XSE-TomatoNet:一种可解释的基于人工智能的西红叶病分类方法,使用EfficientNetB0与挤压激发块和多尺度特征融合的EfficientNetB0
Md Assaduzzaman1, Prayma Bishshash1, Md Asraful Sharker Nirob1
1Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia 1216, Dhaka, Bangladesh.
MethodsX
|July 14, 2025
概括
准确的番茄叶病诊断对于农业至关重要. 增强的EfficientNetB0模型XSE-TomatoNet在识别疾病方面达到99.41%的准确性,优于其他模型并帮助种植者.
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:
- 准确的番茄叶病诊断对于农业生产率和防止生态系统损害至关重要.
- 深度学习模型显示出希望,但与微妙的疾病变异作斗争.
- 现有的方法需要改进,以准确有效地识别疾病.
研究的目的:
- 引入XSE-TomatoNet,这是一个增强的深度学习模型,用于准确的番茄叶疾病分类.
- 通过结合Squeeze-and-Excitation (SE) 块和多尺度特征融合来改进现有的EfficientNetB0架构.
- 评估XSE-TomatoNet的性能和可解释性,以便在农业中实际应用.
主要方法:
- 通过增强EfficientNetB0以Squeeze-and-Excitation (SE) 块和多级特征融合,开发了XSE-TomatoNet.
- 提取了多个尺度的特征,用SE块对它们进行了改进,并使用全球平均聚合 (Global Average Pooling) 将它们合并.
- 采用数据增强,废除研究,十倍交叉验证,LIME,SHAP,Grad-CAM和Grad-CAM++进行评估和解释.
主要成果:
- XSE-TomatoNet实现了99.11%的高精度 (99%的精度和回忆),明显超过了MobileNet (87.44%) 和VGG-19 (95.50%).
- 10倍的交叉验证显示出强烈的概括性,平均训练准确率为99.41%和验证准确率为98.88%.
- 模型解释性技术 (LIME,SHAP,Grad-CAM,Grad-CAM++) 提供了对决策和视觉疾病识别的见解.
结论:
- XSE-TomatoNet为番茄叶病的分类提供了一个高度准确和强大的解决方案.
- 增强的模型架构有效地捕捉了细微的疾病变异,提高了诊断精度.
- 将其集成到基于网络的系统中,有助于番茄种植者在实践中采用,提高了作物管理.
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