Weed Detection Using Deep Learning: A Systematic Literature Review
Problem
Weeds are harmful agricultural pests that raise production costs and damage the global agricultural economy, motivating early detection technology for farmers.
Why it matters
Weed-driven crop losses impose significant costs on the global agricultural economy.
What the AI did
A systematic literature review of deep learning weed-detection research, filtering 52 application papers and 8 survey papers for analysis.
Results
Review found rapid growth in DL weed-detection research since 2015; 52 application papers and 8 surveys analyzed; pooled results yielded 34 unique items (abstract truncated).
Impact
Maps the state of the art in AI weed detection rather than presenting a new solution.
Record
| Venue | Sensors |
|---|---|
| Published | 2023 |
| Work type | review |
| Authors | Nafeesa Yousuf Murad, Tariq Mahmood, Abdur Rahim Mohammad Forkan, Ahsan Morshed, Prem Prakash Jayaraman, Muhammad Shoaib Siddiqui |
| Citations | 106 (OpenAlex, at import time) |
| References | 98 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/s23073670 |
| Open access | gold |
| Keywords | weed detection, deep learning, systematic review, agriculture, image analysis |
Importance rating
significant — thousands to millions affected
Abstract states weeds are harmful agricultural pests raising production costs with significant impact on the global agricultural economy, motivating early detection for farmers; broad industry-level problem, addressed here via literature review.
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