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自动根测量:用于测量豆根长度的轻量级方法

Haoyu Jiang¹, Chenhan Hu², Luxu Tian³

¹College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 210031, China.

|December 8, 2025

概括

本研究介绍了一种轻量级的自动根测量 (ARM) 模型,用于高效的豆根长度分析. 该ARM模型增强了作物抗压力研究,并支持可持续农业.

科学领域:

农业科学农业科学
植物生物学植物生物学
计算机视觉计算机视觉

背景情况:

气候变化增加了极端天气事件,影响作物产量和稳定性.
发展抗压作物对于可持续农业和粮食安全至关重要.
根长度是水/营养吸收和耐压力的关键特征,但测量是一个瓶.

研究的目的:

开发一个高效,经济高效的自动化系统来测量根长度.
解决手动测量和高成本现有自动化模型的局限性.
支持在作物育种和农业研究中的高通量表型化.

主要方法:

开发了一个种子发芽图像采集系统和一个豆根数据集.
使用YOLOv8-Seg-n实例细分模型进行轻量级的自动根测量 (ARM) 模型.
应用特征蒸,结构修剪和后处理用于根长度计算.

主要成果:

在ARM模型有1.81M参数,8.3GFLOPs,4.2MB的文件大小,并实现70.4FPS.
实现了高性能,mAP@0.5为90.3%,AProot为81.2%,与手动测量有很强的相关性 (R2 = 0.993).
通过对干旱条件下的豆根长度进行时间序列分析,证明了实际应用潜力.

结论:

ARM 模型提供了一种高效,成本效益的解决方案,用于在豆类中测量高通量根长度.
它平衡了准确性,速度和计算要求,显示了农业和育种的广泛潜力.
为粮食安全提供关键的技术支持,并增强作物抗压能力.

关键词:

具有成本效益的部署.实例细分是指实例的细分.豆种子豆种子根长度测量方法根长度测量方法培育抗压作物的作物繁殖.

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