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使用CPD-WeedNet在密集的作物树冠中精确细粒度杂草实例细分

Lan Luo¹, Jinfan Wei¹, Lingyun Ni¹

¹College of Information Technology, Jilin Agricultural University, Changchun, China.

Frontiers in plant science

|September 19, 2025

概括

CPD-WeedNet为可持续农业提供了精确的农田杂草细分. 这种新的框架可以提高复杂领域的准确性和效率,帮助有针对性的除草系统.

科学领域:

农业技术农业技术
计算机视觉计算机视觉计算机视觉
机器学习是机器学习.

背景情况:

准确的农田杂草细分对于有针对性的除草和可持续农业至关重要.
挑战包括相似的作物/杂草形态,遮蔽,照明变化和尺度多样性,阻碍了资源有限的平台上的当前方法.

研究的目的:

提出一种新的实例细分框架,CPD-WeedNet,用于在复杂的农业环境中识别细粒度杂草.
为了提高实时智能除草系统的准确性和效率.

主要方法:

开发了CPD-WeedNet的三个核心组件:CSP-MUIB骨干用于增强的特征歧视,PFA部用于整合浅层细节,DFS部利用变压器用于全球背景.
在自建的大豆田杂草数据集和公共Fine24数据集上进行评估.

主要成果:

在大豆数据集上,CPD-WeedNet实现了80.6%的mAP50 (面膜) 和85.3%的mAP50 (盒子),表现优于YOLO基线.
在Fine24数据集中,它达到75.4%mIoU和81.7%mAcc,显示了性能和效率之间的平衡.
像素级度量 (mIoU,mAcc) 在大豆数据集上分别达到86.6%和94.6%.

结论:

CPD-WeedNet展示了低成本,实时智能除草系统的巨大潜力.
该框架提供了一个强大的解决方案,用于在具有挑战性的农业条件下精确细分杂草.
这项研究通过改进的杂草识别技术来推进精准农业.

关键词:

这就是CPD-WeedNet.田野杂草的细分田野杂草的细分细粒度识别细粒度识别实例细分实例细分实例细分精准农业精准农业精准农业

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