基于深度学习的轻量级模型,用于有效识别植物疾病和害虫,基于深度学习
Hongliang Guan1, Chen Fu1, Guangyuan Zhang1
1School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China.
Frontiers in plant science
|July 31, 2023
概括
一个新的深度学习模型,Dise-Efficient,准确地识别植物疾病和害虫. 通过动态学习率和转移学习进行训练,它实现了高精度,为移动农业应用铺平了道路.
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:
- 植物病和害虫导致农业的重大损失.
- 深度学习模型,特别是卷积神经网络 (CNN),提供了准确的识别能力.
- 现有的方法可能缺乏效率或对现实世界应用的概括性.
研究的目的:
- 设计一种新的,高效的深度学习网络架构,用于植物疾病和害虫识别.
- 通过特定的培训策略来提高模型准确性和概括能力.
- 评估模型在基准数据集上的表现,并评估其在移动部署方面的潜力.
主要方法:
- 基于EfficientNetV2.2的Dise-Efficient网络架构的开发.
- 在培训期间实施动态学习速度衰减策略.
- 纳入转移学习以改善对不同数据集的概括性.
主要成果:
- 该Dise-Efficient型号实现了13.3 MB的紧尺寸.
- 在植物村数据集上使用动态学习速率衰减获得了99.80%的准确性.
- 在代表现实条件的IP102数据集上,该模型实现了64.40%的识别精度.
结论:
- 该Dise-Efficient模型在植物疾病和害虫识别方面表现出高准确度.
- 该模型的紧尺寸和性能表明它适合移动和嵌入式设备.
- 这项研究为开发自动植物疾病和害虫识别系统提供了宝贵的参考.
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