Recipient Organization
VIRGINIA POLYTECHNIC INSTITUTE
(N/A)
BLACKSBURG,VA 24061
Performing Department
(N/A)
Non Technical Summary
The aim of this project is to leverage the use of acoustic data to address two significant challenges to the dairy industry: animal welfare and climate change. The long-term objective of this proposal is to develop an acoustic, data-driven tool to leverage animal welfare and accelerate the mitigation of methane emissions in precision livestock farming (PLF). The tool is an automatic and comprehensive pipeline that manages, analyzes, and evaluates production quality using acoustic data in the cow product industry. To support the long-term goal, the specific objectives of this proposal include: (1) to test acoustic data to assess calf stress and welfare post-weaning objectively, (2) to test acoustic data to determine the potential genetic variation and nutritional effects on eructation patterns to leverage future methane emission mitigation strategies, and (3) to improve the efficiency in managing acoustic data for precision livestock farming, which contains millions of data points for a single cow in one day, by developing both a Python library tool and a web-based application. This project specifically addresses two of the six Program Area Priorities of Agriculture and Food Research Initiative (AFRI), which are (B) Animal health and production and animal products and (D) Bioenergy, natural resources, and environment.
Animal Health Component
40%
Research Effort Categories
Basic
30%
Applied
40%
Developmental
30%
Goals / Objectives
The long-term goal of this proposal is to develop an acoustic, data-driven tool to leverage animal welfare and accelerate the mitigation of methane emissions in precision livestock farming (PLF). The tool is an automatic and comprehensive pipeline that manages, analyzes, and evaluates production quality using acoustic data in the cow product industry. To support the long-term goal, the specific objectives of this proposal include the following: (1) To test acoustic data to assess calf stress and welfare post-weaning objectively, (2) To test acoustic data to determine the potential genetic variation and nutritional effects on eructation patterns to leverage future methane emission mitigation strategies, and (3) To improve the efficiency in managing acoustic data for precision livestock farming,which contains millions of data points for a single cow in one day by developing a Python library tool and a web-based application.
Project Methods
The project consists of two components: animal management and data analytics. Co-PI Ferreira will conduct experiments using a rigorous experimental design to avoid biased observations and confounding factors in the trials. PI Chen will help collect data, including acoustic recordings and other indicators of animal welfare and methane emissions. After the trials are completed, PI Chen will continue to build software infrastructure and implement deep learning algorithms to extract meaningful information from the collected data.EffortsEfforts to deliver science-based knowledge to our target audience include extension components ofworkshops, extension, and outreach programs. We will develop innovative teaching methodologiesto effectively communicate our findings.EvaluationThe success of our project will be evaluated using several performance metrics. Specifically, we will measure the precision and recall of our model in categorizing animal emotion status and identifying eructation sound events. Our goal is to achieve a precision and recall of at least 0.8 for both metrics. This will demonstrate the effectiveness of our approach in accurately interpreting animal emotions and monitoring methane emissions.