Andrew P. French
Papers
10
Total Citations
663
H-Index
7
About
Andrew P. French is a leading researcher at the intersection of computer vision, robotics, and plant phenomics. His work is defined by pioneering the use of deep learning and active vision systems to automate the high-throughput analysis of plant structure and function. French’s most impactful contribution is his 2017 paper demonstrating that deep machine learning achieves state-of-the-art performance in image-based plant phenotyping, a work that has garnered over 370 citations and fundamentally shifted the field toward automated, data-driven analysis. He has also developed innovative 3D reconstruction pipelines for plant shoots, using active vision to overcome the challenges of complex leaf structures, and created the Microphenotron, a robotic miniaturized platform for high-throughput chemical genetics screening. Beyond plants, his early work on vision-guided robotics for animal tracking—including a system for tracking a loosely constrained pig—showcases his versatility. With a career spanning from individual animal monitoring to large-scale plant phenotyping, French’s contributions are essential for linking genetic data with observable traits, enabling discoveries in agriculture and fundamental plant biology.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling45 citations · 2019
- 6
- 7A vision guided robot for tracking a live, loosely constrained pig17 citations · 2004
- 8High-Throughput Quantification of Root Growth5 citations · 2011
- 9Visual Tracking: From An Individual To Groups Of Animals3 citations · 2005
- 10