果疾病分类的双模式融合,使用基于动态注意力的叶子和水果图像集
Muhammad Mohsin1, Muhammad Shadab Alam Hashmi2, Irene Delgado Noya3,4,5,6
1Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, 64200, Pakistan.
Scientific reports
|November 26, 2025
概括
这项研究引入了一种多式深度学习模型,用于使用叶子和水果图像准确检测果疾病. 先进的融合技术显著提高了分类准确性,有助于农业诊断.
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:
- 果种植对热带经济至关重要,但受到诸如病和红等疾病的威胁.
- 由于症状相似和错误分类,早期和准确的果病检测具有挑战性.
研究的目的:
- 开发一个多式联网深度学习框架,以加强果疾病的分类.
- 通过整合叶子和水果图像数据来提高检测准确性和概括性.
主要方法:
- 在果叶和水果疾病数据集上训练了个别的卷积神经网络 (CNN) 模型 (ResNet-50,MobileNetV2,EfficientNet-B0,ConvNeXt).
- 引入了模式注意力融合 (MAF) 机制,以动态结合不同图像类型的预测.
- 实施了对类意识的增强管道,以减轻过度装配和增强泛化.
主要成果:
- 以注意力为基础的融合策略使用EfficientNet-B0.0.实现了99.08%的测试准确率,99.03%的F1得分和99.96%的ROC-AUC.
- 多式联络方法显著优于单个模型和静态聚变方法.
- 一个Web应用程序通过分布之外的测试证明了其在现实世界中的适用性.
结论:
- 结合来自多个植物器官 (叶子和果实) 的视觉数据,可以提高诊断准确度.
- 适应性模型对上下文特征的关注对于有效的农业诊断至关重要.
- 拟议的框架为早期和准确的果疾病识别提供了强有力的解决方案.
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