MSGID: 1:317/3 64b2210e   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Precision technology, machine learning lead to early diagnosis of calf   
   pneumonia    
    Wearable sensors, automatic feeders yield clues about onset of bovine   
   respiratory disease    
      
     Date:   
         July 14, 2023   
     Source:   
         Penn State   
     Summary:   
         Monitoring dairy calves with precision technologies based on the   
         'internet of things,' or IoT, leads to the earlier diagnosis of   
         calf- killing bovine respiratory disease, according to a new   
         study. The novel approach -- a result of crosscutting -- will   
         offer dairy producers an opportunity to improve the economies of   
         their farms, according to researchers.   
      
      
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   Monitoring dairy calves with precision technologies based on the "internet   
   of things," or IoT, leads to the earlier diagnosis of calf-killing bovine   
   respiratory disease, according to a new study. The novel approach -- a   
   result of crosscutting collaboration by a team of researchers from Penn   
   State, University of Kentucky and University of Vermont -- will offer   
   dairy producers an opportunity to improve the economies of their farms,   
   according to researchers.   
      
   This is not your grandfather's dairy farming strategy, notes lead   
   researcher Melissa Cantor, assistant professor of precision dairy science   
   in Penn State's College of Agricultural Sciences. Cantor noted that   
   new technology is becoming increasingly affordable, offering farmers   
   opportunities to detect animal health problems soon enough to intervene,   
   saving the calves and the investment they represent.   
      
   IoT refers to embedded devices equipped with sensors, processing and   
   communication abilities, software, and other technologies to connect   
   and exchange data with other devices over the Internet. In this study,   
   Cantor explained, IoT technologies such as wearable sensors and automatic   
   feeders were used to closely watch and analyze the condition of calves.   
      
   Such IoT devices generate a huge amount of data by closely monitoring   
   the cows' behavior. To make such data easier to interpret, and provide   
   clues to calf health problems, the researchers adopted machine learning   
   -- a branch of artificial intelligence that learns the hidden patterns   
   in the data to discriminate between sick and healthy calves, given the   
   input from the IoT devices.   
      
   "We put leg bands on the calves, which record activity behavior data   
   in dairy cattle, such as the number of steps and lying time," Cantor   
   said. "And we used automatic feeders, which dispense milk and grain and   
   record feeding behaviors, such as the number of visits and liters of   
   consumed milk. Information from those sources signaled when a calf's   
   condition was on the verge of deteriorating."  Bovine respiratory   
   disease is an infection of the respiratory tract that is the leading   
   reason for antimicrobial use in dairy calves and represents 22% of calf   
   mortalities. The costs and effects of the ailment can severely damage   
   a farm's economy, since raising dairy calves is one of the largest   
   economic investments.   
      
   "Diagnosing bovine respiratory disease requires intensive and specialized   
   labor that is hard to find," Cantor said. "So, precision technologies   
   based on IoT devices such as automatic feeders, scales and accelerometers   
   can help detect behavioral changes before outward clinical signs of   
   the disease are manifested."  In the study, data was collected from 159   
   dairy calves using precision livestock technologies and by researchers   
   who performed daily physical health exams on the calves at the University   
   of Kentucky. Researchers recorded both automatic data-collection results   
   and manual data-collection results and compared the two.   
      
   In findings recently published in IEEE Access, a peer-reviewed   
   open-access scientific journal published by the Institute of Electrical   
   and Electronics Engineers, the researchers reported that the proposed   
   approach is able to identify calves that developed bovine respiratory   
   disease sooner. Numerically, the system achieved an accuracy of 88%   
   for labeling sick and healthy calves.   
      
   Seventy percent of sick calves were predicted four days prior to   
   diagnosis, and 80% of calves that developed a chronic case of the disease   
   were detected within the first five days of sickness.   
      
   "We were really surprised to find out that the relationship with the   
   behavioral changes in those animals was very different than animals that   
   got better with one treatment," she said. "And nobody had ever looked at   
   that before. We came up with the concept that if these animals actually   
   behave differently, then there's probably a chance that IoT technologies   
   empowered with machine learning inference techniques could actually   
   identify them sooner, before anybody can with the naked eye. That offers   
   producers options."  Contributing to the research were: Enrico Casella,   
   Department of Animal and Dairy Science, University of Wisconsin-Madison;   
   Melissa Cantor, Department of Animal Science, Penn State University;   
   Megan Woodrum Setser, Department of Animal and Food Sciences, University   
   of Kentucky; Simone Silvestri, Department of Computer Science, University   
   of Kentucky; and Joao Costa, Department of Animal and Veterinary Sciences,   
   University of Vermont.   
      
   This work was supported by the U.S. Department of Agriculture and the   
   National Science Foundation.   
      
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   Story Source: Materials provided by Penn_State. Original written by Jeff   
   Mulhollem. Note: Content may be edited for style and length.   
      
      
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   Journal Reference:   
      1. Enrico Casella, Melissa C. Cantor, Megan M Woodrum Setser, Simone   
         Silvestri, Joao H.C. Costa. A Machine Learning and Optimization   
         Framework for the Early Diagnosis of Bovine Respiratory   
         Disease. IEEE Access, 2023; 1 DOI: 10.1109/ACCESS.2023.3291348   
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   Link to news story:   
   https://www.sciencedaily.com/releases/2023/07/230714131136.htm   
      
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