使用多类SVM方法与内核比较增强基于图像的作物疾病检测分类
Parkavi Sridhar1, Parthiban Angamuthu2
1Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, 632 014, India.
Scientific reports
|November 17, 2025
概括
早期发现作物疾病对于粮食安全至关重要. 这项研究使用机器学习准确识别植物叶上的黄色生和炭菌等疾病,达到99%的准确性.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 植物病严重影响农业生产,威胁到粮食安全和经济稳定.
- 黄色生和炭病等特定疾病导致小麦,棉花和果等作物的产量大幅下降.
研究的目的:
- 开发和评估一种机器学习框架,用于早期和精确地检测各种作物叶病.
- 为了比较不同多类支持向量机 (SVM) 内核在疾病分类方面的有效性.
主要方法:
- 实现了一个机器学习管道,包括图像预处理,细分 (GraphCut),基于纹理的特征提取和分类.
- 用于模型培训和验证的数据集包括多种作物的9111个增强图像.
- 使用分层5倍交叉验证系统评估SVM内核性能.
主要成果:
- 线性内核SVM表现出卓越的性能,达到99.0%的准确性,98.6%的精度,98.7%的回忆率和98.6%的F1分数.
- 拟议的方法结合了双边过,GraphCut细分和纹理特征,超过了以前基于SVM的方法.
结论:
- 核的选择和预处理显著提高了植物疾病分类的准确性.
- 这些发现支持开发可扩展和可靠的自动化植物疾病检测系统,并有可能在未来进行深度学习比较.
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