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面向农业的实用人工智能:用于检测菜叶病的自我监督的注意力框架

Nilavro Das Kabya¹, Md Shaifullah Sharafat¹, Rahimul Islam Emu¹

¹Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.

|January 16, 2026

概括

一个新的深度学习框架准确地分类了马拉巴菜叶病如Alternaria和草虫. 高效的SimSiam-CBAM-ResNet-50模型为农业应用提供了一个实用的解决方案.

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:

马拉巴尔菜在孟加拉国至关重要,但易受Alternaria叶斑和草虫的感染.
准确的疾病鉴定对于保持作物产量和质量至关重要.

研究的目的:

开发一个高效和可解释的深度学习框架,用于马拉巴菜叶病的自动分类.
在有限的数据条件下评估各种深度学习架构的性能,包括CNN和变压器.
用自我监督的预训来解决注释短缺的问题.

主要方法:

创建了马拉巴菜图像的精心策划的数据集,分为健康,Alternaria和草类别.
像SpinachCNN,Spinach-ResSENet,SpinachViT和SwinV2-Base这样的模型被训练和评估.
在未标记的数据上使用SimSiam进行自我监督的预训,然后进行监督的微调.
域优化的模型包含了注意力机制 (挤压和刺激,卷积块注意力模块).

主要成果:

该SimSiam-CBAM-ResNet-50模型实现了97.31%的测试准确度和高ROC-AUC,证明了对噪声的稳定性.
像SwinV2-Base这样的基于变压器的模型显示出略高的精度,但需要大量的预训练和更多的参数.
可解释性方法 (Grad-CAM,LayerCAM) 证实了模型的重点是相关的病理区域.

结论:

拟议的SimSiam-CBAM-ResNet-50为马拉巴菜疾病检测提供了一个参数高效和可部署的解决方案.
自主监督学习有效地缓解了农业数据集中的注释稀缺性.
深度学习为精准农业提供了强大的工具,有助于疾病管理和作物保护.

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