Recipient Organization
UNIVERSITY OF TENNESSEE
2621 MORGAN CIR
KNOXVILLE,TN 37996-4540
Performing Department
Entomology & Plant Path - RES
Non Technical Summary
This is a two-year applied research project focused on enhancing agricultural biosecurity. Tick-borne diseases affect 80% of the world's cattle population and ongoing tick-pathogen biosecurity threats to the United States include Texas Cattle fever ticks causing Texas Cattle fever and bont ticks causing Heartwater in cattle. Asian longhorned ticks (ALTs) represent a new and emerging pest of companion animals, livestock, and wildlife. Since its discovery in the US in 2017, ALTs have been recovered from seventeen states and its feeding has been linked to sheep death, calf death, and cattle infection with T. orientalis Ikeda. Direct damage from ALT blood feeding includes significant economic losses in livestock including animal death and animal weight loss, milk loss, and direct damage to pelts / hides. Current surveillance methods for ticks on cattle are labor intensive as personnel must restrain an animal and then risk personal injury via physically touching and searching for ticks on animals making tick surveillance limited to the head and neck of an animal or not possible. Thus, the overarching problem is that while many exotic ticks and associated pathogens are poised to enter the United States, the current methods for detecting ticks on cattle (termed scratching) is impractical for producers, difficult for veterinarians, and primarily limited to the Texas Cattle fever tick eradication program. The proposed project addresses this severely neglected and critical need to help stakeholders use practical decision-making tools for producing sustainable and healthy cattle and limiting the movement of exotic and invasive tick species. Our long-term goal is to develop a data-driven IPM plan that integrates active and passive surveillance and prevents establishment of exotic and invasive ticks at a new site. There is a neglected and critical need to develop IPM strategies for ticks and their pathogens at livestock markets; thus, the objective of this proposal is to develop an automated method for detection of ticks on cattle at livestock markets. We will test the hypothesis that ticks can be automatically detected using computer vision and when combined with manual inspection, will help improve the overall efficiency of tick detection. We propose to develop, validate, and evaluate an automated method for tick detection to allow producers to quickly and safely check their livestock for ticks. Our proposed research is innovative and transformative because we are addressing a neglected point for tick dispersal in different emergent regions.Project results will enable producers to make informed decisions about treating cattle for ticks and will provide the necessary foundation for future sustainable veterinary pest management programs for livestock. Knowing cattle primarily move through livestock markets and that these markets do not have an IPM plan for managing ticks, our project is providing much-needed information and guidelines for preventing movement and establishment of exotic invasive ticks at livestock markets, using ALTs as the justification. After the automated tick detection method is developed, validated, and evaluated, it can be used in different areas, evaluated for adoption and practicality, and improved as needed for specific pest-pathogen complexes on different hosts in different regions. The proposed research is in contrast to traditional tick management approaches that involve on-animal studies, as we are working with stakeholders at the site of dispersal. Given the project's collaborative and integrated partnership of academic faculty and stakeholders, we are set to apply our research results in different regions with additional tick species and for future studies on cattle and other livestock.
