多模式交叉关注网络用于检测草幼苗的过度生长
Zhenzhen Cheng1, Yifan Cheng2, Tingting Fang1
1Department of Horticulture, Xinyang Agriculture and Forestry University, Xinyang, China.
Frontiers in plant science
|January 19, 2026
概括
早期检测草苗的过度生长是至关重要的. 一个新的多式人工智能框架MM-CAPNet集成了植物图像和环境数据,用于准确的早期检测,帮助种植者优化作物管理.
科学领域:
- 园艺和农业科学 园艺和农业科学
- 计算机科学和人工智能 人工智能
- 植物科学 植物科学
背景情况:
- 早期检测草苗的过度生长对于平衡植物和生殖生长至关重要.
- 目前的监测方法受到微妙的视觉症状和不足的异常样本的限制.
研究的目的:
- 开发一个多式融合框架 (MM-CAPNet) 以早期检测草苗的过度生长.
- 整合历史环境数据与当代植物图像,以加强监测.
主要方法:
- 通过系统的诱导实验开发了一个代表性的样本收集.
- 使用双流架构与变压器和MobileNetV2编码器分别用于环境和图像数据.
- 实施了以图像为导向的交叉注意力机制,以适应性地检索相关的环境数据.
主要成果:
- MM-CAPNet实现了87.6%的准确性和0.901 AUC,表现优于基线方法.
- 在早期检测过度生长方面表现出强大的区分能力.
- 废弃性研究通过将表型与环境驱动因素联系起来,证实了框架的可解释性.
结论:
- MM-CAPNet提供了一个概念验证,用于在草幼苗中早期检测过度生长.
- 该框架通过调节施肥,灌和光管理来支持精密种植.
- 通过优化苗圃阶段管理,提高资源效率和作物弹性.
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