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在基于改进的ShuffleNetV2的田间作物中识别叶病

Hanmi Zhou¹, Jiageng Chen¹, Xiaoli Niu¹

¹College of Agricultural Engineering, Henan University of Science and Technology, Luoyang, China.

Frontiers in plant science

|March 26, 2024

概括

本研究介绍了REM-ShuffleNetV2,这是一种用于识别作物疾病的轻量级模型. 它实现了高精度和降低的参数数量,使其成为在复杂的现场条件下移动部署的理想选择.

科学领域:

计算机视觉计算机视觉
农业技术农业技术
机器学习机器学习

背景情况:

准确的作物疾病识别对于保持作物产量至关重要.
现有的方法往往受到大型模型尺寸和在复杂的现场环境中低精度的影响.

研究的目的:

开发一种轻量级且准确的作物叶病识别模型.
解决现有模型的局限性,包括大参数数量和复杂背景中的性能差.

主要方法:

提出了一个新的轻量级模型,REM-ShuffleNetV2,基于改进的ShuffleNetV2架构.
集成的剩余结构,以更好地学习特征,以及一个高效的双通道注意力 (EDCA) 模块,以增强跨通道交互.
引入多个尺度的特征提取模块,以改善各种尺度的病变检测.

主要成果:

在田间作物叶病数据集上,REM-ShuffleNetV2实现了96.72%的准确性和96.62%的F1得分.
该模型具有4.40M的参数,比原来的ShuffleNetV2.2.5少9.65%.
在准确性和参数效率方面,超越了像DenseNet121,EfficientNet和MobileNetV3这样的既定模型.

结论:

在复杂的现场环境中,REM-ShuffleNetV2提供了一种轻量级且准确的解决方案,用于在复杂的现场环境中识别作物叶病.
该模型的小尺寸有助于在移动设备上部署智能作物疾病诊断.
为开发精密农业中高效的人工智能解决方案提供了宝贵的参考.

关键词:

这是一个EDCA模块.在ShuffleNetV2中使用.复杂的背景复杂的背景.农作物叶病是一种病.剩余结构的残留结构

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