Deep Learning in Image-Based Plant Phenotyping
importance 6/10
ai assisted
Smart Agriculture and AI crop phenotyping stage: research
Problem
Manually measuring crop traits is slow and labor-intensive, and extracting meaningful information from large plant image datasets is difficult.
Why it matters
Fast, accurate crop phenotyping is a bottleneck in crop improvement and food security.
What the AI did
The review covers deep learning methods that analyze plant image data and make predictions on unseen images, reducing the need for human input.
Results
not stated in source
Impact
Evidence supports only that deep learning is being applied to reduce human labor in plant phenotyping.
Record
| Venue | Annual Review of Plant Biology |
|---|---|
| Published | 2024 |
| Work type | review |
| Authors | Katherine M. Murphy, Ella Ludwig, Jorge Gutierrez, Malia A. Gehan |
| Citations | 137 (OpenAlex, at import time) |
| References | 78 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1146/annurev-arplant-070523-042828 |
| Open access | hybrid |
| Keywords | deep learning, plant phenotyping, high-throughput imaging, crop improvement, phenomics |
Importance rating
6
significant — thousands to millions affected
Abstract explicitly calls phenotyping a major bottleneck in the crop improvement pipeline; faster phenotyping affects agriculture broadly, though no numbers or food-security claims are given.