通过将生理特征与SSA-XGBoost算法集成来智能分级甘叶病的严重程度
Xinrui Wang1, Jihong Sun2, Peng Tian1
1College of Big Data, Yunnan Agricultural University, Kunming, Yunnan, China.
Frontiers in plant science
|October 31, 2025
概括
这项研究引入了一种智能方法,使用生理特征来评估甘叶病的严重程度. 优化的SSA-XGBoost模型实现了高精度,为农业早期疾病检测提供了实用工具.
科学领域:
- 农业科学 农业科学
- 植物病理学 植物病理学
- 计算生物学 计算生物学
背景情况:
- 准确评估甘叶病的严重程度对于有效的作物管理和产量保存至关重要.
- 现有的方法可能缺乏快速的现场诊断的效率或可访问性.
研究的目的:
- 开发一种智能方法,利用生理特征识别甘叶病的严重程度.
- 用Sparrow搜索算法 (SSA) 优化各种机器学习模型的性能进行比较.
主要方法:
- 从三种疾病的甘叶收集了生理数据 (SPAD值,叶子温度,含量) 在四个严重程度的三种疾病.
- 开发并优化了六个分类模型 (KNN,AdaBoost,RF,LR,DT,XGBoost) 使用SSA进行超参数调整.
- 标准化数据使用最小-最大缩放用于模型输入.
主要成果:
- 搜索算法 (SSA) 显著提高了所有测试模型的分类性能.
- 该SSA-XGBoost模型表现出卓越的性能,实现精确度,回忆,F1得分和准确度超过0.9186 (PRFA得分为0.9326).
- 对独立数据集的验证证实了强大的概括能力,总准确率为0.91.
结论:
- 拟议的基于生理特征的方法在数据可访问性,计算效率和模型透明度方面比基于图像的方法具有优势.
- 这项研究为甘疾病的智能诊断和早期预警系统提供了可靠的技术框架.
- 这些发现支持这种方法在现场快速农业诊断的实际应用.
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