Deep Learning-Based Weed Detection in Turf: A Review
importance 4/10
partially solved
Smart Agriculture and AI turf management stage: research
Problem
Autonomous precision herbicide spraying in turf requires accurate and reliable detection of weeds.
Why it matters
Accurate weed detection can significantly reduce herbicide input in turf management.
What the AI did
Review summarizing studies where deep convolutional neural networks (classification and object detection) detect turf weeds, finding them suitable but influenced by weed and turf factors.
Results
not stated in source
Impact
Compiles prior findings supporting DCNN suitability for ground-based turf weed detection.
Record
| Venue | Agronomy |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Xiaojun Jin, Teng Liu, Yong Chen, Jialin Yu |
| Citations | 35 (OpenAlex, at import time) |
| References | 64 |
| Model or method | deep convolutional neural networks (DCNNs) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/agronomy12123051 |
| Open access | gold |
| Keywords | weed detection, turf, deep learning, DCNN, precision spraying, review |
Importance rating
4
real but narrower advance
Accurate weed detection enables precision spraying that can significantly reduce herbicide input in turf management; real but narrow environmental benefit, no scale stated.
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