轻量级视觉转换器网络用于在温室农业环境中早期识别昆虫害虫
Wenjie Hong1, Shaozu Ling1,2, Pinrui Zhu1,2
1China Agricultural University, Beijing 100083, China.
Insects
|January 28, 2026
概括
这项研究介绍了Light-HortiNet,一个轻量级的网络,用于准确有效地检测温室害虫和疾病. 它在识别小型害虫和早期疾病阶段方面表现出色,使其能够在边缘设备上实时部署.
科学领域:
- 园艺科学 园艺科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 温室中植物疾病和害虫的早期检测对于作物产量和质量至关重要.
- 在边缘设备上实时部署检测系统面临着计算效率和准确性的挑战.
- 现有的模型在复杂的环境条件和温室中常见的小规模目标中扎.
研究的目的:
- 开发一个轻量级,高效和准确的智能识别网络,用于自动检测温室害虫和疾病.
- 解决当前模型在处理复杂环境和小目标方面的局限性.
- 为了在资源有限的边缘设备上实时识别害虫和疾病.
主要方法:
- 建议Light-HortiNet,一个轻量级的跨尺度智能识别网络.
- 使用了移动变压器骨干,并配备了跨度轻量级注意力机制.
- 集成了一个小物体增强分支和一个替代的块蒸策略.
主要成果:
- 通过0.872的mAP@50和0.561.56的mAP@50:95实现了高检测性能.
- 证明了优越的分类准确度 (0.894),精度 (0.886),回忆 (0.879) 和F1得分 (0.882).
- 展示了增强的小物体识别 (mAP-small:0.536,回忆-small:0.589) 和实时推断 (>20 FPS 在Jetson Nano上).
结论:
- 灯-HortiNet提供了一个强大的和稳定的解决方案,用于自动化温室害虫和疾病检测.
- 该模型在准确性和效率方面明显优于主流轻量级网络.
- 在边缘设备上有利的部署适应性使其适合实时农业监测.
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