Perches as Cooling Devices for Reducing Heat Stress in Caged Laying Hens: A Review

Heat stress is one of the most detrimental environmental challenges affecting the biological process and the related production performance of farm animals, especially in poultry. Commercial laying hens have been bred (selected) for high egg production, resulting in increased sensitivity to heat stress due to breeding-linked metabolic heat production. In addition, laying hens are prone to heat stress due to their inadequate species-specific cooling mechanisms resulting in low heat tolerance. In addition, hens have no sweat glands and feathering covers almost their entire body to minimize body heat loss. The poultry industry and scientists are developing cooling methods to prevent or reduce heat stress-caused damage to chicken health, welfare, and economic losses. We have designed and tested a cooling system using perches, in which chilled water (10 oC) circulates through a conventional perch passing through the layer cages to offer the cooling potential to improve hen health, welfare, and performance during acute and chronic periods of heat stress (35 oC). This review summarizes the outcomes of a multi-year study using the designed cooled perch system. The results indicate that conducting heat from perching hens directly onto the cooled perch system efficiently reduces heat stress and related damage in laying hens. It provides a novel strategy: perches, one key furnishment in cage-free and enriched colony facilities, could be modified as cooling devices to improve thermal comfort for hens during hot seasons, especially in the tropical and subtropical regions

Evaluation of trailer thermal environment during commercial swine transport

Transport is a critical factor affecting swine welfare in modern U.S. commercial pork production. Broad temperature ranges encountered during transport can challenge pig welfare and have been shown to increase the number of dead or down pigs following transport. Despite the general understanding of the relation between challenging transport conditions and pig welfare, little quantitative data exist which document conditions within transport trailers. To better characterize the thermal environments experienced by trailered pigs during hot, mild, and cold weather, The National Pork Board commissioned this observational study to evaluate the thermal environment during commercial pig transport when the trailer environment was managed according to a set of industry guidelines. The overall goal of this observational study was to identify weather conditions and micro-climates within the trailer that created thermal challenges for the pigs. In this study, 84 temperature sensors were placed across trailer cross-sections in six evenly distributed zones within the transport trailer to measure air temperature experienced by the pigs. Six relative humidity and temperature probes were installed on the central ceiling of each zone to measure a representative moist-air state point for each compartment. Eighteen to twenty-four floor temperature sensors were placed onto the trailer floor prior to each monitoring trip to measure trailer floor/bedding temperature. Transport thermal environment data from forty-three monitoring trips were collected from May 2012 to February 2013, with trailer management conducted by a commercial hauler following the National Pork Checkoff Transport Quality Assurance (TQA) guidelines. The thermal environment profile within the trailer was used to evaluate the thermal conditions to which pigs were exposed over a broad range of outside conditions [-14 to 38°C (7 to 100°F)] encountered over the four seasons of this study. Results indicate that for outside temperature below -7 (20°F) and above 32°C (90°F) , pigs experienced extreme thermal conditions inside at least some portions of the trailer when managed according to current TQA guidelines. The ventilation patterns inside the trailer did not follow the same trend for all monitoring trips, revealing a potential to manipulate ventilation patterns with trailer management strategies. This approach for improving the thermal extremes needs further exploration. The effectiveness of fans and misting for cooling the pigs was critically impacted by the location and coverage areas of the spray nozzles and fans. When outside temperature ranged from 10 to 20°C (50-68°F), trailer environment was within acceptable thermal limits without misting the pigs. During cold weather, frozen floor conditions were observed, with floor temperature as cold as -20°C (-4°F) recorded in some areas of the trailer. No evidence was found to suggest that bedding depth had a measurable effect on the thermal comfort of the pigs, and its presence might increase the severity and likelihood for the pigs to be loaded onto freezing or frozen bedding in extreme cold weather. Our data revealed no critical problems with boarding level recommendations based on current TQA guidelines, but indicated that industry guidelines could be modified to offer greater flexibility for drivers for boarding and bedding

Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support precision beef cattle management. Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (\u3e12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry

Design of Air Thermal Recovery Experiment Device and Analysis of Thermal Efficiency

AbstractIt was significant for energy save and environment protection that heat recovering high temperature and high humidity air directly discharged into the atmosphere. Based on high temperature and high humidity mine retention tower and return air fluid, the air heat recovery simulation experiment device was designed, and air heat recovery experiment was researched by using spray heat exchanger. Heat exchange efficiency was tested by experiment with certain water spray coefficient and different nozzle number, and experiment result that was error analysis and data correction through compared with theoretical analysis results. Result showed that the air heat recovery simulation device can recycle heat of air fluid stably, and rise temperature of spray cold water to about 15°C. The heat exchange efficiency would be 90% with certain water spray coefficient, which is basically identical with theoretical analysis. Energy saving effect of this device was remarkable with high heat exchange efficiency

