Deep learning–based approaches for weed detection in crops
importance 5/10
ai assisted
Smart Agriculture and AI precision agriculture stage: research
Problem
Detecting weeds in crops is difficult for traditional machine vision, which lacks robustness, scalability and recognition accuracy in variable field conditions.
Why it matters
Weed detection accuracy directly affects crop management decisions and farming efficiency.
What the AI did
The paper reviews deep learning approaches (object detection, segmentation, classification) for weed detection, comparing architectures and analyzing strengths, limitations and emerging solutions.
Results
not stated in source
Impact
The review documents deep learning's advantages for weed detection but states no measured impact.
Record
| Venue | Frontiers in Plant Science |
|---|---|
| Published | 2026 |
| Work type | article |
| Authors | Zhao Hua, Yan Wang |
| Citations | 11 (OpenAlex, at import time) |
| References | 81 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3389/fpls.2025.1746406 |
| Open access | gold |
| Keywords | deep learning, weed detection, object detection, image segmentation, image classification |
Importance rating
5
real but narrower advance
Weed detection affects crop management and farming efficiency across agriculture, but this review reports no measured impact or scale.