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WheatSpikeNet:一个改进的小麦分段模型,用于从现场成像中准确估计

M A Batin¹, Muhaiminul Islam¹, Md Mehedi Hasan¹

¹Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, Bangladesh.

Frontiers in plant science

|September 11, 2023

概括

准确的小麦尖计数对于表型和产量估计至关重要. 本研究介绍了WheatSpikeNet,这是一个增强的实例细分模型,可以从现场图像中检测和细分小麦的最新性能.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物育种植物育种

背景情况:

植物表型化对于识别理想的作物特征和评估环境影响至关重要.
小麦密度是一个关键的农学因素,但在不同的田间条件下准确计数仍然具有挑战性.

研究的目的:

开发一种高效和准确的方法,用于在田间图像中对小麦进行细分和计数.
引入一个精选的数据集 (SPIKE-segm) 和一个优化的实例细分模型 (WheatSpikeNet).

主要方法:

使用了布面具RCNN架构,并进行了增强和超参数调整.
采用ResNet50作为支柱的可变形卷积网络 (DCN),通用ROI提取器 (GRoIE) 和侧面意识边界定位 (SABL).
进行了全面的废弃分析,以确定最有效的模型组件.

主要成果:

在SPIKE数据集上,WheatSpikeNet实现了卓越的性能,bbox和mask mAP分数分别为0.9303和0.9416.
展示了最先进的检测和细分能力,优于现有方法.
在尖端检测方面实现了高达0.41%的mAP改进,在细分任务方面实现了3.46%的mAP改进.

结论:

拟议的WheatSpikeNet模型为准确的小麦分段和计数提供了一个强大的解决方案.
这一进步有助于在小麦育种计划中精确估计产量.
优化的模型和数据集有助于改进自动化植物表型化技术.

关键词:

连锁RCNN 连锁RCNN 连锁可变形卷积网络的可变形卷积网络.植物表型化植物表型化细分化细分化的细分化小麦的棘是小麦的棘.

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