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DBA-DeepLab:双脊柱注意力增强DeepLab V3+模型用于植物疾病细分

Neha Sharma¹, Sheifali Gupta¹, Fuad Ali Mohammed Al-Yarimi²

¹Chitkara Institute of Engineering and Technology Chitkara University Rajpura Punjab India.

Food science & nutrition

|July 23, 2025

概括

一个新的双脊柱注意力增强深度实验室 (DBA-DeepLab) 模型提高了植物疾病细分的准确性. 这种人工智能工具通过准确识别病变植物区域来增强早期诊断和精准农业.

科学领域:

计算机视觉计算机视觉
农业科学农业科学
机器学习机器学习

背景情况:

准确的植物病细分对于早期检测和农业有效管理至关重要.
现有的细分模型经常与复杂的疾病模式和背景噪音作斗争.

研究的目的:

开发一个先进的深度学习模型,用于精确的植物疾病细分.
提高作物中自动疾病识别的准确性和效率.

主要方法:

提出了一种双脊柱注意力增强的深度实验室 (DBA-DeepLab) 模型,将ResNet-50和EfficientNet-B3脊柱与卷积块注意力模块 (CBAM) 集成在一起.
集成的多尺度特征提取,注意力机制,以及 Sobel 过用于边缘保护.
在50个时代中使用Adam优化器在PlantDoc数据集上训练和验证模型.

主要成果:

与标准的DeepLabV3+变体相比,DBA-DeepLab实现了优越的细分性能.
该模型显示了高精度 (99.35%),子系数 (91.48%),IOU系数 (85.85%),精度 (96.78%) 和回忆 (100%).
Grad-CAM可视化证实了该模型能够专注于受疾病影响的区域并减少背景噪声的能力.

结论:

DBA-DeepLab模型为植物疾病细分提供了一个高度准确,高效和可扩展的解决方案.
该模型显示了智能农业,自动疾病检测和精准农业的应用潜力.
注意力机制和双脊柱架构有助于提高细分精度和稳定性.

关键词:

这是一个CBAM模块.在DBA-DeepLab中使用.深度实验室 V3+有效网 B3 有效网资源网 50 资源网 50 资源网双重的脊柱是双重的脊柱.植物叶子植物叶子叶子细分化细分化的细分化

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