Home > 2nd U.S. Precision Livestock Farming Conference (USPLF 2023)

2nd U.S. Precision Livestock Farming Conference (USPLF 2023)

A HYBRID Event with Both IN-PERSON and VIRTUAL Attendance Options

Conference Theme: Field Implementation of PLF

Images of cows, chickens, hogs, and fish
USPLF Conference Logo

May 21 – 24, 2023
Knoxville, Tennessee

usplf2023.utk.edu

Exploring the Insights of USPLF 2023: Access Conference Publications

Thank you for being part of the success of USPLF 2023! The conference has concluded, but the knowledge shared and discussed during this event lives on through the insightful publications presented by our esteemed speakers.

Discover the wealth of information and research shared at USPLF 2023 by accessing the conference papers below. Dive into the groundbreaking ideas, innovative discoveries, and thought-provoking insights offered by our community of researchers and experts.

Click on the links provided to explore the wealth of knowledge presented during the conference. Should you have any questions or require further information, feel free to reach out to us.

We extend our gratitude to all participants, presenters, and contributors for making USPLF 2023 a resounding success. Let’s continue the journey of learning and discovery together.”

SESSION 1
Social Dimensions and Impacts
SESSION 2
Vision
SESSION 3
Data Modeling
SESSION 4
Disease Monitoring
SESSION 5
Precision Livestock Farming Systems
SESSION 6
Weight and Dimensions
SESSION 7
Heat Stress
SESSION 8
Broiler Welfare and Applications
SESSION 9
Identification
SESSION 10
Beef Cattle Applications
SESSION 11
Wearables and Behavior
SESSION 12
Ventilation, Indoor Air Quality, and Emissions
SESSION 13
Applications for Laying Hens – Aviaries
SESSION 14
Applications and Sensors in Swine Production
POSTERS
ABSTRACTS

Abstract. The behavior is the bird’s first response to an adverse environment capable of providing an indicator of wellbeing in real-time. The combination of behavioral parameters with the use of automated techniques improves the decision-making in poultry farms. The use of the sequential pattern mining technique applied to a broiler behavior dataset provides specific sequential patterns of behavior under conditions of different temperatures. The behavior dataset of broiler chickens aged 21 to 42 days was analyzed under thermoneutral and stressful temperatures. The Generalized Sequential Pattern Algorithm (GSP) analyzed the behavior database. The results indicated: (1) through the GSP algorithm it is possible to predict behavior patterns in the different variations evaluated from the behavioral sequences in the growing and finishing phases; (2) broilers exposed to temperatures at 8 °C both above and below thermoneutral conditions tend to decrease locomotor activities (3) short behavioral sequences were characteristic of heat stress; (4) behavior that is considered characteristic of heat stress, only appeared in the sequential behavioral patterns of chickens under heat stress; (5) sequence pattern mining is a valuable and simple technique to estimate the welfare of broilers, allowing the identification of temporal relationships between heat stress and consequent behavior of the broiler; (6) the welfare of broilers can be estimated through the length of the behavior sequences; (7) we were able to identify temporal relationships between heat stress and behavior of broiler, reassuring the need for further studies on the use of temporal behavior sequences in environmental controllers.

Abstract. This study analyzed the effects of locking plate fixation used for bridging of tibial segmental ostectomy and of cast immobilization on goat gait biometrics. We hypothesized that stable fixation of a segmental bone defect, using a locking plate construct, would result in minimal changes in biometric variables of gait in goats, but full-limb immobilization would result in lasting alterations in the immobilized limb’s gait kinetics. A pressure-sensing walkway was used to measure biometric characteristics for stride, gait, and walking force. Thirteen, non-lame adult Boer-cross goats were trained to walk over a pressure-sensing walkway prior to instrumentation. Segmental ostectomy was performed on the right hind tibia of each goat and the defect was stabilized locking plate fixation. The goats underwent right hindlimb cast immobilization for a period of one to three months after the surgical procedure. Data was collected preoperatively and then over twelve months postoperatively in goats with unrestricted mobility. Statistical analysis revealed no significant alterations in hindlimb kinematics or maximum force between surgery and cast immobilization. However, significant decreases in forelimb stride length and velocity were noted postoperatively but normalized prior to cast placement, suggesting the overall functional stability of fixation. Cast immobilization had a profound and sustained effect on gait with significant alterations in both forelimb kinetics and hindlimb kinetics and kinematics for the remainder of the trial period. Increased hindlimb asymmetry characterized by greater weight distribution and impulse to the left hindlimb was observed, suggesting the potential for long-term and/or permanent detrimental effects of prolonged limb immobilization.

