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基于光的果叶疾病的检测YOLO-AppleLeafDxx

Hongyan Zou¹, Peng Lv¹, Maocheng Zhao¹

¹College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Plants (Basel, Switzerland)

|February 26, 2025

概括

这项研究介绍了LightYOLO-AppleLeafDx,这是一种有效的AI模型,用于检测果叶病. 它提供高精度和实时性能,对于提高作物产量和果园管理至关重要.

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
植物病理学植物病理学

背景情况:

早期发现果叶病对于有效的果园管理和最大限度地提高作物产量至关重要.
现有的方法可能缺乏实时现场应用所需的效率或准确性.
开发轻量级,高性能模型对于精密农业至关重要.

研究的目的:

推出LightYOLO-AppleLeafDx,这是一个改进的,基于YOLOv8的轻量级框架,用于检测果叶病.
为了提高检测准确度和回忆,同时最大限度地减少模型大小和计算负载.
验证模型在实时农业环境中的实际适用性.

主要方法:

通过增强YOLOv8模型,开发了一个轻量级检测框架,LightYOLO-AppleLeafDx.
集成的新型模块:Slim-Neck,SPD-Conv和SAHead,以优化网络架构.
进行了废弃性研究,以确认每个模块对性能的贡献.
在关键指标上评估模型,包括精度,mAP,模型大小和检测速度.

主要成果:

光YOLO-果LeafDx实现了高性能:0.930精度,0.965 mAP@0.5和0.587 mAP@0.5:0.95.5的精度.
该模型显著轻量化 (5.2 MB) 并支持快速实时检测 (107.2 FPS).
在RV1103硬件上展示了实际部署可行性,通过NPU通过14.8 FPS检测速度.

结论:

LightYOLO-AppleLeafDx提供了一种高效准确的解决方案,用于识别果叶病.
轻量级设计和实时功能使其适用于精密农业应用.
这一框架解决了现代农业对可访问,高性能工具的需求.

关键词:

在 SPD-Convvv 会议上.这就是YOLOv8的意义.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.叶子疾病叶子疾病轻量级的轻量级的轻量级的轻量级的精细的子和细的子

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相关概念视频

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.