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Connecting the Dots: Linking a Novel Egg Counting Device to Machine Learning Based Software to Facilitate Improved Food Safety and Production Efficiency in Poultry Production
Phone: (530) 752-3215
Email: mepitesky@ucdavis.edu
Phone: (530) 219-1407
Email: mepitesky@ucdavis.edu
Large commercial breeding layer broiler turkey and production flocks collect an overwhelmingamount of data much of which is either inaccurate and/or not leveraged for decision making.Current poultry data management solutions are typically ineffective poorly integrated across thesupply chain (i.e. breeders to processing plants) and ultimately force farmers to make productionfood safety and economic decisions based on incomplete and inaccurate data. Hence the ability toaccurately collect and analyze data in order to be predictive is seen as the "holy grail" of poultryproduction efficiency poultry health and food safety. From an innovation perspective this willrequire improved and greater utilization of remote sensing in addition to optimization of variouspredictive models. As some companies start to explore Machine Learning (ML) based approaches(i.e. statistical techniques that are capable of "learning by finding" non-obvious associations andpatterns in the data in order to create more reliable custom accurate explanatory and predictivestatistical models) for predictive analyses there is a general lack of knowledge about which MLapproaches 'work' and how ML based results can be integrated within decision science based toolswhich highlight company expertise as opposed to data. In other words the ability to createintegrative software that stresses both novel predictive statistics and institutional knowledge wouldallow data and expert knowledge to be dovetailed and considered equally by decision makers.One hindrance to this approach is the inability to acquire accurate data. "Garbage in garbage out"is a real problem in the analysis of ag-based data including poultry data from the farm to theprocessing plant. One paramount example is the inability to count eggs at the poultry house level(as opposed to the processing plant). Without these data at the house level counting is only doneaccurately at the processing plant which makes flock management at the house and row levelimpossible. Here we propose to develop two innovations and dovetail the technologies.1. With respect to egg production we propose to further develop our novel modular egg counterthat attaches to commercial egg conveyor belts of differing width and detects egg counts usingdifferent approaches including ultrasonic analysis infrared analysis and image analysis.2. With respect to predictive data analysis and decision sciences for layer broiler and turkeyproduction using ML based techniques we propose to test 3 ML based predictive approachesfrom historic raw data provided by various commercial poultry partners we are affiliated withincluding our first commercial client Hy-Line North America (an international layer geneticcompany). Our goal is to identify the best predictive ML based model(s) to better predictproduction economics and food safety outcomes. The analyses from the ML will be furtherleveraged by the incorporation of decision science based tools like Analytical HierarchyProcesses (AHP) that can help poultry companies quantitatively query internal experts inorder to integrate expert opinion within a company to statistical observation and cost-benefitanalysis provided by ML analysis based tools.The greatest improvements in poultry production efficiency food safety and economics will bemade via the improved accuracy of data and integration and utilization of data. As experts inpoultry data analytics at the academic and industry level Agrinerds founders recognize theproblems and potential innovations described above. Our goal for this phase I Program is todevelop a commercially viable innovative methodology for egg collection (hardware) and linkthat to innovative methods of analysis (software) to better predict outcomes for the commercialpoultry industry. Our commercial client Hy-Line and collaborators Purdue Chicken and JSWest are major producers that giv
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