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在温室植物环境中使用改进的YOLOv8算法检测植物疾病

Xuewei Wang¹, Jun Liu²

¹Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang, China.

Scientific reports

|February 21, 2024

概括

这项研究介绍了YOLOv8n-vegetable,这是一个增强的物体检测模型,用于识别温室中的植物疾病. 它提高了准确性和速度,同时减少了模型大小,以实现高效的实时疾病监测.

科学领域:

农业技术农业技术
计算机视觉计算机视觉
植物病理学植物病理学

背景情况:

在温室中准确检测植物疾病对于作物管理至关重要.
现有的物体检测模型面临的挑战是不精确的检测,小物体和不同的尺度.

研究的目的:

开发一个优化的物体检测模型 (YOLOv8n-vegetable) 以提高在温室环境中的植物疾病识别.
提高检测准确度,速度和效率,以实时监测疾病.

主要方法:

集成使用GhostConv的新型C2fGhost模块,以减少参数和提高性能.
整合了封闭感知注意模块 (OAM),以在融合过程中保存特征信息.
增加了一个专门的层,用于小物体检测和实施HIoU边界损失,以获得更好的收和回归.

主要成果:

增强的YOLOv8n-蔬菜模型在自建的温室植物疾病数据集上实现了6.46%的平均平均精度 (mAP) 增加.
模型参数和尺寸分别减少了0.16G和0.21 MB.
与原始模型和其他先进探测器相比,证明了更好的检测速度,准确性和实时能力.

结论:

YOLOv8n-vegetable在植物疾病检测准确性和效率方面取得了重大进展.
该模型的轻量级和快速性质使其具有高度竞争力,并适合实际的温室应用.
该模型为植物疾病的自动和实时监测提供了一个有希望的解决方案.

关键词:

注意力机制注意力机制温室植物环境温室植物环境对象检测检测对象检测对象检测植物疾病植物疾病这就是YOLOv8n.

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