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在商业玉米作物中,智能排间和排内早期发现杂草

Adrià Gómez^1,2, Hugo Moreno², Dionisio Andújar²

¹Laboratorio de Propiedades Físicas: Técnicas Avanzadas en Agroalimentación LPF-TAGRALIA, School of Agricultural, Food and Biosystems Engineering (ETSIAAB), Technical University of Madrid, Avenida Puerta de Hierro 2-4, 28040 Madrid, Madrid, Spain.

Plants (Basel, Switzerland)

|April 23, 2025

概括

YOLOv11深度学习模型准确地识别了玉米田中的杂草,提高了作物产量. YOLOv11m非常适合在精密农业中实时,节能地进行现场部署.

科学领域:

农业科学农业科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

排内杂草的竞争大大降低了作物的生产力,因为近距离和封闭.
有效的杂草管理对于优化作物产量和降低运营成本至关重要.

研究的目的:

评估深度学习模型,用于在玉米田中准确识别杂草和作物.
为了比较Faster R-CNN,RT-DETR和YOLOv11用于杂草检测的性能.

主要方法:

创建了玉米田的数据集,其中包括三个杂草物种:Cyperus rotundus,Echinochloa crus-galli和Solanum nigrum.
深度学习模型 (Faster R-CNN,RT-DETR,YOLOv11) 被训练并评估用于杂草和作物识别.
进行了硬件评估,以评估现场部署的可行性.

主要成果:

YOLOv11实现了最高的性能,97.5%的平均平均精度 (mAP) 在每秒34 (FPS).
更快的R-CNN和RT-DETR在11和27FPS时分别显示了91.9%和97.2%的mAP.
鉴于其高精度 (94.4% mAP) 和更低的能源使用,YOLOv11m被确定为最适合现场部署.

结论:

先进的深度学习模型,特别是YOLOv11,对于精确的行内和行内杂草管理是有效的.
使用这些人工智能技术,可以在最小的作物干扰下早期检测杂草.
整合到农业机械可以提高除草控制,降低成本,并支持可持续农业.

关键词:

深度学习是一种深度学习.能源效率是指能效的能源效率.在行内除草.玉米玉米玉米是一种对象检测检测对象检测对象检测现场特定的杂草管理 (SSWM)视觉变压器视觉变压器

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