Performing Department
(N/A)
Non Technical Summary
Precision agriculture technology is potentially to improve management efficiency in animal production by monitoring behavioral and biological characteristics of individual animals. Current methods in estrus check are labor intensive and can be improved to increase the reproductivity and management efficiency. This project aims to develop and validate an automated sow estrus detection and decision-making system to increase management efficiency, reduce labor demand and improve the sustainability of swine production. A robotic imaging system will be developed to continuously monitor individual sows and gilts, and collected imagery data will be analyzed using advanced machine learning modules to quantify vulva size, activities, and body conditions. Data-driven decision-making systems will be developed for detecting estrus and identifying optimal artificial insemination timing. Improved management protocols will be assessed through on-farm evaluations and feasible study using economic analysis. The novelty of this project is that, for the first time, both behavioral and biological characteristics of individual sows towards estrus are quantified using a robotic imaging system. The success of the project may discover the insights between reproductive performance and biological and behavioral characteristics. We will develop various extension activities to disseminate the findings of the is project and promote precision agriculture in animal production. Short courses, certificate and online training programs will be developed to provide education of digital technologies in animal production. We expect that artificial intelligence-powered decision-making systems can improve the reproductivity and sustainability of swine production, which aligns with the long-term goal of USDA NIFA and this program.
Animal Health Component
100%
Research Effort Categories
Basic
20%
Applied
30%
Developmental
50%
Goals / Objectives
Research goal: This project is to improve the reproduction performance of breeding swine herd (sows), reduce labor demands and improve the sustainability of swine industry by adopting emerging technologies. We will develop a robotic sow monitoring and data-driven decision-making system to automatically detect estrus status and identify the optimal timing for artificial insemination for individual sows. Improved management decisions will be evaluated through on-farm evaluation and economic analysis. Extension programs will be developed to promote digital agriculture and its adoption through this project.Objectives include:Obj. 1: Develop and validate a robotic sow monitoring system in sow farms. We will develop a robotic monitoring system and validate it in research and commercial sow farms.Obj. 2: Develop a data-driven decision-making system for detecting estrus and optimizing artificial insemination. Big data from the robotic imaging system, sow management and productive performance will be used to develop a data-driven decision-making system to detect estrus, identify optimal artificial insemination and improve management efficiency.Obj. 3: Conduct on-farm evaluations to assess the feasibility of technology adoption. We will develop and evaluate an improved data-driven estrus detection and artificial insemination protocol through on-farm evaluation and economic analysis.Obj. 4: Increase the knowledge and adoption of precision livestock farming (PLF) technology though extension activities. Research findings from this project and public knowledge on PLF will be disseminated through training, workshops, and other extension activities
Project Methods
The project aims to develop and validate a robotic sow monitoring system that can detect the onset of estrus, identify the optimal timing for artificial insemination and improve reproductive performance and sustainability. The interdisciplinary research team has been closely working with stakeholders to identify the needs and requirements of automated systems for sow estrus detection. This integrated project will develop and validate a robotic imaging system for sow monitoring and estrus detection. On-farm evaluation and economic analysis will be conducted to assess the feasibility of the technology adoption. We will closely work with the stakeholders through extensive extension activities to disseminate the research findings and knowledge of precision livestock farming.