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基于深度学习的大麦疾病量化用于可持续的作物生产

Yassine Bouhouch^1,2, Qassim Esmaeel¹, Nicolas Richet¹

¹Université de Reims Champagne-Ardenne, Unité de recherche Résistance Induite et Bioprotection des Plantes (RIBP), EA 4707 USC INRAE 1488, Reims, France.

|June 4, 2024

概括

一个新的深度学习模型使用Cascade R-CNN和U-Net.net准确量化大麦 (Hordeum vulgare) 的网点病. 这项技术有助于自动疾病评估和查生物控制剂.

科学领域:

植物病理学植物病理学
计算生物学计算生物学
农业科学农业科学

背景情况:

由Drechslera teres引起的净斑显著影响大麦 (Hordeum vulgare) 的产量.
准确量化疾病严重程度对于作物管理和研究至关重要.

研究的目的:

开发和验证一种深度学习模型,用于量化大麦的净斑病症状.
通过Cascade R-CNN和U-Net架构评估模型的性能.

主要方法:

使用注释大麦叶图像的数据集进行训练.
采用Cascade R-CNN和U-Net深度学习架构用于症状检测和量化.
与传统的亡测量和实时PCR对模型进行了验证.

主要成果:

实现了95%的准确性和0.99的雅卡德指数用于疾病检测和量化.
结合的Cascade R-CNN和U-Net模型有效地检测出小的,不规则的病变.
自动测量显示与古典和分子方法的高相关性.

结论:

开发的深度学习模型提供了准确和自动化的净斑病量化.
该工具可以集成到用于疾病监测和生物控制剂查的自动化系统中.

关键词:

BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA BCA C 连锁R-CNN连锁连锁连锁连锁这就是U-Net.大麦大麦大麦大麦大麦大麦大麦大麦深度学习是一种深度学习.网红斑点网红斑点网红斑点现型定制现型定制在实时PCR中进行实时PCR.

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Light Acquisition

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