Abstract. Currently, accurate quantification of tick infestation levels for effective selection of tick tolerant cattle, remains a global challenge. This study explored the convolutional neural networks algorithms for detection and quantification of tick burdens on cattle. The present study aimed to evaluate the efficacy of convolution neural networks in detection and quantification of tick burdens on cattle as an alternative for conventional visual methods. It is hypothesized that application of CNNs on the algorithms can detect and quantify ticks in cattle. The deep learning algorithms with architectures: “ConvNet ” and “MobileNet” were trained on a dataset of 1124 “thermograms” to detect ticks on cattle. The algorithms were evaluated and validated for rapid and accurate tick detection. The transformation of images during training was observed. ConvNet model achieved a training and validation accuracy of ~90 and 60%, respectively. Whereas MobileNet scored a training and validation accuracy of ~95 and 75%, respectively. For MobileNet, data augmentation and feature extractor layers were rescaled using Keras Functional API to build a model. After evaluation, the model was then ready to predict the number of ticks in a thermogram. Deep learning was successfully used to detect ticks on cattle using pre-trained CNNs, which facilitates tick control and selection of tick tolerant animals. It is recommended to use camera with better resolution and sensitivity and capturing images aiming at maximizing the contrast between ticks and the host animal to obtain higher accuracies when training the algorithm(s).

Abstract. Automated sensor data has incredible potential to provide actionable management information to dairy producers. Wearable sensors were developed to monitor individual animal behavior related to health and production. We hypothesized that sensor measurements may be associated with feed intake and other feed efficiency traits. Four different wearable technologies were evaluated, including: rumen bolus (1), ear tags (2), and milking collar (1) data with n=56–676 lactating Holstein cows, in parities 1-6 ranging from 24-278 Days in Milk (DIM). Daily milk yield and feed intake measured for 40-74d, weekly milk fat, protein and lactose and body weight and condition score (BCS) measurements were included for analyses. Individuals with health events were excluded. Statistical associations with dry mater intake (DMI) were evaluated in PROC MIXED of SAS, accounting for effects of contemporary/season, parity, DIM and energy sink variables (i.e., milk yield, fat, protein, lactose, BCS, metabolic body weight = BW0.75) known to impact cow feed efficiency. Sensor measurements included: activity (3), rumination (1), ear temperature (1), rumen pH (1), and temperature (1). Significant associations (p<0.05) with DMI were identified with nearly all measurements with the largest estimate for rumen temperature (-3.48 ± 0.32kg/°C; p<0.0001). Associations of feed intake with activity were identified across all sensor devices (p<0.05). Notably, all measurements were able to account for additional model variability in DMI beyond the variables routinely included in feed efficiency estimates. Though validation is required, wearable sensor data appear to provide new information that may help more accurately identify variation in individual cow feed intake.

Abstract. Cellulose paper, as a non-toxic, environmentally friendly, and renewable material, has low price, simple preparation and mass production, which is suitable for the substrate of field-effect biosensor (BioFET). However, its inherent moisture absorption causes swelling and contamination in serum, affecting the stability, sensitivity, and service life of BioFET; besides, its bad corrosion resistance in organic solvent increases the difficulty of modification. In this study, a micro- to nanoscale hydrophobic material was synthesized using octadecyltrichlorosilane, which were further decorated on cellulose paper with outstanding surperhydrophobicity. The superhydrophobic paper was applied as BioFET’s substrate, significantly enhancing stability and service life. In addition, organic silica gel instead of wax was constructed the BioFET’s barrier(SPFET) with high corrosion resistance to decrease the difficulty of modification. And pyrene carboxylic acid-modified semiconducting single-walled carbon nanotube(SWNTs-PCA) in organic solvent could decorate the BioFET with no need of further treatment. Two DNAzymes with different sensitivity was designed by changing sequence length and cleavage site, and functionalized with SPFET/SWNTs-PCA to formed Dual-BioFET, decreasing the interference of impurities in serum. After optimization, Dual-BioFET can detect the Ca2+ concentration in the range from 25 μM to 5 mM with a detection limit of 10.7 μM, which can meet the requirements of hypocalcemia diagnosis.

