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图像识别基于修改后的 rime优化算法和 ConvNeXt 网络的图像识别
1College of Information Science and Technology, Gansu Agricultural University, Lanzhou, China.
Frontiers in plant science
|September 29, 2025
概括
这项研究引入了一个新的ConvNeXt模型,具有注意力机制和元启发性优化,用于准确诊断果叶病. 该模型显著改善了早期检测,促进了作物健康和农业生产率.
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:
- 早期诊断果叶病对作物健康和农业生产率至关重要.
- 传统的方法与复杂的模式,阶级不平衡和现实世界的挑战 (如灯光不良) 相斗争.
研究的目的:
- 开发一种用于准确和早期诊断果叶病的新型模型.
- 增强特征提取和模型概括,以提高诊断性能.
主要方法:
- 将ConvNeXt模型与卷积块注意模块 (CBAM) 集成,用于特征提取.
- 使用修改后的 rime优化算法 (MRIME) 进行超参数调整,以避免过拟合.
- 对果叶疾病症状数据集的评估.
主要成果:
- 拟议的模型实现了高性能指标:92.7%的准确性,92.5%的精度,92.6%的回忆,92.5%的F1得分和92.3%的mAP.
- 废弃性研究显示,CBAM提高了精度1.5%,MRIME增加了1.2%.
- 该模型超过了像ResNet50和EfficientNet-B0.0这样的基线模型.
结论:
- 注意力机制 (CBAM) 和元听觉优化 (MRIME) 的结合方法显著提高了果叶病的检测.
- 这种新型模型为自动植物疾病诊断提供了最先进的解决方案.
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