose ammonia measurement standards for each purpose differ from one another or are unclear, and ammonia sensors in the actual field have problems such as durability and management constraints; nonetheless, there is a lack of research on these aspects. Accordingly, for effective ammonia monitoring of smart livestock operations, the necessity of ammonia monitoring for smart livestock operations is first presented, after which a plan for ammonia monitoring of smart livestock operations, supplemented with sensor specifications and maintenance standards, is proposed. This study presents a clear measurement standard based on a comprehensive literature review, as well as an effective monitoring standard reflecting field operation information. This can lead to reliable ammonia data collection and can be a starting point for smooth smart livestock operation.

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