Animal Health Component
25%
Research Effort Categories
Basic
50%
Applied
25%
Developmental
25%
Goals / Objectives
We will use a combination of computational and field-based approaches to achieve the following research objectives. We will first develop hardware and software of an automated camera system to monitor ticks on a beef herd known to have tick problems at the University of Tennessee, and then validate the developed system at livestock markets in Tennessee and North Carolina (sites where ticks were previously collected). Additional collaborators at the University of Georgia and USDA-ARS in Kerrville agreed to monitor their cattle for ticks and assess the automated method for tick detection (see letters of support). Our long-term goal from this project is to develop a data-driven IPM plan that integrates active and passive surveillance and prevents establishment of exotic and invasive ticks (e.g., H. longicornis) at a new site. There is a severely neglected and critical need to develop IPM strategies for ticks and their pathogens at livestock markets; thus, the overall objective of this proposal is to develop an automated method for detection of ticks on cattle at livestock markets. We will test the hypothesis that, like other machine learning and automated methods that we have developed (Gan et al. 2018, Gan, Lee, Alchanatis, et al. 2020, Gan, Lee, Peres, et al. 2020, Nasiri et al. 2020, Psota et al. 2021), ticks can be automatically detected using computer vision that, when combined with manual inspection, will help improve the overall efficiency of tick detection. We are a prepared team with the expertise, current pest threats, access to tick-infested cattle, and collaborations to test our hypotheses with the following three objectives.Objective #1. Develop an automated computer vision system that detects and counts ticks on cattle in order to develop a producer friendly strategy for monitoring ticks. Our working hypothesis, based on our preliminary data, is that a neural network will aid in the rapid detection of ticks on cattle. As we have developed a camera system for monitoring cattle's body conditions, we will upgrade this system by adding cameras, optimizing the placement of the cameras, and developing new machine learning algorithms for tick detection. The system will be deployed and developed for cattle at a tick-infested farm owned by the University of Tennessee.Objective #2. Validate the resulting computer vision system by comparing tick detection via traditional methods (scratching) to our developed system in order to use this method at detecting ticks quickly and accurately. Our working hypothesis, based on our preliminary data, is that tick detection with the network will be faster, safer, and equally (if not better) at detecting ticks on cattle by having a higher specificity and sensitivity than the golden standard (tick scratching). At the above tick-infested farm, the center hosts a bull development and evaluation program followed by a livestock sale, we will validate the computer vision system against the gold standard as cattle pass through chutes at these events.Objective #3. Evaluate the developed computer vision system for detecting ticks on cattle at various livestock markets in order to implement a rapid plan for preventing tick and pathogen movement at livestock markets. Our working hypothesis, based on our previous experiences and preliminary data, is that the computer vision system will quickly inform livestock markets if animals are infested with ticks and provide the data for the market to use an acaricide to prevent the spread of those ticks. Evaluating the computer vision program at several livestock markets with various degrees of tick infestation and in collaboration with the Cattle Fever Tick Eradication Program will assist with rapid tick detection and trace backing which provides the first steps for reducing the chances for exotic and invasive ticks (e.g., ALTs and Texas Cattle fever ticks) to spread to new farms.The proposed research is innovative and transformative because we are applying a state-of-the-art AI approach in a novel setting to address a severely neglected point for tick and pathogen dispersal in different emergent regions. Our expected outcomes include enhanced ability to detect and manage ticks as well as information about dispersal and establishment of exotic and invasive tick species. We anticipate these outcomes will have a positive impact by providing the US cattle industry and stakeholders who use livestock markets a mechanism for detecting and managing exotic and invasive ticks, thereby preventing widespread negative health and economic impacts caused by tick feeding.
Project Methods
The computer vision network will be developed and validated at the University of Tennessee Middle Tennessee Research and Education Center (MTREC). Information about each sampled animal and its associated lot will be recorded. All collections will have baseline information requested by USDA-APHIS. All data will be stored in a relational database and mapped in ArcMap 10.6.1. We will use a computer vision system mounted at the location of a water supply which is easily powered, requires minimal maintenance efforts, and can capture high-resolution images when animals are recorded. Already, each animal visits the water supply several times a day, so that it is guaranteed that each animal will be assessed every day. Building on what we initiated, we will develop a computer vision system consisting of several high-resolution cameras and deploy the system at a water supply for cattle.Development. At MTREC there is a bull evaluation facility which uses a C-LOCKTM SmartScale for each waterer. The SmartScale forces animals to approach the waterer from one direction and automatically logs the weight and radio frequency identification (RFID) tag of each animal. A solar panel kit, also from C-LOCKTM is used to provide power to the SmartScale. The computer vision system will be developed and installed on or close to the SmartScale. Because the animals can only approach the water from one side, cameras can be placed permanently to capture different body areas on cattle. In order to capture the head, neck, and tail areas, the initial