GACN:为平衡植物疾病数据集和植物疾病识别提供生成对抗分类网络
Xiaotian Wang1,2, Weiqun Cao1,2
1School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了生成对抗分类网络 (GACN),以提高植物疾病分类的准确性. GACN通过生成合成图像来增强卷积神经网络,优于现有的方法.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的植物疾病分类对农业至关重要.
- 生成对抗网络 (GAN) 在增强基于图像的识别任务方面表现有前途.
- 现有的基于GAN的方法在改善植物疾病识别方面存在局限性.
研究的目的:
- 为改进植物疾病识别提出一个新的生成对抗分类网络 (GACN).
- 提高卷积神经网络 (CNN) 在植物疾病分类中的性能.
- 评估GACN与其他基于GAN的方法的有效性.
主要方法:
- 开发了一个GACN模型,集成了一个生成器,区分器和分类器.
- 利用GACN生成的合成图像来平衡植物疾病数据集.
- 直接将GACN分类器应用于植物疾病识别任务.
主要成果:
- 与CGAN,ACGAN,BAGAN和MFC-GAN相比,GACN在CNN的歧视性方面表现出了卓越的改善.
- 在植物疾病识别方面,GACN分类器的准确性高于公开数据集上的现有模型.
- 实验分析证实了合成图像数量和分辨率对CNN可辨率的影响.
结论:
- 拟议的GACN有效地提高了植物疾病分类的准确性.
- GACN为不平衡的植物疾病数据集和直接分类提供了强大的解决方案.
- 这些发现表明,GACN是农业疾病检测技术的有希望的进步.
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