基于多式融合和注意力引导的轻量级网络的高精度害虫控制
Ziye Liu1, Siqi Li1, Yingqiu Yang1
1China Agricultural University, Beijing 100083, China.
Insects
|August 28, 2025
概括
一个新的AI框架使用多种数据类型 (RGB,热,传感器) 来准确识别农业害虫和捕食者. 这种智能农业解决方案改善了害虫管理,
科学领域:
- 智能农业和可持续农业实践
- 人工智能在害虫防治中的应用.
背景情况:
- 传统的虫害识别模型难以应对复杂的现场条件和单一来源的数据.
- 准确识别农业害虫和捕食者对于粮食安全和可持续发展至关重要.
研究的目的:
- 开发一个强大的农业昆虫害虫和捕食者的多式识别框架.
- 克服智能农业中单模式识别系统的局限性.
主要方法:
- 整合RGB成像,热红外成像和环境传感器数据.
- 使用跨模式注意力机制和以环境为导向的模式权重.
- 使用脱识别头来提高稳定性.
主要成果:
- 拟议的模型达到91.5%的精度,89.2%的回忆率,90.3%的F1分数和88.0%的mAP@50,超过现有方法的6%.
- 在低光和传感器噪音等具有挑战性的条件下显著改善.
- 在边缘设备上实现了25.7 FPS的推断速度,平衡精度和效率.
结论:
- 多式人工智能框架为虫害监测和生物控制提供了高效,智能和可扩展的解决方案.
- 这项技术有助于在农业生态系统中精确管理害虫.
- 该模型在边缘设备上的性能验证了其在现实世界中的适用性.
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