placement of the cameras is high; however, various camera placement will be tested for optimal image quality. The camera system will include a Raspberry Pi 4 (8 GB RAM) and high-resolution surveillance cameras. The exact camera model will be determined during the development phase. A custom designed enclosure will be 3D printed to house and protect the camera system. Power will be supplied by the same solar panel kit as the SmartScale. To optimize power usage, an ultrasonic sensor will be used to trigger camera recording. A Python algorithm will be developed to use the ultrasonic sensor readings to trigger the cameras when an animal is at the waterer. The position and the distance threshold that triggers the cameras will be determined by experimentation. The cameras will be triggered to take multiple images, e.g., five images, each time an animal is at the waterer. Data will be stored in a 1TB flash drive. We expect each animal to visit the waterer at least twice a day. Assuming 30 animals on each pasture, the total daily recording for each pasture should be less than 150 images. Based on our preliminary results and calculations, the flash drive will last for more than 1.5 years at which point it will be replaced.The goal of the computer vision system is to perform automated detection of ticks. This is an object detection task that has been studied extensively. There are several object detection deep learning architectures; however, to achieve high detection accuracy for a specific task, a comprehensive training dataset is important. We propose to build a comprehensive dataset by incorporating a diverse set of annotated images (e.g., geo-graphic, breed, lighting, tick species, environmental conditions, etc.). The project team, including two post-doctoral scholars and an undergraduate student, will be trained by an expert to label the images. An initial deep-learning model will be trained for tick detection using the dataset collected previously. While more training data are collected and labeled, they will be added to the dataset to improve the model accuracy. The accuracy of the model will be evaluated based on mean average precision (mAP), an evaluation standard in the field of deep learning. The outcome will be a lightweight deep learning model that can accurately detect various tick species in the southeast region.Resulting images will be annotated and counted by three individuals, along with the computer vision system, in order to determine interrater and intrarater reliability. Both the individuals and the neural network will repeat the process three times with the same set of pictures. The total number of ticks counted by an individual rater and the system for one picture will be averaged amongst the repetitions. The coefficient variation (CV) will be calculated by dividing the standard deviation by the mean for every rater's picture. The CV will be calculated to determine a rater's dispersion around the mean for each picture counted. A lower CV means the rater had a more precise estimate every repetition. Mixed model analysis of variance will be used to assess interrater reliability, with the random effect of image and rater*image, while blocking on the image. To assess intrarater reliability, a no variable model in SAS 9.4, (Cary, NC) will be used with random effect of intercept. An interclass correlation (ICC) will then be calculated for each rater by dividing the estimate by the residual. ICC will be used to determine the correlation within a rater.Validation. To validate the resulting automated method, to maximize the efficiency of collections, and to protect the safety of both the investigator and the animal we will work with MTREC animals as they routinely enter chutes. The greater of 25% of the total lot size or ten animals will be sampled during each event, and to avoid reducing the efficiency of the husbandry practices of the producer and/or market. Animals that pose a threat to the safety of itself or the investigator will not be sampled. Procedures will be approved through the University of Tennessee's IACUC. The computer vision system will be installed at the end of chutes. As cattle are briefly stopped there, the computer vision system will first take the image(s) of the animals. Then animals will be scratched for ticks at common attachment sites along the ears, head, neck, tail, legs, belly, and underside of the tail for 5 minutes to minimize host stress. A second person will record animal information, where the investigator safely searched for ticks, where ticks originated from on the animal, the time it took to check the animals for ticks, and if any other incidences occurred. Ticks will be stored in ethanol until counted and identified to species and life stage.We will compare the number of ticks recorded by the automated system to the gold standard (number of ticks collected) for each animal. Additionally, analyses will include presence/absence variables about location of ticks, if animal could be checked, etc. We will also determine the amount of time it took for a person to scratch a tick compared to the vision system to capture and process the images. Separate generalized linear-mixed models will be conducted for each response variable. In the model, assessment method will be the fixed effects and other variables associated with the animal (e.g., breed) and collection (e.g., time, season) will be random effects.Evaluation. We will work with collaborators at livestock markets in North Carolina and Georgia and at the Cattle Fever Tick Eradication Program in Texas to evaluate the efficacy of the automated vision system and improve its efficiency to ensure it can be adopted and used at other markets. We will first develop a video and informational sheet for use of the vision system. Once instructions are developed, we will set up the vision systems at livestock markets for the spring, summer, and fall animal sales. Livestock markets will be visited weekly in year two by our team and we will modify the automated vision system as suggested by the market crews. For quality control as well as for tests of sensitivity and specificity, we will also compare the number of ticks collected from cattle to the number of ticks detected from cattle.