Multi-scenario pear tree inflorescence detection based on improved YOLOv7 object detection algorithm
Zhen Zhang, Xiaohui Lei, Kai Huang, Yuanhao Sun, Jin Zeng, Tao Xyu, Quanchun Yuan, Yannan Qi, Andreas Herbst, Xiaolan Lyu · 2024
Read Multi-scenario pear tree inflorescence detection based on improved YOLOv7 object detection algorithm by Zhen Zhang, Xiaohui Lei, Kai Huang, Yuanhao Sun, Jin Zeng, Tao Xyu, Quanchun Yuan, Yannan Qi, Andreas Herbst, Xiaolan Lyu (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-10840500.txt.
Description
Zhen Zhang and co-authors present an improved vision model for finding pear blossom clusters in orchards, in a paper whose hosted text is dated 2023. Thinning blossoms well sets up good fruit, and smart machines need to spot clusters even when dense or far away. Their model, YOLOv7-E, upgrades a standard detector with an attention step that keeps fine detail and place data, plus softer handling of overlapping finds.
Trained on real field photos across times, distances, and weather, the model reached about 91 percent precision and 90 percent recall. It reads one image in about eighty milliseconds. Against the base model, accuracy rose near five points for a small size cost, which suits thinning machines.
Catalog record
| CREATOR | Zhen Zhang, Xiaohui Lei, Kai Huang, Yuanhao Sun, Jin Zeng, Tao Xyu, Quanchun Yuan, Yannan Qi, Andreas Herbst, Xiaolan Lyu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 50 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |