一个轻量级且易于解释的CNN模型,用于加强植物疾病的诊断
Chiranjit Pal1, Swastik Karmakar2, Imon Mukherjee3
1Computer Science & Engineering, Indian Institute of Information Technology, Kalyani, India. chiranjit_jrf21@iiitkalyani.ac.in.
Scientific reports
|August 21, 2025
概括
一个新的轻量级人工智能模型Mob-Res使用先进的深度学习准确地检测作物疾病. 这种自动化植物病检测系统提高了农业效率并支持全球粮食安全.
科学领域:
- 农业科学
- 计算机科学
- 人工智能
背景情况:
- 农作物疾病对全球粮食安全构成重大威胁,造成巨大的经济损失.
- 传统的疾病诊断方法往往是缓慢的,劳动密集的,对于大型农业运作缺乏可扩展性.
- 需要快速,准确和可扩展的自动化解决方案来检测作物疾病.
研究的目的:
- 介绍Mob-Res,一个新的,轻量级的深度学习架构,用于自动检测作物疾病.
- 在不同的基准数据集上评估Mob-Res的性能和效率.
- 使用可视化技术提高模型预测的可解释性.
主要方法:
- 通过将剩余学习与MobileNetV2特征提取器集成来开发Mob-Res.
- 在两个大规模数据集上训练并验证了Mob-Res:植物疾病专家 (199,644张图像,58个类别) 和PlantVillage (54,305张图像,38个类别).
- 使用准确度指标和跨域验证率 (CDVR) 评估模型性能;使用Grad-CAM,Grad-CAM++和LIME进行解释.
主要成果:
- 在植物疾病专家数据集上,Mob-Res的平均准确率高达97.73%,在PlantVillage数据集上达到99.47%.
- 该模型表现出强大的跨领域适应性,并超过了包括ViT-L32在内的先前训练的突出的卷积神经网络 (CNN) 架构.
- 与其他模型相比,Mob-Res的参数数量显著降低 (3.51万),推断时间更快.
结论:
- Mob-Res是一个非常准确,高效和轻量级的解决方案,用于自动检测植物疾病,适合移动应用.
- 该模型的可解释性功能为疾病识别过程提供了宝贵的视觉洞察力.
- 支持大规模农业监测,提高作物产量,并为全球粮食安全做出贡献.
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