Abstract. The body of work involving computer vision and pigs is difficult to build on previous efforts because the availability of robust datasets is limited. Most computer vision efforts in applications for pigs have utilized custom datasets for individual challenges and specialized cases. An open-access benchmark dataset that encompasses pig production system complexities could attract cutting edge development in new computer vision and artificial intelligence techniques. We applied a custom animal behavior analysis tool (AVAT) to create a general-purpose dataset (PigLife) that contains video clips and images across most pig production phases in commercial systems: Breeding & Gestation, Farrow to Wean, Nursery, and Grow-Finish. We applied the AVAT to label every frame of the PigLife dataset with individual identification, instance segmentation, posture labels (4 classes), behavior labels (7 classes), and occlusion labels (5 classes). The PigLife dataset has been created to support benchmarking or model development. The dataset consists of 40 short video clips (5 seconds~2 minutes) containing scenarios of a single pig, multiple pigs (up to 17 pigs), and farrowing sow with a group of piglets. Each pig production phase includes at least three types of vision and environment variation (view angle, lighting condition, housing type, etc.). The framework for hosting the dataset has been created to accommodate further expansions of this dataset and with the opportunity to host additional datasets with common standards for formatting and sharing. We present a detailed description of the dataset and demonstrate its multi-task application (detection, segmentation, identification) and how to use metadata for error analysis.

Abstract. Herd monitoring through image analysis can be performed at either an individual or group level. Animal identification has become necessary to enable individual animal monitoring. The most commonly used method of livestock identification is via passive Radio Frequency Identification (RFID) tags. These low-cost tags are commonly pierced on the animals’ ears in a time-consuming and stressful process. They have a limited range at which they can be activated and read successfully, and multiple tags cannot be read concomitantly. Common elements in the farm environment made of metal can also be detrimental to the antenna’s effectiveness and further reduce the range of reading or prevent it. With those factors, it is not trivial to conclude that a more efficient and less invasive animal identification method that still enables realtime data analysis has become necessary. Image processing and computer vision could be the solution. This study attempts to produce a model for swine recognition that can not only distinguish between individual pigs but also adapt and continue to recognize growing pigs as the same individual over time.

Abstract. The purpose of this study was to develop a thermal balance model to allow for the prediction of dynamic responses of a dairy cow to high temperature (heat stress) conditions. The model is based on previous models and was improved using recent experimental data to allow animal responses to be adjusted at the same time to given environmental conditions. Tissue resistance, respiration rate and sweating rate were related to skin temperature. Metabolic heat production was related to characteristics of cows as well as to air temperature. Model inputs include cow body weight, daily milk production, pregnant days, dry-bulb temperature, relative humidity and air velocity. The model included three nodes (body core, skin and coat) and therefore three differential equations were developed to represent the dynamic heat change based on the energy balances of each node. The model was evaluated with experimental data measured on 20 cows from climate-controlled respiration chambers. The root mean squared error of prediction was 0.3°C for body temperature and 1.2°C for skin temperature, respectively. The model was likely to overestimate the body temperature, given some predictions were higher than the observations with mean bias of -0.11°C. The model gave higher values of body and skin temperatures when cows were exposed to the same warm conditions for a duration of 8 hours compared to a duration of 1 hour, which was in agreement with experimental findings. Besides, the benefits and limitations of different cooling methods were predicted under various environmental conditions.

