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在复杂的背景下使用改进的MobileNetV3-小模型进行葡萄叶培养物识别

Liuyun Deng¹, Zhiguo Du¹, Xiaoyong Liu²

¹College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.

Plants (Basel, Switzerland)

|December 11, 2025

概括

这项研究介绍了ICS-MobileNetV3-Small (ICS-MS),一种新的轻型卷积神经网络,用于准确识别葡萄叶品种. 该模型以较少的参数实现了高精度,使其在精密葡萄种植中实现了实际的移动部署.

科学领域:

计算机视觉计算机视觉
机器学习机器学习
农业科学农业科学

背景情况:

准确的葡萄叶识别对于葡萄种植管理,育种和精准农业至关重要.
挑战包括微妙的形态变化,背景噪音和移动应用程序中计算效率的需求.
现有的方法在实际的现场部署中难以平衡准确性和效率.

研究的目的:

开发一个改进的轻型卷积神经网络 (CNN),用于准确的葡萄叶品种识别.
增强特征提取,多尺度融合和特征分布优化,以提高分类稳定性.
为基于移动的精密葡萄种植提供计算效率高的解决方案.

主要方法:

提出了一个改进的轻量级CNN,ICS-MobileNetV3-小 (ICS-MS).
集成了一个协调注意力机制,用于增强空间特征捕获和噪音抑制.
整合了一个多分支ICS-Inception结构,用于多规模的特征融合.
利用关节损失函数来优化特征空间分布和分类稳定性.

主要成果:

在11个葡萄品种的葡萄叶数据集上,只用117万个参数实现了96.53%的识别准确度.
与基线MobileNetV3-Small.相比,ICS-MS模型显示了显著的改进.
精度,回忆和F1得分始终接近96%,这表明了有利的效率-精度权衡.

结论:

ICS-MS为葡萄叶识别提供了一种实用和可靠的方法,解决了特征提取和计算效率方面的关键挑战.
该模型的轻量级设计和高精度使其适合于精密葡萄种植的移动部署.
这项工作为支持葡萄种植的智能管理和决策提供了有价值的工具.

关键词:

协调注意力机制协调注意力机制葡萄叶品种的识别方法轻量级的深度学习精密葡萄种植的葡萄种植.

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