CPD-CCNN:使用卷积神经网络模型连接的胡病的分类
Yohannes Agegnehu Bezabh1, Ayodeji Olalekan Salau2,3, Biniyam Mulugeta Abuhayi1
1Department of Information Technology, University of Gondar, Gondar, Ethiopia.
Scientific reports
|September 21, 2023
概括
这项研究引入了一个新的深度学习模型来分类胡植物疾病. 连接的VGG16和AlexNet模型实现了高精度,为农民提供了可靠的解决方案来识别作物问题.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 植物病理学 植物病理学
背景情况:
- 胡作物对于粮食安全和发展中经济体至关重要,但疾病显著影响产量.
- 传统的疾病检测方法不准确,耗时,需要先进的解决方案.
- 现有的图像处理和深度学习模型用于胡病分类需要进一步改进,特别是对于复杂的病例.
研究的目的:
- 开发一个准确和高效的胡病分类模型,使用一种新的深度学习方法.
- 为了解决以前对胡病的二元分类方法的局限性.
- 改进从数字图像中识别各种胡叶和水果疾病.
主要方法:
- 提出了一个连接的卷积神经网络 (CNN) 模型,将VGG16和AlexNet的特征结合起来.
- 模型开发涉及数据集收集,图像预处理,消除噪音,细分和特征提取.
- 完全连接的层被用于最后的分类阶段.
主要成果:
- 拟议的连接CNN模型在分类胡病方面表现出很高的性能.
- 培训准确率达到100%,验证准确率达到97.29%,测试准确率达到95.82%.
- 该模型有效地从数字图像中识别了叶子和水果疾病.
结论:
- 开发的连接CNN模型是一个强大而准确的工具,用于识别胡植物疾病.
- 这种方法比传统方法和以前的分类模型有显著的改进.
- 该研究强调了深度学习在提高农业可持续性和作物管理方面的潜力.
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