Filter Airflow prediction model development

The implementation of air filters on commercial swine farms has effectively reduced the frequency of airborne disease transmission. However, efficiently managing filter lifespan remains a challenge and an unknown operational cost for filtered swine facilities. Individual filter testing protocols are time consuming and expensive for producers. The objective of this study was to develop a predictive model for estimating airflow for an individual filter in situ by comparing multiple machine learning models to eliminate the need for manual, individual filter testing for filter resistance. The data set was generated from a custom Air Filter Environmental Testing Chamber that mimics on farm operational conditions with a low static pressure drop per filter and ground level installation. Model parameters were developed from a six-month long data set. The models were developed when the chamber was running with a new set of pre-filters and a set of five-month old v-bank filters. The developed models include a single input linear regression, multiple linear regression, and random forest models. A single input linear regression was not an effective method for predicting the chamber airflow, R2=0.08. The multiple linear regression moderately explained the variation in the data, R2=0.77. The random forest models performed the best for predicting the test chamber airflow with both models featuring R2= 0.98. The results and models from this study will be used to determine the feasibility of an on-farm application

Infrared proximity measurement system development and validation for classifying sow posture

The rapidly progressing field of precision livestock farming is becoming increasingly dependent on the utilization of camera technology. Integration of camera technology involves substantial intellectual input and computational power to acquire, process, and interpret images in real-time. Further, cameras and the necessary computational power can be cost-prohibitive and subsequently, become a constraint for application in a commercial livestock and poultry production systems. The purpose of this study is to develop an infrared proximity sensor based system to serve as a substitute a camera system to perform real-time monitoring of sow posture in farrowing stalls for a potentially lower cost and computational power. Monitoring sow posture can provide producers an indicator of farrowing and aid in evaluating sow demeanor during lactation. During the development of this system the long range infrared (IR) proximity sensors were individually calibrated, a sow posture algorithm was developed, and the IR-Sow Posture Detection System (IR-SoPoDS) system was evaluated in a commercial setting to a Kinect V2® camera for a range of sow postures. Average accuracy of the sow posture algorithm on the training data was found to be 96%. The overall accuracy of the IR-SoPoDS system across the three sow frame sizes were:87% (small), 90% (medium), and 89% (large). This IR-SoPoDS system shows a strong promise for further development for sow posture and behavior detection in the farrowing stall environment

Control algorithm development and simulation for comparing evaporative pads and sprinklers for grow-finish pigs

Seasonal variability attributed to heat stress (HS) has a large economic impact on the US swine industry by reducing daily gain and finishing market weights. Strategies to mitigate HS lack evidence showing effectiveness in different climates and have not been adequately controlled to provide a thermally optimum environment for pigs. Hence, the goal of this study was to describe the initial experimental design and instrumentation as well as develop innovative control algorithms for operating evaporative pads (EPs) and sprinklers. Located in northeast Iowa, a four room (~1,875 head per room) grow-finish facility featured side-by-side rooms separated by a hallway. Three thermal environment sensor arrays (TESAs) quantifying dry-bulb and globe temperature, relative humidity, and airspeed were placed in each room and served as feedback for control system to evaluate the thermal environment and potential HS conditions. The newly developed housed swine heat stress index (HS2I) combines TESA measurements and optional wetted skin to assess the potential for HS onset. Custom software interfaced with a multifunction data acquisition board was used to condition TESA signals and control EP pumps and sprinkler solenoids. A control algorithm was developed and simulated using data collected during a 23-d period in July 2017 to preliminarily evaluate the robustness and potential control decisions. Linear models developed to predict indoor dry-/wet-bulb temperature showed good agreement with measured data and will be critical for developing a control systems to selects the best cooling system given forecasted ambient conditions

Thermal environment assessment and controller performance comparison for a wean-finish barn

The thermal environment (TE) inside swine facilities has a substantial impact on animal growth performance and facility energy usage; therefore, proper control and measurement are required to maintain the optimal TE that maximizes feed efficiency and consumes minimal resources. An inexpensive and novel network of 44 thermal environment sensor arrays (TESAs) capable of capturing the spatial and temporal distribution of the TE were deployed in August 2016 inside a two-room (designated as North; N and South; S), wean-finish barn (~1200 hd and 22 TESAs per room) and placed about 1.8 m above the slatted floor. All TESAs simultaneously measured and averaged 20 samples of dry-bulb temperature, back globe temperature, airspeed, and relative humidity at 1 min intervals. The objectives of this research were to: (1) summarize the TE observations from this monitoring period and (2) develop some preliminary analysis methods to quantitatively compare the TE in each room. Each room of the fully mechanically, power-tunnel ventilated facility featured independent TE control (i.e., fan, heater, inlet, and tunnel curtain operation) by a unique ventilation controller. A set point uniformity coefficient (γSP; binned by ambient temperature; ta) was used to assess ventilation controller performance and a two-sample (from random subsampling of ta bins) t-test was used to test if γSP in each room was statistically different. Results showed a statistically significant difference between N and S room γSP for ta bins8°C (p = 0.26; p = 0.07; p = 0.73; p = 0.31). This is a preliminary and novel approach to assessing ventilation controller performance and future approaches will need incorporate all parameters of the TE