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AMS-YOLO:用于针对玉米害虫的智能植物保护的多级特征集成

Leilei Deng^1,2, Di Fang¹, Aziz Ullah¹

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

Frontiers in plant science

|October 20, 2025

概括

这项研究介绍了AMS-YOLO,这是一个先进的AI模型,用于准确检测玉米害虫,提高作物产量和质量. 这种轻量级模型在资源有限的农业环境中提供了高性能.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

玉米是全球重要的粮食作物,受到害虫威胁,影响产量和质量.
目前的害虫检测方法与各种害虫外观,相似性和复杂的现场条件作斗争.
准确有效地识别害虫对于有效的农业管理和可持续的作物保护至关重要.

研究的目的:

开发一种针对玉米害虫的增强检测模型,克服现有方法的局限性.
在具有挑战性的农业环境中提高玉米害虫识别的准确性和效率.
为精准农业应用创建一个轻量级和可部署的模型.

主要方法:

开发了AMS-YOLO,这是一个基于YOLOv8n的增强检测模型.
集成了三个协同作用的模块:SMCA注意力机制,MSBlock多尺度特征融合,以及AMConv优化下采样.
在13种常见的玉米害虫的数据集上训练和评估模型,跨越发展阶段.

主要成果:

AMS-YOLO实现了90.0%的精度,89.8%的回忆率和94.2%的mAP50,表现优于YOLOv8n.
与其他最先进的方法 (如SSD和RT-DETR) 相比,表现出卓越的性能.
该模型重量轻 (5.3MB),参数和计算要求降低,适用于Jetson Nano等边缘设备.

结论:

通过有针对性的架构改进,AMS-YOLO有效地解决了玉米害虫检测方面的挑战.
该模型的轻量级设计和高精度使其能够在资源有限的环境中进行现场部署.
这一进步支持精确的农药应用,资源优化和智能植物保护,以实现可持续农业.

关键词:

注意力机制注意力机制智能植物保护智能植物保护玉米的害虫玉米的害虫多尺度特征融合的多尺度特征融合.对象检测检测对象检测对象检测

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