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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Updated: Sep 13, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

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Published on: May 10, 2024

基于深度学习的自动诊断大米叶疾病使用组合CNN模型.

Prameetha Pai¹, S Amutha², Seema Patil¹

¹Department of Computer Science & Engineering, B.M.S. College of Engineering, Bengaluru, India.

Scientific reports

|July 29, 2025

概括

这项研究引入了一种自动化的深度学习系统,用于诊断六种常见的叶疾病,改善作物管理. 开发的整体模型为实际农业应用提供了准确和可扩展的疾病识别.

关键词:

农业人工智能农业人工智能自动化诊断自动化诊断农作物生产率农作物生产率深度学习是一种深度学习.组合学习学习组合学习米疾病米疾病

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科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

病严重威胁全球作物产量和粮食安全.
传统的病诊断方法往往是劳动密集型,耗时,需要专门的专业知识.
对于早期检测水疾病的有效,准确和可扩展的工具有着至关重要的需求.

研究的目的:

开发和评估基于深度学习的自动诊断系统,用于识别六种常见的叶疾病.
为了比较七个高级深度学习架构对大米疾病分类的性能.
创建一个整体模型,以提高诊断准确性和稳定性.

主要方法:

一个大规模的数据集的注释叶图像涵盖六种疾病被策划.
七个深度学习模型 (MobileNetV2,GoogleLeNet,EfficientNet,ResNet-34,DenseNet-121,VGG16,ShuffleNetV2) 被训练并进行了评估.
通过使用平均融合策略集成表现最佳的个体网络,构建了一个整体模型.

主要成果:

谷歌LeNet,DenseNet-121,ResNet-34和VGG16在准确性方面表现出卓越的表现,并减少了类混.
整体模型显示,与单个模型相比,错误分类率显著降低.
该系统实现了强大且可扩展的诊断功能,在各种环境数据上进行了验证.

结论:

深度学习,特别是组合方法,为自动化叶疾病诊断提供了强大的方法.
开发的系统为现实世界农业应用提供了可靠和可扩展的解决方案,有助于及时管理作物.
这项技术有可能提高作物生产率,并减轻米疾病造成的损失.