Recipient Organization
FORT VALLEY STATE UNIVERSITY
1005 STATE UNIVERSITY DRIVE
FORT VALLEY,GA 31030
Performing Department
(N/A)
Non Technical Summary
Big data analytics (BDA) and artificial intelligence (AI) have potential to improve sustainability of livestock production. Small ruminant (sheep and goat) production is a growing industry in the U.S., but is severely limited by infection with internal parasites, increased prevalence of anthelmintic-resistant gastrointestinal nematodes, and lack of producer access to expert information on forage and parasite management. Research and outreach initiatives are underway at Fort Valley State University (FVSU) and collaborating institutions using AI and machine learning (ML) to develop cellphone APPs for automated animal health monitoring and site-specific anti-parasitic forage management decision support, but professionals trained in these disciplines (AI, BDA, ML) related to plant and animal sciences, veterinary parasitology, and agricultural extension are currently lacking. In this project, a diverse team of national and international collaborators propose to develop an on-line BDA and AI curriculum to increase proficiency of students at FVSU and other institutions in the real-world application of these technologies, with a goal of developing a work force prepared for careers in AI-related fields. Collaborators on the project will develop on-line teaching modules on BDA, AI, and ML, and their application to precision bioactive forage and animal health management, climate change, and agricultural extension, and will provide students with experiential research and outreach learning opportunities at their institutions and on-farm. Farmers and other clientele groups will also be trained in the use of animal health monitoring and site-specific bioactive forage (sericea lespedeza) management cellphone APPs in producer workshops and field days.
Animal Health Component
40%
Research Effort Categories
Basic
20%
Applied
40%
Developmental
40%
Goals / Objectives
The primary goal of the proposed project is development and implementation of an online educational curriculum at FVSU consisting of a series of course modules on big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL) related to animal science, including animal health monitoring and production of anti-parasitic bioactive forages.Increase student knowledge by integrating BDA, AI, ML and DL educational modules into existing course curricula in animal science and other disciplines at FVSU and other institutions.Complete research in automated decision support modeling for sustainable animal health monitoring and bioactive forage production to provide students with experiential learning opportunities in AI and ML field and laboratory research through training in use of tools used in these disciplines.Provide students with experiential learning opportunities in AI and ML extension through interaction with small ruminant producers.
Project Methods
The program will be designed as a series of modules, with a certificate of completion after passing an exam for each module. The modules can either be taken as a series over an entire semester, or individually. This will increase the flexibility of the curriculum, as individual modules can be adapted for inclusion in multiple courses in different disciplines at FVSU or other institutions. For example, at FVSU this could include undergraduate courses in animal science, plant science, biology, agricultural engineering, or computer science, or Master's level graduate courses in animal science, biotechnology, or public health.The first module will provide students a comprehensive overview of the subject matter, specifically tailored to their individual fields of interest. They will be introduced to fundamental analytics tools, including software applications, data dimensionality, coding basics, model functionality, and results interpretation. Module 2 will focus on clustering techniques, Module 3 unsupervised learning, and Module 4 on supervised learning techniques. In Module 5, students will be introduced to DL and image processing, while Module 6 will focus on an introduction to the fundamental structure of the R programming language, accompanied by the presentation of user-friendly, open-access software for BDA and a guest lecture on Natural Language Processing and ingredient informatics. In the final module (7), students will focus on completing their projects and final presentations.From automated irrigation systems to precision farming, technology will continue to enhance efficiency, productivity, and sustainability within the agricultural sector. A promising breakthrough will be at the forefront of this technological revolution - development of a Rapid Visual Identification System (RVIS) mobile application with advanced machine learning capabilities. This groundbreaking application will employ Convolutional Neural Networks (CNN) for bioactive plant identification, GI tract parasitic infection detection, and hematocrit value evaluation - three pivotal aspects that will bear considerable influence on the health and productivity of livestock, particularly small ruminants. Data generated from this work will be made available to students and farmers to work with in model development training and use of cellphone APPs, respectively, for animal health and forage management decision support.