一个高效的卷积神经网络为类水果疾病的诊断系统.
Zhangcai Huang1, Xiaoxiao Jiang1, Shaodong Huang1
1Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.
Frontiers in genetics
|September 11, 2023
概括
这项研究引入了一种深度学习模型,用于识别类水果疾病及其严重程度. 这种新的方法在疾病识别方面达到95%以上的准确性,有助于农业生产.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 水果病对农业经济回报有重大影响.
- 深度学习模型越来越多地用于作物疾病识别和严重性诊断.
- 高度特征提取对于提高农业分类性能至关重要.
研究的目的:
- 利用深层卷积神经网络来加强果疾病的识别和严重性诊断.
- 通过使用新型深度学习架构来提高多级特征提取和分类准确性.
- 开发一种有效的方法来诊断类水果疾病的严重程度.
主要方法:
- 一个混合深度学习模型,结合Inception模块和EfficientNetV2进行特征提取.
- 使用VGG取代U-Net骨干,以提高细分性能.
- 应用转移学习来优化疾病检测和严重程度诊断的网络培训.
主要成果:
- 拟议的方法在识别果疾病方面达到95%以上的准确性.
- VGG-U-Net模型以87.66%的准确度显示出卓越的细分性能.
- 综合方法在疾病识别和严重程度诊断方面都证明有效.
结论:
- 开发的深度学习方法对于识别和诊断类水果疾病及其严重程度非常有效.
- EfficientNetV2和Inception模块的组合可以增强多个尺度的特征提取.
- VGG-U-Net在农业应用中准确细分疾病方面显示出有希望的结果.
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