一个CNN-变压器混合框架,用于在农田中多标签捕食者捕食者检测
Yifan Lyu1,2, Feiyu Lu1, Xuaner Wang1
1China Agricultural University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
这项研究引入了一种混合深度学习模型,用于使用图像准确识别捕食者-害虫关系. 该先进的框架通过在复杂的野外环境中改进对昆虫共发生的识别来增强农业中的生物控制策略.
科学领域:
- 农业昆虫学 农业昆虫学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确识别掠食者-害虫关系对于有效的农业生物控制至关重要.
- 目前基于图像的方法在复杂的现场条件下识别昆虫的共同存在面临挑战,这限制了它们的生态适用性.
研究的目的:
- 开发一个强大的深度学习框架,用于准确识别捕食者-害虫组合的多标签识别.
- 提高基于图像的昆虫识别在农业环境中的生态适用性和准确性.
主要方法:
- 提出了一个混合深度学习框架,集成卷积神经网络 (CNN) 和变压器架构.
- 引入了一种新的共发生注意力机制,以捕捉昆虫类别之间的语义关系.
- 采用对对标签匹配损失函数来提高生态配对的准确性.
主要成果:
- 在5,037张图像的数据集上,获得了86.5%的F1分数和85.1%的mAP50.
- 在未见的捕食者-害虫对中表现出强烈的概括性,平均F1得分为79.6%.
- 超过了既定的基线,如ResNet-50,YOLOv8和视觉变压器.
结论:
- 开发的模型为多对象生态检测提供了一个强大的和可解释的方法.
- 该框架显示了在智能农业,无人机监测和精确害虫管理系统中部署的实际潜力.
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