PalmNeXt:基于ConvNeXt的深度学习模型,用于在枣树叶中检测害虫
Mahmood Ashraf1, Muhammad Zeeshan Aslam2, Natasha Saeed2
1Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia.
Frontiers in plant science
|February 9, 2026
概括
这项研究引入了一种改进的ConvNeXt-Tiny模型,用于在枣树作物中自动检测害虫. 改进的框架显著提高了识别虫害的准确性和性能,如Bug和Dubas.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动化害虫检测对于有效的作物监测至关重要.
- 现有的方法通常需要手动检查或是计算密集型,限制了可扩展性.
- 挑战包括处理农业应用中常见的小,可变数据集.
研究的目的:
- 开发一个高精度,计算效率高的自动化害虫检测框架,用于枣树作物.
- 通过定制的预处理管道来提高ConvNeXt-Tiny模型的性能.
- 将拟议的模型与传统和最先进的基线进行比较.
主要方法:
- 开发了一个增强的ConvNeXt-Tiny框架,集成了一个专门的预处理管道.
- 该模型在RGB图像数据集上进行训练和评估,该数据集包括4个类别 (Bug,Dubas,Healthy,Honey) 的3000个枣树叶样本.
- 性能与CNN-Attention,ResNet13-Attention,ViT,ECA-Net和标准的ConvNeXt-Tiny.对比进行了对比.
主要成果:
- 预处理增强的ConvNeXt-Tiny模型在所有评估的指标上实现了卓越的性能:准确性,精度,回忆和F1-score.
- 拟议的框架显著优于定制和现有最先进的模型.
- 解决方案的轻量级性质证明了可扩展的害虫检测的有效性.
结论:
- 增强的ConvNeXt-Tiny框架提供了一个可扩展和准确的解决方案,用于精密农业中自动检测害虫.
- 量身定制的预处理是改善特征质量和模型性能的一个关键因素.
- 这种方法解决了现有方法的局限性,使作物监测更有效.
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