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一个轻量级的混合模型用于可扩展和强大的植物叶病分类

Muhammad Asghar¹, Zahid Farooq Khan², Muhammad Ramzan³

¹Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan.

Scientific reports

|September 3, 2025

概括

一个新的轻量级深度学习模型,HPDC-Net,准确地识别了土豆和西红中的植物叶病. 这种高效的模型以极少的计算资源提供了高精度, 帮助农业早期发现疾病.

科学领域:

农业科学
计算机视觉
深度学习

背景情况:

植物疾病导致农作物产量大幅下降和经济损失,威胁到粮食安全.
准确和早期的植物疾病诊断对于有效的管理和治疗至关重要.
现有的自动化系统在现场环境中面临计算资源限制的挑战.

研究的目的:

开发一个轻量级和紧的卷积神经网络模型,以有效地分类植物叶病.
解决在计算能力有限的现场条件下快速识别疾病的需求.
提出混合植物疾病分类网络 (HPDC-Net) 以加强农业诊断.

主要方法:

采用混合区块架构设计的HPDC-Net:深度可分离的卷积区块 (DSCB),双路径自适应的聚合区块 (DAPB) 和通道智能注意力改进区块 (CARB).
在DSCB中使用深度可分离的卷积,以有效地提取坚固的特征,确保轻量级模型.
在三个数据集上进行了培训和评估,用于对马和西红叶病进行分类.

主要成果:

在所有三个植物叶病分类数据集中都取得了99%以上的准确性.
保持0.06个GFLOP和0.52M个参数 (10类) /0.17M个参数 (3类) 的低计算复杂度.
证明了高推理速度:CPU上的19.82 FPS和GPU上的408.25 FPS.

结论:

HPDC-Net是一个准确,轻量级和高效的植物疾病早期检测模型.
该模型的性能表明它适用于具有计算限制的现实世界农业应用.
在GitHub上提供代码有助于进一步的研究和实施.

关键词:

紧神经网络植物健康监测边缘人工智能模型叶病的分类精准农业

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