Weed detection in soybean fields using improved YOLOv7 and evaluating herbicide reduction efficacy
Jinyang Li, Wei Zhang, Hong Zhou, Chuntao Yu, Qingda Li · 2024
Read Weed detection in soybean fields using improved YOLOv7 and evaluating herbicide reduction efficacy by Jinyang Li, Wei Zhang, Hong Zhou, Chuntao Yu, Qingda Li (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-10808379.txt.
Description
Jinyang Li and co-authors published a study, with the head dated 2023, on spotting weeds in soybean fields with drones to cut herbicide use. Cutting spray use is a goal for greener farming, but checks still rely on slow hand work. The team ran field trials with four levels of reduced spraying. A drone gathered field images for a detection model.
Their improved model beat the base model and an enhanced variant on all measures. One spray method with a ten percent cut controlled weeds well without hurting soybean growth, judged by leaf area and dry weight. The authors present drone detection plus tuned spraying as a path to less herbicide with sound weed control.
Catalog record
| CREATOR | Jinyang Li, Wei Zhang, Hong Zhou, Chuntao Yu, Qingda Li |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 48 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |