From the report “Plant Breeding AI Gets Schooled in New Student Competition“, of Jennifer Howard, published by Crop and Soil Sciences News, of NC State University.
Phenotyping is time-consuming and labor-intensive for researchers. NC State and the USDA-ARS jointly developed a low-cost, open-source solution for non-destructive high-throughput phenotyping in greenhouses, affectionately called the BenchBot.
BenchBot is a plant phenotyping platform consisting of two main components: an RGB+depth camera and a processing unit to control the platform and camera movement.
Researchers have successfully used the BenchBot’s greenhouse-acquired images to train machine learning algorithms under controlled conditions and are now refining the algorithms to detect and identify plants, detect leaves and determine leaf area, and estimate total plant biomass for field use.
The team recently won third place regionally in the OpenCV Spatial AI Competition with the BenchBot and decided to host a similar competition for students.
The university and USDA-Agricultural Research Service created the Ag Tech Hackathon to accelerate agricultural research using computer vision, machine learning, and robotics.
Hackathon participants competed in three categories, all using the BenchBot to sense and interpret greenhouse plant growth:
- Deep learning