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使用U-Net架构进行精准农业的增强叶病细分:深度学习方法

Gurpreet Singh¹, Asma A Al-Huqail², Ahmad Almogren³

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

Food science & nutrition

|July 15, 2025

概括

本研究引入了U-Net深度学习模型,通过图像细分来准确识别叶病. 该模型精确地识别了病变植物区域,帮助精准农业.

科学领域:

计算机视觉计算机视觉
植物病理学植物病理学
机器学习机器学习

背景情况:

叶病显著影响作物产量.
准确和早期发现疾病对于有效管理至关重要.
传统的方法往往缺乏精度和可扩展性.

研究的目的:

为精确的叶病细分开发一个深度学习模型.
评估U-Net架构用于识别患病的叶子组织.
通过自动化疾病识别来增强农业实践.

主要方法:

使用了U-Net卷积神经网络 (CNN) 架构.
在"叶病细分"数据集上训练并验证了模型,并附有注释图像.
采用图像预处理,增强和规范化技术.
使用Adam优化器,学习率为0.001.

主要成果:

实现了99.70%的训练准确率和98.99%的验证准确率.
在像素级别上对患病的叶片区域进行细分,证明了高精度.
在未见的数据上展示了强大的概括能力.
性能优于传统的图像处理技术.

结论:

U-Net模型为叶病细分提供了强大而准确的解决方案.
深度学习方法在识别植物疾病方面非常有效.
这项技术在精密农业领域的实际应用中具有显著的潜力.

关键词:

在美国,CNN是CNN.这是一个U-Net架构.深度学习是一种深度学习.图像处理是图像处理的过程.叶病细分叶病细分植物病理学植物病理学精准农业精准农业精准农业语义细分语义细分语义细分语义细分

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