Indoor air temperature (IAT) and indoor relative humidity (IRH) are the prominent microclimatic variables; still, potential contributors that influence the homeostasis of livestock animals reared in closed barns. Further, predicting IAT and IRH encourages farmers to think ahead actively and to prepare the optimum solutions. Therefore, the primary objective of the current literature is to build and investigate extensive performance analysis between popular ML models in practice used for IAT and IRH predictions. Meanwhile, multiple linear regression (MLR), multilayered perceptron (MLP), random forest regression (RFR), decision tree regression (DTR), and support vector regression (SVR) models were utilized for the prediction. This study used accessible factors such as external environmental data to simulate the models. In addition, three different input datasets named S1, S2, and S3 were used to assess the models. From the results, RFR models performed better results in both IAT (R2 = 0.9913; RMSE = 0.476; MAE = 0.3535) and IRH (R2 = 0.9594; RMSE = 2.429; MAE = 1.47) prediction among other models particularly with S3 input datasets. In addition, it has been proven that selecting the right features from the given input data builds supportive conditions under which the expected results are available. Overall, the current study demonstrates a better model among other models to predict IAT and IRH of a naturally ventilated swine building containing animals with fewer input attributes.

To achieve successful composting, all the biological, chemical, and physical characteristics need to be considered. The investigation of our study was based on various physicochemical properties, i.e., temperature, ammonia concentration, carbon dioxide concentration, pH, electrical conductivity (EC), carbon/nitrogen (C/N) ratio, organic matter (OM) content, moisture content, bacterial population, and seed germination index (GI), during the composting of poultry manure and sawdust for different aeration rates and reactor shapes. Three cylindrical-shaped and three rectangular-shaped pilot-scale 60-L composting reactors were used in this study, with aeration rates of 0.3 (low), 0.6 (medium), and 0.9 (high) L min −1 kg −1 DM (dry matter). All parameters were monitored over 21 days of composting. Results showed that the low aeration rate (0.3 L min −1 kg −1 DM) corresponded to a higher and longer thermophilic phase than did the high aeration rate (0.9 L min −1 kg −1 DM). Ammonia and carbon dioxide volatilization were directly related to the temperature profile of the substrate, with significant differences between the low and high aeration rates during weeks 2 and 3 of composting but no significant difference observed during week 1. At the end of our study, the final values of pH, EC, moisture content, C/N ratio, and organic matter in all compost reactors were lower than those at the start. The growth rates of mesophilic and thermophilic bacteria were directly correlated with mesophilic and thermophilic conditions of the compost. The final GI of the cylindrical reactor with an airflow rate of 0.3 L min −1 kg −1 DM was 82.3%, whereas the GIs of the other compost reactors were below 80%. In this study, compost of a cylindrical reactor with a low aeration rate (0.3 L min −1 kg −1 DM) was more stable and mature than the other reactors. Implications: The poultry industry is growing in South Korea, but there are problems associated with the management of poultry manure, and composting is one solution that could be valuable for crops and forage if managed properly. For high-quality composting, the aeration rate in different reactor shapes must be considered. The objective of this study was to investigate various physicochemical properties with different aeration rates and rector shapes. Results showed that aeration rate of 0.3 L min −1 kg −1 DM in a cylindrical reactor provides better condition for maturation of compost.

Linear partial least square and non-linear support vector machine regression analysis with various preprocessing techniques and their combinations were used to determine the soluble solids content of hardy kiwi fruits by a handheld, portable near-infrared spectroscopy. Fruits of four species, namely Autumn sense (A), Chungsan (C), Daesung (D), and Green ball (Gb) were collected from five different areas of Gwangyang (G), Muju (M), Suwon (S), Wonju (Q), and Yeongwol (Y) in South Korea. The dataset for calibration and prediction was prepared based on each area, species, and in combination. Half of the dataset of each area, species, and combined dataset was used as calibrated data and the rest was used for model validation. The best prediction correlation coefficient ranges between 0.67 and 0.75, 0.61 and 0.77, and 0.68 for the area, species, combined dataset, respectively using partial least square regression (PLSR) method with different preprocessing techniques. On the other hand, the best correlation coefficient of predictions using the support vector machine regression (SVM-R) algorithm was 0.68 and 0.80, 0.62 and 0.79, and 0.74 for the area, species, and combined dataset, respectively. In most cases, the SVM-R algorithm produced better results with Autoscale preprocessing except G area and species Gb, whereas the PLS algorithm shows a significant difference in calibration and prediction models for different preprocessing techniques. Therefore, the SVM-R method was superior to the PLSR method in predicting soluble solids content of hardy kiwi fruits and non-linear models may be a better alternative to monitor soluble solids content of fruits. The finding of this research can be used as a reference for the prediction of hardy kiwi fruits soluble solids content as well as harvesting time with better prediction models.

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Purpose This study aimed to achieve successful composting and aeration rate and to optimize the carbon:nitrogen (C:N) ratio to provide favourable conditions for the process. In the current experiment, investigation were made on variations in physico-chemical properties, i.e., temperature, ammonia and carbon dioxide emissions, pH, electrical conductivity (EC), organic matter (OM) and seed germination index (GI%) of composting chicken manure mixed with sawdust and wood shavings under different aeration rates in a closed reactor system. Methods Three cylindrical reactors (total volume, 60 L) were used with three aeration rates of 0.25, 0.50 and 0.75 L min −1 kg −1 OM. The air was dispensed from the bottom of an air compressor. The ambient and compost temperature were monitored thrice daily over 30 days of composting. Results The highest temperatures were 56.9, 55.8 and 48.1 °C, with 0.25, 0.50 and 0.75 L min −1 kg −1 OM aeration rates, respectively. At the end of composting, lowest ammonia and carbon dioxide emissions were observed with 0.25 L min −1 kg −1 OM aeration, indicating that this compost was more stable than other composts. The lowest GI was recorded on day 30 with 0.75 L min −1 kg −1 OM aeration, indicating severe phytotoxicity in the substrate. Maximum OM degradation occurred with 0.25 L min −1 kg −1 OM aeration. Conclusion This study, therefore, suggested that 0.25 L min −1 kg −1 OM aeration in the composing of the chicken manure mixed with sawdust and wood shavings in closed a reactor system provided the most favourable conditions for maturation.