Abstract. The body weight of broiler chicken is a crucial indicator for productivity and animal welfare. Accurate, continuous and rapid estimation of broiler body weight can effectively reduce the workload of farmers and better track the growth status and performance of broilers, which can be achieved via developing and applying precision livestock farming technologies. The objective of this study is to develop a multi-view depth camera vision system for estimating body weight parameters of individual broilers in real time. Three depth cameras were used to capture images of individual broilers from three directions (i.e., top view, two side views). Based on the depth data, the broiler volume was calculated using a numerical integration method. A back propagation (BP) neural network prediction model was developed to predict body weight using the parameters of bird’s back width, body width, body height and volume. The results showed that the prediction model could predict the body weight of broilers well, with the coefficient of variation and relative error within 5% and 10%, respectively. The system can be used as a prototype of vision-based method to measure the body weight of broilers and provide an automated solution for measuring bird body weight in broiler production industry.

Abstract. Voluntary soaking systems for heat stress abatement provides the cow freedom of choice and the opportunity to self-manage heat stress. Hence, this precision dairy technology (PDT) targets the individual cow needs and provides the animal with agency. We have developed and validated a voluntary radiofrequency controlled soaking system capable of identifying cows automatically and providing them with a 10 second soaking cycle at 30L/min. in our prior study, there was great individual variation in voluntary soaker use ranging from 0 to 227 soakings/d, (13±30; mean ± SD). Thus, the objective of our project was to determine the effects of the voluntary soaking system on the behavior, physiology, and production of dairy cows milked in voluntary milking systems. During the study, the voluntary radio-frequency controlled soaking system operates in three commercial dairy farms with voluntary milking systems (VMS). Following our previous study, for two weeks, cows are trained to use the voluntary soaker by guiding them to the system sporadically throughout the day. At enrollment, animals are randomly assigned to either have unlimited voluntary soakings or no voluntary soakings by crossover design. Each treatment period of 21 days in duration (14 days for treatment adaptation and 7 days of data collection). Treatments are balanced by lactation number, milk yield, and body condition score. We hypothesize that cows with unlimited access to the voluntary soaking system will have lower respiration rates, lower panting scores, lower dehydration scores, and greater milk yield when compared to cows without access to the voluntary soaking system.

Abstract. In recent years, the livestock breeding industry is being converted into large-scale, intensive and intelligentize production mode. The Internet of things (IoT) is under rapid development, which promotes the development of smart agriculture. The improvement of agricultural IoT is also driving the development of precision livestock farming. Based on the structure of IoT, an intelligent service platform of facility environment is built. The platform consists of four layers including information awareness layer, network layer, management service layer and application layer. Through using the distributed network architecture, asynchronous data transmission and the distributed file system, the platform can centrally manage multiple farms data. The functions are included in the platform as accessing and displaying the internal and external environmental data and water and electricity consumption of livestock house, performing data analysis, managing production data as well as the basic data of farms, and managing the system. The service platform was applied in an actual layer house.

Abstract. The accurate and rapid detection of objects in videos facilitates the identification of abnormal behaviors in pigs and the introduction of preventive measures to reduce morbidity. In addition, accurate and effective pig detection algorithms provide a basis for pig behavior analysis and management decision-making. Monitoring the posture of pigs can enable detection of the precursors of pig diseases in a timely manner and identify factors that impact pigs’ health, which helps to evaluate their health status and comfort. Excessive sitting represents abnormal behavior when pigs are frustrated in a restricted environment. The main contributions of this paper are as follows: a human-annotated dataset of standing, lying, and sitting postures captured by 2D cameras during the day and night in a pig barn was established, and a simplified copy, paste, and label smoothing strategy was applied to solve the problem of class imbalance caused by the lack of sitting postures among pigs in the dataset. The improved YOLOX has an AP0.5 of 99.5% and AP0.5–0.95 of 91% in pig position detection; an AP0.5 of 90.9% and AP0.5–0.95 of 82.8% in sitting posture recognition; and an mAP0.5 of 95.7% and mAP0.5–0.95 of 87.2% in all posture recognition. The method proposed in our study can improve the position detection and posture recognition of grouped pigs effectively, especially for pig sitting posture recognition, and can meet the needs of practical application in pig farms.

