This Project Grant award from the National Science Foundation (CFDA 47.070 - Computer and Information Science and Engineering) will fund the development of an autonomous robotic system for precision and high-throughput tomato phenotyping in large-scale greenhouses. The $1,179,584 award, effective July 15, 2025 through June 30, 2028, will support four key research goals: 1) Developing deep learning models for plant localization, greenhouse environment perception, and target fruit selection; 2) Creating novel robot motion planning algorithms to acquire high-quality images while preventing plant damage; 3) Developing a multi-task learning model to compute diverse tomato fruit traits and evaluate phenotyping results; and 4) Designing an optimized, high-efficiency, high-reliability, and low-cost wireless dynamic battery charger for the autonomous system. This project aims to enable greater farm productivity, improved worker safety, and potential translation to other application domains. Subawards are planned for this award.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $1.2m | 7/10/25 |