Recipient Organization
MICHIGAN STATE UNIV
(N/A)
EAST LANSING,MI 48824
Performing Department
ELEC COMP ENGR
Non Technical Summary
There is currently a pressing need for the implementation of inexpensive high-throughput phenotyping of pigs that can be widely applied to swine research, production and nucleus farms indistinctly.We are proposing a hardware and software phenotyping tool that will benefit and bring synergy between three areas: production animal management, animal research and genetic selection breeding. With low investment and operational cost, it will estimate individual animal traits useful for livestock management decisions. For animal researchers it will obtain quantitative traits that can be linked to animal health and productions. And breeders will be able to use the tool both in nucleus farms as well as in production farms, where much larger datasets can be acquired reflecting actual swine production conditions.Our tool consists of two components. (1) Smart hardware includes a networked, multi-modal camera system can be readily installed in farms and operates with minimal oversight. It acquires realtime color and depth imaging of swine in pens, at feeding and drinking stations and along hallways, and is integrated with a tag-based identification system. It automatically detects swine, tracks them and provides low-level phenotypes including shape scans, posture and joint motions. These low-level phenotypes are key enablers for large-scale, individualized swine data collection. Using these, many tools can be built that in infer swine health and welfare characteristics. (2) Construction of two modules that build high-level phenotypes from low-level phenotypes. (a) A body condition estimator module that can replicate the performance of current body condition measures, and can also estimate a score that more directly reflects swine welfare and productivity. (b) A swine interaction module that can automatically detect and annotate pairwise interactions such as fighting and intimidation at the feeder. Ability to automatically identify swine interactions enables adjustment of management strategies as well as research into genetic factors influencing swine interactions.Our broader goal is that our tool will be easily built-on and extended in both production and research farms. There is potential to create additional modules to estimate many more high-level phenotypes from the low-level phenotypes. The two modules we develop can be used as templates for modules that estimate other phenotypes.
Animal Health Component
50%
Research Effort Categories
Basic
0%
Applied
50%
Developmental
50%
Goals / Objectives
The project goal is to develop and demonstrate an automated phenotyping tool to advance precision livestock farming. The following are our objectives in reaching the goal:Assemble hardware and data collection software for 10 PhenoKits and deploy in participating farmsDevelop low-level phenotyping capabilities and deploy on PhenoKits. These include detection, tracking, 3D posture estimation, instance segmentation and long-term sow identity within pens.Integrate low-level phenotypes to estimate sow Body Condition from walking sowsIntegrate low-level phenotypes to quantitatively measure swine activity and interactions at feeders
Project Methods
Hypothesis 1: Low-level phenotypes can be remotely and automatically estimated for swine in pens and hallways. These phenotypes include: swine detection, segmentation, 3D posture estimation, tracking and identity maintainance.Approach: A combined hardware & software solution called PhenoKits will be constructed, and machine learning techniques applied to estimate these phenotypes.Evaluation: Manual annotation will provide ground truth for each phenotype and an array of quantitative measures will be used to assess the phenotypes.Milestones:Year 1, Q4: Initial detection, segmentation and tracking of swineYear 2 Q2: Swine ID maintanance complete and evaluatedYear 2 Q 4: All low-level phenotypes complete and evaluatedHypothesis 2: High-level phenotypes can be remotely and automatically estimated by integrating low-level phenotypes for swine in pens and hallways. The two phenotypes we will address are body condition based on 3D shape and feeder interaction behaviors. Evaluation: Manual annotation will provide ground truth for each phenotype and an array of quantitative measures will be used to assess the phenotypes.Milestones:Year 3 Q 4: Body condition complete and evaluatedYear 3 Q 4: Feeder behaviors characterized and evaluated