Abstract. Thermal environment impacts livestock welfare and productivity. Animals’ behavior patterns are altered by high temperatures; however, little is known about how behavioral shifts can be moderated or how they contribute to productivity declines. The use of wearable inertial measurement unit (IMU) sensors has increased the feasibility of behavioral monitoring in livestock due to the success of these sensors at classifying behaviors; however, verification of IMU-based behavior classification during heat stress is incomplete. Our objective was to use an open-source IMU to explore animal behavior classifications under different temperatures. Four crossbred sheep were fitted with an IMU sensor comprised of a generic MPU- 9250 9 degree of freedom IMU, an Arduino-compatible microprocessor, and a data storage module. Animals were assigned to 1 of 2 groups to 1 of 2 rooms where they were housed for 20 d before switching rooms. The thermal environment was changed five times (every 4 d) starting at thermoneutral (20°C, 27°C, 35°C, 27°C, 20°C) in one room and hot (35°C, 27°C, 20°C, 27°C, 35°C) in the other room. Four behaviors (eating, lying, standing, and ruminating) were recorded through visual observation. A random forest regression was capable of distinguishing eating, lying, and lying and ruminating behaviors with similar accuracy (~76%), and similar, but lower accuracy for standing and standing and ruminating (67%). These accuracies are lower than observed in previous studies conducted in thermoneutral environments suggesting that additional data collection and verification efforts are needed to ensure IMU sensing can be leveraged in heat-stressed conditions.

Abstract. Heart rate (HR) measurement can be used as a physiological indicator of heat stress in livestock; however, monitoring HR in animals remains a challenge and requires animal restraint. Photoplethysmography (PPG) sensing has been proposed as an alternative, non-invasive tool for HR monitoring. The purpose of this work was to evaluate the precision and accuracy of a PPG sensor in monitoring HR in sheep experiencing heat stress. Four crossbred sheep were divided into 1 of 2 groups exposed to different thermal environments changing every 4 days. The first group was first exposed to a low temperature (20°C, 27°C, 35°C, 27°C, 20°C) and the second group was first exposed to a high temperature (35°C, 27°C, 20°C, 27°C, 35°C). Manual heart rates (ground truth observations) were collected via stethoscope twice daily and automatically (sensed observations) by an open-source sensor comprised of a microprocessor (SparkFun ® ThingPlus) and a PPG (SparkFun ® MAX30101 & MAX32664) placed on a neck collar. Pearson correlation, root means squared prediction errors (RMSE), and a random forest regression were used to explore precision, accuracy, and HR classification, respectively. With a negative correlation (-0.23), an RMSE of 36.41 %, and classification accuracies ranging from 40 to 55%, we concluded that PPG sensors were ineffective on measuring HR in sheep and future work should focus on securing the sensor more effectively and leveraging data analytics to reduce noise within the measurement.

Abstract. Manually observing individual chickens in commercial broiler production housing systems has become a tedious and demanding job. Vision-based crowd counting and tracking systems using advanced computer vision and machine learning methods have gained popularity over the years to locate subjects and analyze location-based data for behavioral research automatically. Following this trend, automated crowd counting and tracking have become very important in quickly identifying individual chickens in large groups for populational and behavioral analysis. However, in real-world scenarios, fully supervised deep learning methods usually learn to predict through a training process that requires an extensive annotation of densely populated subjects in thousands of images. Directly employing deep learning models that are trained on existing datasets to a new dataset suffers from a significant performance decrease due to the domain gap. Therefore, we introduce a new training approach to the crowd-counting task toward a domain adaptation setting where the deep learning algorithm utilizes entropy minimization and adversarial learning to alleviate the distributional discrepancy between the source domain and the target domain. While the network is trained with labelled data, samples without labels from the target domain are also added to the training process. In this process, the entropy map is computed and minimized in addition to the adversarial training process designed in parallel. Experiments on Shanghaitech, UCF_CC_50, and UCF-QNRF datasets prove a more generalized improvement of our method over the other state-of-the-art in the cross-domain setting.

