复杂环境中的Camellia oleifera疾病的分类通过注意力和多维特征融合神经网络
Yixin Chen1,2, Xiyun Wang1,2, Zhibo Chen1,2
1School of Information Science and Technology, Beijing Forestry University, No. 35 Qinghuadong Road, Beijing 100083, China.
Plants (Basel, Switzerland)
|July 29, 2023
概括
一个新的注意力和多维特征融合神经网络 (AMDFNet) 改善了植物疾病的识别. 这种模型可以实现更高的准确性和更小的参数数量,适用于移动应用程序.
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:
- 鉴定植物病对农业至关重要,但面临诸如类内变异和环境噪音等挑战.
- 现有的神经网络模型与植物疾病的复杂特征作斗争,影响了分类准确性.
研究的目的:
- 提出一个注意力和多维特征融合神经网络 (AMDFNet) 以改善Camellia oleifera疾病分类.
- 通过在Inception结构中整合多维特征融合和注意力机制来提高分类性能.
主要方法:
- 开发了AMDFNet,通过融合Inception层中的功能,并应用注意力增强.
- 在一个定制的驼虫病数据集上,对比了AMDFNet与GoogLeNet,Inception V3,ResNet50,DenseNet121和DICNN.
主要成果:
- 据了解,AMDFNet的识别准确率为86.78%,超过了谷歌LeNet的2.3%.
- 与ResNet50.0相比,该模型的参数数量减少了四分之一.
- 拟议的方法表明了移动部署具有高精度和效率的潜力.
结论:
- AMDFNet有效地解决了植物疾病分类的挑战,包括特征变异和噪音.
- 该模型提供了高识别精度和减少计算资源之间的平衡.
- 这项研究为基于移动的植物疾病诊断提供了可行的解决方案.
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