Lab Overview
The advent of advanced instrumentation, detailed environmental data, and precision treatment capabilities in agriculture, provides new and compelling opportunities to apply Data Science to Agricultural Engineering. The Texas A&M Institute of Data Science (TAMIDS) collaborates with Texas A&M’s Department of Electrical & Computer Engineering, the School of Performance Visualization and Fine Arts, AgriLife Research, AgriLife Extension, the Texas Water Resources Institute, and Texas A&M University-Corpus Christi in a cross-disciplinary research program that integrates across foundations and practice by:
- using machine learning techniques to model the influence of plant genetics, the environment, and treatment factors on plant growth;
- developing algorithms that adaptively optimize treatment over the growing season to meet goals for yield and cost
- embodying these algorithms in decision support systems that can deliver practical recommendations for crop treatment based on available knowledge
Members
Collaborators
Researchers
- Pancho Abello
- Mahendra Bhandari
- Craig Bednarz
- Salvatore Calabrese
- Bardia Heidari Haratmeh
- Timothy Goebel
- Fouad Jaber
- Juan Landivar
- Robert Lascano
- Pankaj Pal
- David Parker
- Payton Paxton
- Nithya Rajan
- Avay Risal
Organizations
- Texas Water Resources Institute
- Texas A&M Corpus Christi
- Texas A&M AgriLife Research
- Texas A&M AgriLife Extension
- Texas A&M Departments of Agricultural Economics, Soil & Crop Science, Biological & Agricultural Engineering, and Animal Science
- Goanna Ag
- West Texas A&M University
- Texas A&M Beef Center
Past Events
- 2024 Conference: AI in Agriculture and Natural Resources
- 2022 Workshop: Data Science at the Intersection of AgrIculture with its Affiliated Disciplines
- 2020 Workshop: Artificial Intelligence Applications to Agriculture