Abstract. Facial expression is important information about expressed emotion. It is a comprehensive reflection of livestock physiology, psychology, and behavior. It can be used to evaluate livestock welfare. Therefore, the successful recognition of animal expressions is of great significance in the field of animal welfare. We propose a joint Multi-attention Cascaded LSTM model (MA-LSTM) framework to recognize pig’s temporal facial expressions in our work. Firstly, a simplified multi-task convolution neural network is used to detect and locate the pig face in the frame image. Secondly, a multi-attention mechanism is proposed, which makes use of the characteristics that different feature channels have different visual information and corresponding peak response regions are also different. By clustering the regions with similar peak responses, the facial salient regions caused by facial expression changes are captured. Facial salient regions are used to focus on subtle changes in the pig face. Finally, the convolution feature and attention feature are fused and input into LSTM to classify the data. The proposed method can effectively extract the key area features of the pig face required by the network according to the difference in animal expressions, and ensure the timing of pig face expression recognition at the same time. To validate the effectiveness of our proposed model, we work with technicians to collect an expression dataset from a pig breeding enterprise in Nong’an County, Jilin Province, China.

Abstract. The safety and welfare of horses and riders is of utmost importance to the equine community, especially in those disciplines which are high-risk, like jumping. Wearable technologies leveraging inertial motion sensing coupled with GPS have shown promise at evaluating equine biomechanics to provide quantitative measurements of jump quality, which if sensitive enough, could help provide real-time feedback to riders to facilitate improved horse health and performance. Our objective was to evaluate sensitivity of sensed biomechanical measurements to variation in horse and rider experience level. Participants were solicited on a volunteer basis through an email-based referral request. A total of 6 farms participated, allowing data collection on 29 horses 22 riders, resulting in a total of 24 total pairings with usable data. Horses and riders were categorized into “Beginner”, “Intermediate”, or “Advanced” experience levels based on previous competition history. Each horse was equipped with a sensor which was affixed centrally to the girth, and data were collected for the duration of a regular training session. Data were analyzed using linear mixedeffects model with breed, farm, and weather conditions as random effects. Horse and rider experience level and their interaction, jump height, and jump type were considered fixed effects. Nearly all biomechanical factors were affected by horse and rider experience level or the interaction of these factors, suggesting that inertial sensing coupled with GPS may be a useful tool for more quantitatively benchmark horse jump parameters toward the goal of enhancing sporthorse welfare.

Abstract. Photoplethysmography (PPG) sensing is used to measure heart rate and oxygen saturation in humans. Despite success in the human health industry, there has been limited PPG sensor testing in the equine industry, and the translatability of these sensors to horses is unknown. The purpose of this study was to evaluate the accuracy of wearable PPG sensors, originally designed for humans, in monitoring distal limb pulse of horses. The study utilized six mature (9 to 22 years of age) horses of various breeds and sexes (5 geldings, 1 mare). Data were collected by placing a modified exercise boot containing a PPG sensor on the distal limb. Each horse was subjected to 80 readings: 20 per limb with 10 on each side of the limb. Heart rates were taken manually with a stethoscope prior to each recording. To assess sensor accuracy, the Root Mean Square Prediction Error (RMSPE) of raw values was calculated, RMSPE within limb and location were also calculated for each horse. A general linear model with fixed effects for limb and location and random effect for horse was used to evaluate agreement between measured and sensed data and to evaluate the influence of device placement on accuracy. Results indicated that the PPG-based sensor was ineffective at measuring horse heart rates accurately, irrespective of location; however, it was accurate in some horses and extremely inaccurate in others. Future work around designing effective PPG sensors for horses should evaluate this horse-to-horse variation to better understand casual factors.

Abstract. Respiratory diseases are a significant cause of mortality and decreased productivity in pigs. In 2018, the African Swine Fever pandemic resulted in 43.46 million pigs killed either from the disease itself or from culling required to prevent disease spread. This yielded a total economic loss of 0.78% of China’s GDP in 2019. In light of this recent catastrophe, disease surveillance towards early prevention has been brought to the forefront of precision livestock farming. However, frequent manual checking of pig cough occurrence is labor-intense and difficult to manage especially on larger farms. An automatic pig cough monitoring device that operates ig coughs 24/7 is, therefore, a valuable tool for the farm manager. In this paper, we detail an end-to-end solution from detection to the communication of surveillance to the farm manager; the development of which requires a multi-disciplinary skillset spanning from hardware to software. In particular, our solution begins with a TinyML edge-based artificial intelligence-based detection algorithm, allowing for an objective measure of pig cough occurrence and classification throughout the farm. This is followed by an IoT ecosystem that ensures robust communication of surveillance throughout the farm and to a specified endpoint that is in reach of the farm manager. At this endpoint is a dashboard that distils and presents this information in a convenient and informative manner.

