适应性混合细分与转移学习中的元启发式优化相结合,用于植物叶病分类的转移学习
1Department of Computer Science and Engineering, Research Scholar (Anna University, Chennai), KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India. naveenkumarmkpr@gmail.com.
Scientific reports
|March 22, 2025
概括
本研究介绍了一种使用先进图像处理和转移学习的自动化植物疾病检测模型. 拟议的方法实现了高准确性,帮助农民早期识别疾病并提高作物产量.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 植物疾病大大降低了作物产量,并造成经济损失.
- 植物疾病的早期检测对于有效的管理和缓解至关重要.
- 现有的疾病检测方法往往受到特征提取和计算效率的限制.
研究的目的:
- 开发一种高效和自动化的模型,用于早期检测各种植物疾病.
- 克服以前植物疾病识别方法的局限性.
- 提高分类准确度,减少疾病检测中的计算负担.
主要方法:
- 叶子图像是从各种在线来源收集的.
- 图像预处理涉及中间波以消除噪声.
- 病态区域的细分使用可适应混合K-means与模糊C-means集群 (AHKM-FCM) 进行,并通过改进的基于随机变量的水步行算法 (IRV-WSA) 进行参数调整.
- 转移学习网络,包括Efficient-net,ResNet和Densenet,用于分类,IRV-WSA进行了微调.
主要成果:
- 拟议的IRV-WSA-ETLNet模型实现了高性能指标:94.853%的准确性,94.750%的灵敏性,94.888%的特异性,96.068%的F1评分.
- 该系统表现出了24378毫秒的低计算时间.
- 与现有方法相比,该模型显著提高了分类率.
结论:
- 开发的模型为自动检测植物疾病提供了高效和准确的解决方案.
- 集成先进的算法,如AHKM-FCM,IRV-WSA和转移学习网络,提高了检测能力.
- 这项技术可以帮助农民及时进行干预,从而促进作物生产并减少经济损失.
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