Deep-Learning-Based Automatic Monitoring of Pigs' Physico-Temporal Activities at Different Greenhouse Gas Concentrations.

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs' health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs' health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect pigs' short-term physical activities in the compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00-08:00, 13:00-14:00, and 20:00-21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Faster R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, was coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results revealed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs shortened their sternal-lying posture, increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models' efficacy in the monitoring and tracking of pigs' physical activities non-invasively.

The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals.

Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human-machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs' feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors.

Influence of different growing media on the growth and development of strawberry plants.

The optimal production of strawberries requires the essential nutrients and favourable media for vegetative and reproductive growth. The present study sought to determine the effectiveness of growth parameters and fruit yield of strawberries in different media growing under a greenhouse. To analyze the significant effect for the growth and fruit yield among the growing media, four treatments such as control soil (CS), bio plus compost (T1), the combination of bio plus compost, and synthetic nutrient applied media/integrated media (T2) and synthetic nutrient applied soil media (T3) were assayed. Morphology parameters like plant height, canopy area, fresh weight, dry weight of roots were measured in each stage after eight weeks and sixteen weeks and yield attributing parameter as the number of fruits set per plant and number of fruits per plant were measured at the beginning and end of the reproductive stage eight and sixteen weeks respectively. The effects of growing media for the strawberry plant growth and productivity were analyzed using completely randomized block designs through analyzing the variance with a significance level of p < 0.05. The canopy area of the strawberry plants was calculated using the image processing technique applied in HSV colour space. Correspondingly, the vegetative stage and reproductive stage of T2 plants attained the maximum plant height of 16.93 ± 0.31 cm and 19.34 ± 0.21 cm, canopy area with 23.02 ± 1.94 cm2 and 28.78 ± 0.93 cm2, fresh weight of 18.00 ± 3.06 g, and 20.15 ± 3.49 g, dry weight of 5.15 ± 1.26 g and 6.66 ± 2.34 g and the number of fruits set per plant 18.83 ± 2.64 and number of fruits per plant 24.17 ± 2.14 followed by T1, T3, and CS respectively. A comparison of the relative growth and fruit yield at the vegetative and reproductive phases of plants T2 implied better performance. This study demonstrated that bio plus compost with synthetic nutrients act as a better source for the growth and production of strawberries under the greenhouse.

Machine Learning-Based Microclimate Model for Indoor Air Temperature and Relative Humidity Prediction in a Swine Building.

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.

A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer.

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.

Influence of aeration rate and reactor shape on the composting of poultry manure and sawdust.

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.