Abstract. Climate change causes a higher risk of heat stress (HS) in cows, even in moderate-climate countries. HS has consequences on production and health. Early detection of HS can help a farmer intervene and thus limit the negative consequences. This research assessed the effect of different risk levels for developing HS (low (LR), mild (MR) and high (HR)) on activity when cows are kept indoors year-round in a naturally ventilated barn. Commercial accelerometers on the neck and leg (SmartTag, Nedap Livestock Management) measured lying, eating and ruminating on a farm with 130 dairy cows. Temperature and relative humidity were measured and the THI was calculated. Based on average THI, three periods, of three or five consecutive days, were defined: LR (THI<68), MR (68≤THI≤75) and HR (THI>75). Effect on the behaviors was analyzed with an ANOVA with risk level (LR, MR, HR), parity (≤2;>2) and lactation days (<100;100-200;>200) as independent variables. Lying decreased from 736 (LR) and 749 (MR) to 629 minutes/day (HR, P<0.05). Rumination decreased from 569 (LR) and 577 (MR) to 503 minutes/day (HR, P<0.05). Eating increased from 253 (LR) to 289 minutes/day (MR, P<0.05) and decreased to 137 minutes/day (HR, P<0.05). In conclusion, all behaviors decreased in the HR compared to LR and MR. No changes were found in behaviors in the MR compared to the LR except for an increase in eating time. This increase is unexpected and needs more research to study whether this is a repeatable change and might be an early indicator of HS.

Keynote Speakers

Dr. Angelica Van Goor
National Program Leader
USDA-NIFA
Ms. Kalyn Reed
North American Business Development Manager
Evonik
Dr. ir. Dries Berckmans
CEO
SoundTalks
Conference Agenda
Scholarship Opportunity

Topics

Sensors and Sensing in PLF

  • Development of sensor systems for animal and animal facilities
  • Implementation of sensor systems
  • Sensing technologies for high-throughput phenotyping
  • Robotics in animal facilities

Data Management and Algorithm Development

  • Artificial Intelligence (AI) solutions for animal monitoring and management
  • Internet of Things (loT) applications for data management
  • Analysis on multitype data (e.g., image, video, sound, etc.)
  • Data security

Measuring, Modeling, and Managing of Dynamic Responses

  • Real-time animal welfare and behavior monitoring
  • Advanced modeling techniques in PLF
  • PLF for detecting animal disease
  • Precision animal environment and nutrition management
  • PLF solutions for management decision-making
  • Case studies of PLF field applications

Societal Impacts of PLF

  • Economics of PLF systems
  • Ethics in PLF
  • Consumer and farmer perceptions on PLF
  • Challenges and opportunities for PLF
  • Sustainable animal production

Commercial PLF Systems and Field Application Experience – Open for farmers and PLF system providers with abstract submission (full paper is not required)

Key Deadlines

Abstract submission opening: May 15, 2022
Abstract submission closing: July 31, 2022
Invitation to submit full paper: August 31, 2022
Full paper submission due: January 15, 2023
Review feedback to authors: March 15, 2023
Final full paper submission due: April 15, 2023

Field Tours

UTIA Robotic Dairy Farm, UTIA Precision Beef Farm,
Johnson Research and Teaching Unit

Venue

University of Tennessee Conference Center
600 Henley Street, Knoxville, TN 37902, USA

Parking at Locust Street Garage

540 Locus St SW, Knoxville, TN 37902, USA
For participants not staying at the Marriot Knoxville Downtown, nor using their valet parking

Lodging

Marriott Knoxville, Downtown
525 Henley Street, Knoxville, TN 37902, USA

Contact

Dr. Robert Burns – rburns@utk.edu
Dr. Yang Zhao – yzhao@utk.edu

Organized by

Photos of UT and Knoxville, Organizer logos.

Sponsors

Partnering Sponsor

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Supporting Sponsor

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Vendor

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Scholarship Sponsor

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Collaborators

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