Multi-scale Multi-resolution Agriculture Data Analytics for Crop/Vegetation Health Prediction and Optimization
(supported by USDA/NIFA)
Award Number: 20 22-38821-37338
Duration: May 2022 – Apr 2025
PI: Lijun Qian; Co-PI: Xishuang Dong, Ram Ray
Prairie View A&M University
Prairie View, TX 77446
Project Summary:
Recent advances in machine learning and big data analytics have created exciting new opportunities for applications in precision agriculture. In this project, a framework of agriculture data analytics for crop/vegetation health prediction and optimization is proposed to create novel paradigm-shifting approaches for quantitatively evaluating crop/vegetation health using multi-scale multi-resolution data. Three research and extension objectives are proposed to achieve this goal: 1) collect multi-scale multi-resolution data of crop/vegetation health at PVAMU research farm, by ground sensors and drones, to complement satellite data (MODIS NDVI Version 6 data); 2) apply and develop cutting edge machine learning algorithms for crop/vegetation health assessment; 3) develop a visualization tool for the proposed crop/vegetation health prediction and optimization on PVAMU research farm to enhance research and outreach activities, and educate and train limited resource farmers and other stakeholders on outcomes of the project. If successful, this project will benefits farmers and growers to apply the best agricultural management practice and improve the crop/vegetation yield. Furthermore, this project is multidisciplinary in nature and it will foster collaborations between experts in agriculture and data scientists and engineers. The proposed project will help different stakeholders such as farmers to collaborate with data scientists and engineers, to apply big data analytics to extract valuable knowledge from big agriculture datasets and transform the knowledge into actionable strategies. This will make America’s farmers more competitive in the global agricultural market and contribute to a strong and sustainable economy.
Results:
I. Publications
This material is based upon work supported by the USDA/NIFA under Award Number: 2022-38821-37338.
Journal papers:
- Olamofe, R. Ray, X. Dong, and L. Qian, “Normalized Difference Vegetation Index Prediction using Reservoir Computing and Pretrained Language Models,” submitted to Artificial Intelligence in Agriculture, 2024.
Conference papers:
- J. Olamofe, R. Ray, X. Dong, and L. Qian, “Normalized Difference Vegetation Index Prediction using Reservoir Computing and Pretrained Language Models,” The 2024 AI in Agriculture and Natural Resources Conference, April 15-17, 2024, College Station, TX.
- L. Nwosu, X. Li, S. Kim, L. Qian, X. Dong (2023). “Proformer-based Ensemble Learning for Gene Expression Prediction,” ICIBM.
II. Curriculum Development
New courses created:
- ELEG 4318: Edge Computing
- ELEG 4306: Machine Learning for Engineering Applications
Existing courses upgraded:
- ELEG 4247/4248: Senior Design and Professionalism I/II
III. Students training
- Python programming
IV. Seminar series
- April 5, 2023:
“The Role of Algae Farming in Industrial and Agricultural Decarbonization”, Ryan Davis, Ph.D., Principal Member of the Technical Staff, Bioscience, Sandia National Laboratories.
- April 26, 2023:
“Topological Metabolic Analysis for the Reconstruction of Major Biosynthetic Pathways and Cycles in Methylococcus capsulatus”, Lealon Martin, Ph.D., Associate Professor, Chemical Engineering Department, Associate Dean of Roy G. Perry College of Engineering, Prairie View A&M University.
- October 4, 2023:
“Branches, Boundaries and Bracts: in Search of the Meristematic Homunculus”, Clint Whipple, Ph.D., Associate Professor, Whipple Lab, Biology, College of Life Sciences, Brigham Young University.
Contact information:
Office: NENR Building Room 332
Phone: (936) 261-9908
Fax: (936) 261-9930
E-mail: Lijun Qian
Last Modified: April 2024