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根据YOLO v8和Mask R-Convolution神经网络的基础上,对大麻的细分和表型计算

Nan Wang¹, Hongbo Liu¹, Yicheng Li¹

¹The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, College of Advanced Agricultural Sciences, Zhejiang A&F University, Linan, Hangzhou 311300, China.

Plants (Basel, Switzerland)

|September 28, 2023

概括

研究人员开发了深度学习模型,YOLO v8和Mask R-CNN,用于精确的菜分析. 这些方法自动化表型,改善产量估计和作物育种选.

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:

油菜的豆大小和长度对于作物生产率至关重要.
手动测量菜是劳动密集型和耗时的.
准确的表型化对于高效的作物育种和产量估计至关重要.

研究的目的:

开发和评估深度学习模型,用于自动识别和属性测量.
通过先进的图像分析,提高菜产量估计的准确性.
为了建立一个新的数据集和方法来确定菜的表型.

主要方法:

实施了两个深度学习模型:YOLO v8n和Mask R-CNN (Detectron2框架与Resnet101骨干).
开发一种基于硬币的方法,以使用机器视觉来准确估计子大小.
创建一个包含各种*Brassica napus*和*Brassica campestris*L.物种的综合数据集.

主要成果:

无论是YOLO v8n还是Mask R-CNN模型,在菜的识别和细分方面都实现了超过90%的精度.
在手动和机器视觉测量之间观察到高相关系数 (长度:0.991,宽度:0.989).
开发的方法在细分,计数和测量菜中表现出高准确度.

结论:

深度学习技术提供了一种有效的策略,可以自动化菜的表型化.
这项研究成功建立了大麻的数据集,并验证了准确的,自动化的测量方法.
这些方法可以显著加快在菜和类似的豆类作物中识别和选生殖质资源的速度.

关键词:

进行计数,计数.深度学习是一种深度学习.实例细分实例细分实例细分测量长度和宽度的尺寸.菜种子的果.

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