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无人驾驶飞行器图像检测玉米-YOLOv8n幼苗泄漏情况

Jiaxin Gao¹, Feng Tan², Jiapeng Cui³

¹College of Engineering, Heilongjiang Bayi Agricultural University, Daqing, China.

Frontiers in plant science

|June 9, 2025

概括

这项研究引入了一种使用无人机遥感和改进的玉米-YOLOv8n模型的新方法,以准确检测田间缺失的玉米苗. 该系统通过识别及时重新种植和提高作物产量的差距来增强精准农业.

科学领域:

农业工程农业工程
遥感遥感遥感遥感
计算机视觉计算机视觉

背景情况:

在玉米田中缺少的苗木会降低产量,并且很难用当前的无人机遥感器 (UAV) 来检测.
播种机械和种子发芽的局限性导致苗木缺口,影响整体作物生产.

研究的目的:

开发一种快速而准确的方法,使用无人机遥感来检测丢失的玉米苗.
提高玉米种植管理和后续作物决策的效率和精度.

主要方法:

利用图像处理技术 (背景细分,茎中心检测,线性拟合) 来计算来自无人机图像的种子间距离.
采用了改进的玉米-YOLOv8n模型来检测实际的苗木出现和识别差距.

主要成果:

玉米-YOLOv8n模型实现了高性能,平均精度为97.4%,回忆率为93.1%,F1得分为93.7%.
轻量级模型展示了实时检测能力 (12.8毫秒推断时间) 和在各种现场条件下的稳定性.
开发了一种线性回归模型来预测缺失的苗木数量,并通过RMSE和MAE指标验证.

结论:

开发的模型准确地检测到玉米种植的缺口,使种植操作的评估.
为及时重新种植,提高玉米产量和推进精准农业提供关键数据.
这项研究提供了一种实际的解决方案,用于在大规模玉米种植中管理缺失的苗木.

关键词:

这就是YOLOv8n.图像处理是图像处理的过程.玉米种植的幼苗自然场景自然场景的场景.无人驾驶飞行器是一种无人驾驶飞行器.

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Light Acquisition

Light Acquisition

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