Classifying Ingestive Behavior of Dairy Cows via Automatic Sound Recognition

Determining ingestive behaviors of dairy cows is critical to evaluate their productivity and health status. The objectives of this research were to (1) develop the relationship between forage species/heights and sound characteristics of three different ingestive behaviors (bites, chews, and chew-bites); (2) comparatively evaluate three deep learning models and optimization strategies for classifying the three behaviors; and (3) examine the ability of deep learning modeling for classifying the three ingestive behaviors under various forage characteristics. The results show that the amplitude and duration of the bite, chew, and chew-bite sounds were mostly larger for tall forages (tall fescue and alfalfa) compared to their counterparts. The long short-term memory network using a filtered dataset with balanced duration and imbalanced audio files offered better performance than its counterparts. The best classification performance was over 0.93, and the best and poorest performance difference was 0.4–0.5 under different forage species and heights. In conclusion, the deep learning technique could classify the dairy cow ingestive behaviors but was unable to differentiate between them under some forage characteristics using acoustic signals. Thus, while the developed tool is useful to support precision dairy cow management, it requires further improvement

DESIGN AND DEVELOPMENT OF A BROILER MORTALITY REMOVAL ROBOT

Manual collection of broiler mortality is time-consuming, unpleasant, and laborious. The objectives of this research were: (1) to design and fabricate a broiler mortality removal robot from commercially available components to automatically collect dead birds; (2) to compare and evaluate deep learning models and image processing algorithms for detecting and locating dead birds; and (3) to examine detection and mortality pickup performance of the robot under different light intensities. The robot consisted of a two-finger gripper, a robot arm, a camera mounted on the robot’s arm, and a computer controller. The robot arm was mounted on a table, and 64 Ross 708 broilers between 7 and 14 days of age were used for the robot development and evaluation. The broiler shank was the target anatomical part for detection and mortality pickup. Deep learning models and image processing algorithms were embedded into the vision system and provided location and orientation of the shank of interest, so that the gripper could approach and position itself for precise pickup. Light intensities of 10, 20, 30, 40, 50, 60, 70, and 1000 lux were evaluated. Results indicated that the deep learning model “You Only Look Once (YOLO)” V4 was able to detect and locate shanks more accurately and efficiently than YOLO V3. Higher light intensities improved the performance of the deep learning model detection, image processing orientation identification, and final pickup performance. The final success rate for picking up dead birds was 90.0% at the 1000-lux light intensity. In conclusion, the developed system is a helpful tool towards automating broiler mortality removal from commercial housing, and contributes to further development of an integrated autonomous set of solutions to improve production and resource use efficiency in commercial broiler production, as well as to improve well-being of workers

MoWE: Mixture of Weather Experts for Multiple Adverse Weather Removal

Currently, most adverse weather removal tasks are handled independently, such as deraining, desnowing, and dehazing. However, in autonomous driving scenarios, the type, intensity, and mixing degree of the weather are unknown, so the separated task setting cannot deal with these complex conditions well. Besides, the vision applications in autonomous driving often aim at high-level tasks, but existing weather removal methods neglect the connection between performance on perceptual tasks and signal fidelity. To this end, in upstream task, we propose a novel \textbf{Mixture of Weather Experts(MoWE)} Transformer framework to handle complex weather removal in a perception-aware fashion. We design a \textbf{Weather-aware Router} to make the experts targeted more relevant to weather types while without the need for weather type labels during inference. To handle diverse weather conditions, we propose \textbf{Multi-scale Experts} to fuse information among neighbor tokens. In downstream task, we propose a \textbf{Label-free Perception-aware Metric} to measure whether the outputs of image processing models are suitable for high level perception tasks without the demand for semantic labels. We collect a syntactic dataset \textbf{MAW-Sim} towards autonomous driving scenarios to benchmark the multiple weather removal performance of existing methods. Our MoWE achieves SOTA performance in upstream task on the proposed dataset and two public datasets, i.e. All-Weather and Rain/Fog-Cityscapes, and also have better perceptual results in downstream segmentation task compared to other methods. Our codes and datasets will be released after acceptance

Engineering solutions to address several current livestock and poultry housing challenges

