LeafAI:用于边缘计算的可解释植物疾病检测
Md Abdullah Al Kafi1, Sumit Kumar Banshal2, Raka Moni1
1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
PloS one
|January 23, 2026
概括
这项研究引入了一种混合人工智能模型,用于有效检测植物疾病. 它通过首先识别健康的叶子来显著加快分析速度,减少农业中的计算负载和资源使用.
科学领域:
- 农业人工智能 农业人工智能
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 现实世界农业表现出显著的阶级不平衡,健康的植物叶子数量超过了生病的叶子.
- 这种不平衡给计算密集型深度学习模型在自动检测植物疾病方面带来了挑战,导致效率低下.
- 需要可持续的AI解决方案来优化资源消耗和提高检测准确度.
研究的目的:
- 为实时植物疾病检测提供一种代,混合AI方法,以提高计算效率,可解释性和可扩展性.
- 解决阶级不平衡和高资源消耗在自动植物疾病识别方面的挑战.
- 为精准农业开发可靠和可持续的AI解决方案.
主要方法:
- 一个两阶段的混合系统:用于初始健康叶子排除的轻量级传统分类器,其次是用于疾病叶子分类的深度学习模型 (ResNet,DenseNet,MobileNet,EfficientNet).
- 实施可解释AI (XAI) 方法,特别是梯度加权类激活映射 (Grad-CAM),以生成预测热图.
- 综合物流回归和Mobilenetv3的混合模型的评估,以提高性能和效率.
主要成果:
- 与传统的深度学习模型相比,混合模型的推断速度高达77.6%,精度损失最小约为3%.
- 在一次大规模测试 (1227张图像) 中,混合模型将入门级笔记本电脑的总推断时间从4 548秒减少到1010.13秒,CPU负载最小.
- 通过突出显示模型预测至关重要的图像区域,Grad-CAM热图提供了透明度,有助于特征改进.
结论:
- 拟议的混合人工智能方法为精准农业中的植物疾病检测提供了一个可扩展,可持续和可靠的解决方案.
- 这种方法有效地解决了阶级不平衡,并优化了推理效率,使人工智能解决方案在现实世界农业应用中变得更加实用.
- 集成XAI增强了模型的可解释性和信任性,这对于在农业实践中采用AI至关重要.
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