MOSSNet: multiscale and oriented sorghum spike detection and counting in UAV images
Sorghum spikes have a slender morphology and their orientation angles tend to be highly variable in natural environments. MOSSNet 's ability has been proved to handle complex scenes with dense distribution, strong occlusion, and complicated background information. This highlights its robustness and generalizability, making it an effective tool for sorghum spike detection and counting. In the future, we plan to further explore the detection capabilities of MOSSNet at different stages of sorghum growth. This will involve implementing object model improvements tailored to each stage and developing a real-time workflow for accurate sorghum spike detection and counting.
Authors
- Yannan Shi (ORCID: https://orcid.org/0000-0001-8125-8422)
- Jianqing Zhao (ORCID: https://orcid.org/0000-0001-7654-9575)
- Yongchao Guo (ORCID: https://orcid.org/0000-0003-4238-6696)
- Zhiyin Jiao
- Wenjie Wu (ORCID: https://orcid.org/0009-0002-9166-8542)
- Ying Zhou (ORCID: https://orcid.org/0000-0003-0687-7818)
- Shiyi Chen (ORCID: https://orcid.org/0000-0003-0021-4191)
- Jinping Wang (ORCID: https://orcid.org/0000-0002-3888-1742)
- Zhifang Wang (ORCID: https://orcid.org/0000-0001-8232-0451)
- Peng Lv (ORCID: https://orcid.org/0000-0002-5719-7997)
Institutions
- Nanjing Normal University (CN)
- Ministry of Agriculture (EE)
- Hebei Academy of Agriculture and Forestry Sciences (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-08-28
- DOI
- https://doi.org/10.3389/fpls.2025.1526142
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75