Robust and sustainable controlled environment agriculture is critical to achieve optimal animal production efficiency with the least impacts to animal welfare and our environment. Achieving optimal agricultural environment is a consistent challenge for current livestock and poultry industries. Example challenges include: 1) high pre-weaning mortality of neonatal piglets in typical farrowing swine facilities, 2) maintaining egg production and sufficient well-being for laying hens under heat stress events, and 3) compromised air quality issues in most poultry housing systems. My research seeks to provide engineering solutions to address these three challenges currently faced by the animal production industry. This dissertation details research findings for projects specifically addressing these three challenges. In the U.S., pre-weaning mortality ranges from about 9 - 15% of live-born piglets. Hypothermia and low vitality are believed to be among the leading causes of pre-weaning piglet mortality. To identify neonatal piglets that are prone to hypothermia, a mathematical model was developed to predict neonatal piglet rectal temperature using surface temperatures. Time series rectal temperatures (RT), thermal images, and corresponding farrowing room conditions were recorded for a group of 99 neonatal piglets. Results showed that RT of the piglets dropped immediately after birth, with a mean drop of 4.4°C recorded in the first 15 min. Piglets experienced the lowest RT at 30 min after birth, reaching a mean low temperature of 33.6°C, approximately 5°C below birth temperature. Linear regression models were developed and assessed, with the refined linear regression model providing a more reliable prediction of piglet RT. The refined regression model presented can be used to provide a direct prediction of RT from simple measurement of the piglet ear surface temperature, with an uncertainty of about 1°C, and thus can be used as a convenient prediction tool for rapid estimation of piglet RT under typical farrowing conditions. Alternative cooling methods, especially a cooled perch system, present an intriguing opportunity for heat removal from birds under heat stress. A perch system was designed and used to examine the effects of water-cooled perches as a cooling alternative on hen performance, production, health and welfare on caged White Leghorn hens exposed to heat stress. The cooled perch system consisted of two replicates of three-tier cage units with galvanized perch pipes forming a complete loop in each tier in which cooled water circulated. Flow for each loop was provided by loop pumps that drew chilled water from an open thermal storage and returned it to the same manifold. Each thermal storage was cooled by continuously circulating water through a water chiller. Each loop pump was thermostatically controlled based on cage air temperature. The performance of the cooled perch system was assessed for a stable system operation period by analyzing the water flowrate, characterizing the loop water temperature rise profile, and using this information to establish estimates of the system total heat gain. It was noted that the circulation pump performance decreased over time, and there was a discrepancy between the pumps’ actual output and that provided by the manufacturer. Different loops and CP replicates did not have equal performance regarding loop water temperature rise and loop net heat gains. There was a strong correlation between room temperature and perch heat gain, indicating natural convection from ambient air to perch surface was the major contributor to heat gain over other heat transfer mechanisms including hen conduction. Design criteria useful for future applications of cooled perch were provided. An average daily heat gain of about 128 W/m perch length or 43.2 W/hen housed was estimated, based on 12-h day/12-h night air temperature of 35/28C and an average loop inlet water temperature of 20C. A peak-day system heat load of 64.4 kWh was estimated and required a thermal storage capacity of 2.5 kWh. Information regarding hens’ perching behavior, footpad area estimation, and thermal conductance or resistance of the footpad were provided. The U.S. egg industry faces growing pressure from consumers and retailers to transition egg production from conventional caged systems to alternative housings such as “cage-free” aviaries and enrichable caged systems, despite research that has established that alternative housing has more challenges to maintain desired indoor air quality parameters. Given the current limited knowledge regarding the interior environment in such housings, it is important to evaluate the thermal environment and air quality in order to provide additional scientific information for alternative hen houses. Indoor air temperature, RH, CO2 and NH3 concentrations were continuously monitored using the six intelligent Portable Monitoring Unit (iPMUs) for three different laying hen houses, including two aviaries and an enrichable cage house from February to July 2019. The thermal environment and the gas concentrations during the study were not uniformly distributed spatially in the houses. There was a variation in temperature distribution between the top and the bottom levels for all three houses. Hens in all three houses experienced THI conditions from normal to emergency (hot and cold) categories. The average CO2 and NH3 concentrations for the three hen houses ranged from approximately 400 to 5800 ppm and 0 to 94 ppm, respectively. During monitoring, 75% of the measurements in the three houses were lower than 5,000 ppm for CO2 and below 60 ppm for NH3 concentrations. Both winter minimum ventilation and summer tunnel ventilation were not sufficient during some monitoring periods, and further improvement to the ventilation management strategies would be helpful. Management practices to monitor the interior thermal environment, investigate the air inlets performance (number of inlets and air velocity), adjust operational static pressure (which drives the air inlets), or which fans to operate during coldest conditions, should be considered by the producer