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一个轻量级的卷积神经网络用于茶叶疾病和害虫识别

Xiaojie Wen^1,2, Qi Liu^1,2, Xuanyuan Tang^1,2

¹Key Laboratory of Pest Monitoring and Safety Control of Crops and Forests of the Xinjiang Uygur Autonomous Region, College of Agronomy, Xinjiang Agricultural University, Urumqi, 830052, China.

|October 15, 2025

概括

这项研究引入了一个新的数据集和一个增强的MnasNet模型,用于检测茶叶疾病和害虫. 该模型实现了高精度,使得用于精密农业的自动检测成为可能.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物病理学植物病理学

背景情况:

茶叶生产对于中国的绿色经济至关重要,但疾病和害虫威胁到农业生产率.
目前用于茶叶植物健康的诊断方法存在数据有限和特征表现不佳的问题.
准确及时识别茶叶植物疾病和害虫对于可持续农业至关重要.

研究的目的:

开发和验证茶叶病和害虫检测的自动化系统.
解决数据稀缺问题,提高分类模型中的特征可区分性.
通过智能茶叶种植园管理,加强精准农业.

主要方法:

创建了一个新的茶叶疾病和害虫数据集 (TDPD),包含23个类别的分类学.
通过使用各种优化器和学习率策略,评估了五个轻量级卷积神经网络 (LCNNs).
开发并测试了结合SimAM注意力机制的增强型MnasNet模型.

主要成果:

增强的MnasNet模型在专有的TDPD数据集上实现了98.03%的准确性.
该模型在开放式数据集上显示了84.58%的准确性.
通过SimAM的注意力机制,功能可辨别性和分类准确性得到了改善.

结论:

开发的模型为自动茶叶疾病和害虫检测提供了强大的解决方案.
这项研究为将智能系统集成到茶叶种植园管理中提供了实际框架.
这些发现支持无人机装载成像和移动平台用于实时诊断的应用.

关键词:

注意力机制注意力机制疾病和害虫疾病和害虫轻量级卷积神经网络 (LCNNs) 是一种轻量级卷积神经网络.在MnasNet中,您可以使用MnasNet.这就是SIMAMAM.茶叶茶叶茶叶茶叶

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